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What is adaptive learning in education? Adaptive learning is defined as an educational method where students get a personalized learning experience. Teachers use this approach to change the curriculum according to learners' needs. As a result, it helps students learn faster, easier, and more efficiently. Imagine that you are in a classroom, struggling to understand a difficult topic. Your instructor will provide different hints based on your actions. For instance, they could shift the lesson's course, provide real-life examples, or even come up with another activity. The teacher changes the class to help the student. It's a prominent example of adaptive learning in the classroom. However, the approach is only efficient when there are a few learners. Providing an individualized learning path for a big group at once is nearly impossible for one instructor. That's where AI for education makes an entrance. If we speak of AI-powered e-learning software development, the number of students may be limitless. AI algorithms analyze the way a learner studies. Based on the information they get, the algorithms start showing customized content. For example, if you make a mistake in the same task more than once, the program will provide similar assignments, explanations, and related materials. So instead of moving to a new topic, you'll have to correct your mistakes. This will help you understand the subject better. Teachers already apply the technology by implementing AI in K-12 education. Who can learn with adaptive learning technologies? The main feature of adaptive learning is that it makes the learning process suitable for everyone. It doesn't matter whether the learner is a K-12 pupil, university student, corporate employee, or anyone else. The technology adapts the material according to your knowledge. AI-powered adaptive learning can be applied in any learning area like: Languages; Economy; Science, and others. The field of study doesn't matter. Artificial intelligence and machine learning can amplify the results of any educational process. That's the benefit of EdTech! 5 ways how AI-powered adaptive learning solves educational challenges Artificial intelligence implemented in LXP, LMS, TMS, or other eLearning applications solves multiple educational challenges, for example, through custom LMS development. You will find a detailed explanation below. 1. Saves training resources to minimize the budget. People learn faster when they are mentored. In fact, 97% of mentees find this approach undoubtedly valuable. However, you can't hire one for each employee in a big corporation. That would lead to huge expenses. Adaptive learning maximizes your training resources. By analyzing all available data, the software provides relevant materials and tasks for each employee. The technology literally adapts the program to their needs. You could think of it as a personal digital mentor. 2. Enhances knowledge retention to improve learners' long-lasting skills. If you don't apply your knowledge for a long time, it constantly moves to a passive state. The same goes for your employees. They are unlikely to remember all the training and courses you've made them complete. Adaptive learning ensures your team is ready to handle any issue whenever it appears. The technology constantly analyzes what the learners remember, detecting knowledge gaps and providing appropriate recommendations. Thus, they are always ready to solve any problem. 3. Boosts learners' engagement to help create a continuous learning culture. A traditional course would force all employees to follow the same curriculum. Everyone has got a different experience, meaning that some people from your team might find more than half of the course as a waste of time. You can't make the whole company study the same material – it's just ineffective. AI-based adaptive learning helps each employee get engaged in the studying process. There aren't any monotonous tasks that can be copied from the Internet. Instead, each member completes a unique path to enhance their skills. Take a look, for example, at how a Learning Experience Platform works. The software will continuously assess the learner to detect their weaknesses and, consequently, remove knowledge gaps. This will keep your team focused on important activities. As a result, employees increase their qualifications, and companies get more competent specialists. 4. Increases time efficiency to reduce training time. E-learning takes 40-60% less time than the traditional classroom. By adding adaptive learning technologies, your employees start to use the time for education more efficiently. The course content is being adapted to the learner, skipping the material you already know. 5. Performs deep analytics to track training efficiency and learners' performance. Training managers can get metrics that show each employee's progress. It's an excellent addition to track their success, understand strengths and weaknesses, and also provide a better learning experience. By the way, AI in talent management is another common use case! It's time to develop your eLearning software! Let's discuss your project now! What is an example of AI-powered adaptive learning in education? Many applications apply adaptive learning in education. Duolingo Duolingo combines machine learning and adaptive learning in artificial intelligence to provide a better experience for all users. This app is focused on language learning through gamification and adaptivity. The software tracks various information like: The number of times you've seen a word; Your mistakes in specific topics; The topics and phrases you know best. It uses AI to predict the probability a learner would recall a word in the given context. The technology also calculates how much the student should practice to memorize the material as much as possible. So that you'd understand the speed at which Duolingo “learns” from your interactions, here's an example. When you open the app for the first time, you'll be asked to complete a placement test. All questions are based on your previous answers. It takes less than five minutes for the system to determine your language level. If you've had four years of English at the university, you don't have to start from scratch. Show your language level and enjoy the tasks! Prodigy Math Another adaptive and gamified learning app is Prodigy Math. Again, students see the recommended content that is completely based on an adaptive algorithm. It is capable of detecting the learner's strengths and weaknesses to provide a better learning environment. As a result, students get the following: Content that is challenging enough to develop new skills and abilities; To-the-point tasks that help them apply previous knowledge. The best part is that learners don't even understand that their answers are being estimated because there is an absence of traditional assessment interfaсes. All assessment work is being done behind the scenes. Prodigy Math applies its adaptive algorithm to provide personalized tasks. It always works in the background, making calculations and predictions. Imagine that there is a student who attempts to master a new skill. They pass 10 questions, and each is correct. The algorithm assumes that material is acquired, so it provides new topics that are a bit more challenging. It is even possible to get content designed for a higher grade, thus continuing the learner's development. Now let us look at another student. They want to master the same skill, but there are several mistakes in their answers. This results in the algorithm assuming the learner is struggling with the topic. Thus, before the skill is mastered, the student must complete a different pathway with more questions and explanations. That's how AI helps people get an individual experience. Embibe Embibe combines AI and data science to help K-12 learners prepare for exams and fill in knowledge gaps. It's very similar to the uplisted two apps because it also uses diagnostic tests, analyzes the students current level, and helps them fix learning gaps. Artificial intelligence gives its users the following: Personalized guidance; Tasks and materials to improve current grades; Theory extracts in the form of hidden tips explain why a learner struggles with a specific topic. Thus, anyone can enhance their knowledge with this technology. It provides relevant explanations and assists learners on their path. Lexia PowerUp Lexia PowerUp is an adaptive blending learning application designed to help struggling learners increase their literacy skills from grades 6-12. The software uses placement tests to determine which programs and assignments the student should complete. For example, if the child struggles with a task too long, the app will provide hints and solutions. They are available both in text and audio format. Adaptive learning technologies help students: Determine their current knowledge level; Get a personalized learning pathway; Get adaptive instructions throughout the process. Lexia PowerUp combines adaptive technologies with fun ways to engage learners. Apart from personalized instructions, they also get humorous videos, elements of gamification, and self-monitoring tools. If you pay close attention, you'll see that the only differences in each app are the number of features, target audiences, and learning areas. The best platforms always attempt to apply gamification in the learning process because it increases engagement, development and boosts memorization. Interested in creating an AI-based adaptive learning solution? Contact us to learn more! How does adaptive learning AI work? The adaptive learning technology in education works based on the following three steps: Assessing the learner's knowledge. The system gathers data from the whole course or separate modules. Then, it calculates the student's mistakes, weaknesses, and strong points. Providing targeted content. After analysis, the software recommends the user to read different materials, complete assignments, or proceed to the next module. Everything depends on the results of the assessment. Interacting with feedback data at all times. An adaptive model constantly monitors all processes. So, for example, if a user struggles with a particular task, AI will provide different variations of the topic in the next assignments. One of the common use cases of AI in education is the integration of AI in an LMS to get better insights into the whole process. Not only does it provide personalized content for learners, but it also gives you an understanding of which topics need more focus. The system applies multiple algorithms to figure out how to meet the student's needs. What is adaptive machine learning? Adaptive machine learning is a technique that needs human assistance only when it matters most. It is capable of learning autonomously as long as new data is provided. Adaptive ML represents the 4th generation of machine learning. 1st generation: rule-based machine learning Rule-based ML works based on rules created manually by people. Let’s suppose that a software developer created a rule that “Ford” followed by “Focus” would mean a car, while preceded by “Harisson” would mean a person. It would help the software identify whether the text is about a car or a human. The key disadvantage of this approach is manual work that can't cover all existing rules for each word and phrase. The process is very time-consuming and lacks accuracy. Even if a specialist managed to add all the words and phrases they could think of, new contexts and meanings would still appear. The world is changing rapidly, so it is necessary to have a time-efficient method. 2nd generation: simple machine learning Simple ML works based on statistics. So, for example, if a sentence includes the word “Ford” that is already labeled as a car, it will automatically assume that “Focus” as the following word is enough contextual evidence. While the statistical approach makes this generation faster and more accurate, its efficiency depends on the number of labeled data for supervised learning. Thus, you need a lot of examples to make this ML generation work properly. 3rd generation: deep learning Deep learning is based on neural networks with multiple layers, almost like a human's brain. For example, the algorithm gets raw data, and it makes a decision of its own whether the info is relevant. The more data it acquires, the more efficient it becomes. It is extremely close to mimicking a human's mind. Deep learning creates patterns, detects objects, and makes decisions, all based on former experience. Also, the system may be taught through raw data that may remain unlabeled. 4th generation: adaptive learning Adaptive machine learning combines the best features of all ML generations and learns continuously. This algorithm may also use unsupervised machine learning, which makes it stand out even more. As we have already mentioned, adaptive ML will always be up-to-date because of its nature. The system isn't tied to one specific feature. It can adapt and survive in changing conditions. How are traditional ML and adaptive ML different? The first difference between these types of machine learning is that traditional ML has two channels. One is used for training, the other for predictions. Because of this separation, the process takes a lot of time. An adaptive system is single-channeled. It gathers data and learns at the same time, making the whole process much faster. That's why it is considered to be superior. The second difference is that traditional ML is based on a static environment. If we apply some changes to it, we might encounter some serious errors. Changing the system's behavior requires a lot of resources. Adaptive machine learning is based on its main feature – adaptability. It doesn't matter whether the environment is static or constantly changing. The system can change its behavior without involving losses and applying outdated info. The last point is that adaptive ML, unlike its traditional counterpart, can learn from its former data. The more data it acquires, the better it works. Additional knowledge helps the system be more accurate with its predictions and avoid mistakes. What are the types of machine learning algorithms? Machine learning applies different algorithms to get new data, process it, and make decisions. Supervised learning Supervised learning means machines are taught by examples. For instance, people provide a number of processed data with their desired inputs and results. Then, the algorithms search for patterns and similarities to make predictions. Human supervision is required to correct the machine when it makes a mistake. The whole process lasts until the machine gets a high level of accuracy. Semi-supervised learning Semi-supervised learning applies examples with both processed and raw data. The general idea is that the machine learns from the labeled information and applies the knowledge to label raw data. Unsupervised learning Unsupervised learning teaches the machine to find patterns without human intervention. No answers or hints are provided. The machine groups data into various categories based on its analysis. The more data is processed, the easier it is to make an accurate decision. Reinforcement learning Reinforcement learning provides the machine with rules and required results. The algorithm then analyzes all existing options to find the optimal result. It's a complex sequence of decisions that make the machine apply former experience. That's how adaptive machine learning works. What are adaptive learning AI tools? We may divide adaptive learning tools into two categories. Simple. This type applies a linear model to determine a learner's progress. For example, a correct answer will take you to Point 1, while a wrong answer will result in Point 2. It is the very foundation of personalization. The only drawback is that the user constantly follows a predefined route, which isn't complete adaptivity. Complex. This type uses different data to show appropriate content. For instance, it could track previous answers, a number of interactions, current situations, and many complex conditions. The core is AI's inference engine that applies logical rules to make assumptions. Adobe Captivate is an example of an adaptive learning tool that may be placed in the “simple” category. It helps instructors to create branched scenarios. While this is the basic version of course adaptation, a complex solution does this task far better. However, a complex adaptive learning tool needs a skilled development team. The future of adaptive learning in education It is expected that the global corporate e-learning market will cost $50 billion by 2026. That's not a surprise, with more than 90% of corporations using these technologies to teach their employees. Increasing one's qualification is even possible via a smartphone. Combine this with adaptive learning, and it will be no wonder people become more engaged in corporate training. The general e-learning market is projected to reach $243 billion by 2022. This is because forced distance education boosted the whole industry, driving the implementation of new technologies. And, of course, adaptive learning is among the leading positions. We can see AI in higher education, K-12 classes, corporate training, online courses, and any other field related to studying. It is suitable for all areas and ages. A report from 2017 stated that AI and machine learning in the US educational system would grow 47,5% by 2021, and we can already reap the benefits. Everyone strives toward personalization and real-time feedback. Most teachers also see it as an optimal solution to improve education. So it's the perfect time to invest in AI-based EdTech! Do you want to get AI-based adaptive learning technologies in your software? How about creating an e-learning platform from scratch? We'll help you make the right choice. Let's talk!

The essence of a skills management system is not merely in its tracking capabilities—it's in how it discerns, assesses, and fosters employee skills, connects them with enriching training resources, and aligns talent with the most fitting projects and roles. "To improve career management, talent mobility, or recruiting effectiveness and we need to know what skills people need in each job." Josh Bersin Set of Criteria for Identifying Top Skills Management Software Skills Inference The system utilizes a machine learning (ML) model to identify, categorize, and assess skills. Data is gathered from job descriptions, courses, projects, reviews, career histories, and even the content users engage with. Once identified, the skill set is sent for the employee's approval. Skills Verification While the system automatically infers skills from undertaken projects or tasks, users can adjust these inferred skills. The skills endorsed by an employee are then confirmed in a work context by peers and managers. Such endorsements are instrumental in pinpointing genuine experts in certain fields. Skills Strength Monitoring If an employee hasn't used a specific skill recently, the system can detect a decrease in its strength. The system then suggests that the skill needs to be refreshed or enhanced. The duration after which a skill needs reinforcement can be customized as per requirements. Upskilling and Reskilling When the need to refresh a skill arises, the system recommends relevant training content. Typically, integrating the skills management system with a Learning Management System (LMS) or a Learning Experience Platform (LXP) addresses this need. Such integration promotes a continuous learning culture within the company and automates the entire learning cycle. Job and Skills Matching Through the use of APIs, skills management software can be integrated into any HR software. This provides HR managers with a centralized skills database, enabling them to align the right individuals with the most suitable roles. This alignment is rooted not merely in formal criteria like job titles, CVs, or interviews, but is based on authentic and validated skills. Top 3 Ready-to-Use Skills Management Software Cornerstone Founded in 1999, US-based Cornerstone OnDemand has evolved from focusing on online education to becoming a global frontrunner in human capital management. Their suite now emphasizes contemporary skills management, overseeing the management, development, and utilization of workforce abilities. Central to their approach is SkillsTech, an AI tool that discerns individual skills, refining its accuracy over time. Cornerstone merges data from its skills management framework with diverse content libraries. As users engage in projects, the platform efficiently assembles teams, considering both skills and learning experiences, which enhances HR functions. With a nod to integration's significance, Cornerstone boasts robust APIs and connectors, ensuring smooth collaboration with major HR tech entities. Their skills software also ties into their Content Studio and Learning Experience Platform. Cornerstone dashboard with skill levels for an employee ServiceNow Established in 2003 in Santa Clara, California, ServiceNow has diversified eLearning solutions. Their recent standout is a skills management application aimed at streamlining skill identification, management, and deployment, bolstering efficiency and productivity. With ServiceNow, companies can delineate, sort, and assign skills. Distinct roles like Skill Administrator, Manager, and User are defined, each having unique skill management permissions. Comprehensive dashboards and reports offer a clear view of skill deficits, aiding employee training planning. This software meshes flawlessly with other ServiceNow offerings, ensuring optimal skill application throughout the enterprise. ServiceNow dashboard with a skill matrix Workday Founded in California in 2005, Workday is a premier enterprise cloud application for finance and HR. Their Workday Skills Cloud offers a holistic view of an organization's skill set. The system discerns and organizes skills by evaluating job profiles, feedback, and more, facilitating an understanding of talent dynamics. Additionally, it supports peer validation, letting managers endorse specific skills. The tool recalibrates based on recent skill engagement and integrates seamlessly with other Workday modules to boost functionalities like hiring and training. The platform quickly adjusts talent, such as finding replacements for employees who leave or are absent, and prioritizes the organization's collective capabilities over individual skill. It continuously updates required skills by analyzing job descriptions, feedback, and customer data. Workday's skill-centric job matching dashboard Custom Skills Management Software vs Ready-Made While the market is flush with ready-to-go skills management solutions, using them may present challenges. Whether you've experienced these issues or are selecting your first SMS and wish to sidestep pitfalls, a custom SMS can be advantageous. It offers a tailored experience, ensures precise skill tracking, and enhances your return on investment. Both an off-the-shelf skills management platform or a custom-made one have their distinct merits. Benefits of Ready-Made Skills Management Systems Quick and cost-effective deployment. These systems are pre-developed, allowing immediate implementation. Vendor-backed support. Bypass the need for a dedicated tech team, as system maintenance is vendor-handled. User community. Popular platforms foster large user communities, enabling knowledge sharing and maximizing system utility. Common Issues of Ready-Made Skills Management Systems While the skills management arena is rife with AI-driven vendors like Workday, Service Now, Eightfold, and Cornerstone, closer scrutiny shows a disjointed picture: Narrow scope. Each system is tailored for distinct uses. For example, while Eightfold zeroes in on discerning skills in job descriptions, Cornerstone emphasizes highlighting skills in learning catalogs. But a complete solution that covers skills verification is still missing. Absence of a unified skills database. The quest for a one-stop solution for all skills management persists unless developed in-house. Functionality constraints. Leading players like Oracle and SAP are still refining their skills management offerings. Their tools often function more as skill data coordinators, using APIs for integration with specialized systems, rather than as an all-in-all solution for all corporate needs. Top 4 Reasons to Get a Custom Skills Management System 1. Perfect fit due to personalized customization Ready-made systems have challenges, so a universal solution may not work for all organizations. Instead, a custom skills management system, tailored to your company's jobs structure, will reflect the skills uniquely relevant to your industry, rather than just general ones. 2. Complete control and enhanced security With custom software, organizations have full ownership and discretion over its functionality, design, and branding. Furthermore, you can fortify the security to meet your standards, ensuring data protection without third-party interventions. 3. Savings in the long term While most Skills Management Software operates on a SaaS pricing model, causing regular subscription fees, a custom-built solution side steps these ongoing costs. After recouping the initial investment, your custom software evolves into a valuable, cost-effective asset for your organization. 4. Opportunity for product commercialization Analysts, including the renowned Josh Bersin, underscore the growing emphasis on skill-centric organizations. This trend suggests an expanding skills management market in the coming years. Early entrants stand to benefit, presenting lucrative commercial prospects for custom solutions. How Belitsoft Can Help Belitsoft offers quick and cost-effective development of your own, custom skills management software that would seamlessly align with your specific requirements. For that, we have built a robust framework with all essential modules. If certain modules are not relevant to your needs, they can be effortlessly deactivated. This adaptability ensures that you receive a tailored solution optimized for your organization's unique demands. Skills Inference into a Centralized Skills Database To timely detect employees' skills gaps, assign the right people to the right roles based on their skills, and optimize your workforce allocation, all the skills required in your organization should be structured and accessible through a centralized inventory. The skills management platform provides a hierarchical structure of skills that serves as an inventory where managers can create, view, and manage skills. The skills can be assigned as skill tags to match job roles and training courses, providing versatile talent management tools for skill allocation and targeted training. An example of a skills tree for a sales representative where skill tags are created by mentors or heads of departments and then assigned to employees and training courses Skills Verification through Employees Assessment Once the skills library, or skills matrix, is created, you can use it as a unified tool for identifying the competencies of each employee within the organization. For that, the skills management platform offers various assessment approaches. The employees can be manually assessed by mentors or managers, or the system provides quizzes and tests for automated knowledge testing. Upon assessment, each employee gets a list of mastered skills that will later be used for the employee's job role assignment, closing their skills gaps, and building skill-based teams for projects. An admin dashboard where a head of the department assigns a quiz to an employee for assessing certain skills that the current employee's job role of sales representative requires Tracking Employee Skills Having a full picture of each employee's current competencies facilitates identifying skills gaps that may impede the company's growth, future competitiveness, and successful project completion. In order to effectively identify skill gaps within an organization, the platform provides a comprehensive dashboard that consolidates all the skills and training data of each employee in an easy-to-navigate view. It not only highlights the specific skills assigned to each employee but also pinpoints areas of skill gaps. By tracking employees' skills and training data, the best skills management software can provide a clear focus for their work and development. Furthermore, it offers associated learning resources to help close skill gaps. An example of an employee dashboard with information about mastered skills, current learning paths, and experience level. Based on the revealed skill gap (58% out of 100% expertise) we see a CRM course assigned by the head of the department for closing the skills gap Upskilling and Reskilling Employees with AI Authoring Tool To effectively tackle the identified skill gaps, it's essential to develop a learning path with the relevant content. For that, our new skills management system has advanced AI authoring tools that automate course creation. An admin just enters a prompt and gets a ready-to-use training course with articles, images, videos, and other content formats of their choice for developing skills. The more specific prompt is entered, the more diverse, rich in content, and tailored-made content you get. An example of a course outline generated by Artificial Intelligence integrated into talent management software, with the possibility to customize each content chunk manually Matching Employees with Job Roles and Projects The grading matrix serves as a powerful tool for aligning employees with specific job roles and projects based on their skills. It visually maps out your team's strengths and weaknesses, providing valuable insights that can guide strategic decision-making. Managers can utilize this skill management tool to filter their teams by specific skills to match employees to roles and projects where their skills are most needed. This not only optimizes team performance, but also fosters individual skill development. In essence, the grading matrix is a strategic resource for effectively managing talent and driving team growth. An example of a grading interface where business stakeholders get a full picture of the employees' skills throughout the organization to plan business growth, while managers can effectively gather teams and assign roles based on the mastered skills REQUEST DEMO

Cloud Document Management Benefits Everybody knows the benefits that a digital document management system offers compared to traditional paper storage in file cabinets. Going digital lowers office costs. Suddenly, your documents want less office space, furniture, and paper usage. And your workers start to spend less time accessing documents. Physical handling from employee to employee no longer makes sense for this reason. Since it’s widely known, let's stay focused on the advantages of moving such important assets as documents to the cloud. Security If the U.S. Department of Defense selected the cloud, specifically Amazon, there was a reason. The main one is to offload security from the internal IT teams in the face of the growing number and complexity of cyber threats. By default, up-to-date security means automatic control that assigns and revokes appropriate document access, encryption to protect stored and shared data, anti-disaster digital storage, backup, recovery and restore, and, of course, software updates. It's worth focusing on how cloud content is backed up. It does this after each edit and in multiple data centers. Business continuity is supported, and data and services aren’t down regardless of any reason. Remote access At first sight, remote access does not appear to be something completely new. Maybe your workers already connect to a corporate network using a VPN. The key here is the VPN. Your IT team has to set this up for each new team member and help with management and solving issues (there may be a lot of them). The major drawback of VPN is dramatically slowing down and lengthening an employee's journey. Traffic is slowed by taking longer routes. Transferring large files comes with significant long loading times. Encrypting/decrypting further slows down the connection. Direct access to cloud resources eliminates the extra twists that VPNs introduce. Cloud-based document management systems are accessible on any device, regardless of the user's location. They just need an internet connection to log in through a web browser. Budget Optimization No capital expenditures. Monthly subscription costs for cloud services are operational expenses. IT staff is not necessary to manage servers, disk space, or to buy new computers. The number of servers, processor speed, and the amount of storage automatically increase with the growth of files and traffic. For heavily regulated finance or healthcare industries, compliant cloud solutions are always at hand. Extensive tech resources at your side are no longer required. Features of a Document Management System Some features are already set up in the described system. Others can be customized on request, which is fairly easy to do since the system is deployed on AWS. AWS provides many options for integrating ready-made solutions, which makes customization simpler. Convert Scanned Documents to Texts and Process them With AI Optical Character Recognition technology (OCR), implemented in modern document management systems, automatically, quickly, and accurately extracts large amounts of text from scanned documents, images, or PDFs. After such preprocessing, users can quickly find and edit information from scanned documents. OCR is not a new thing in document management. It has established itself as a tool to process banking documents (checks, loan applications, and account statements), automate extraction of information from invoices (vendor details, amounts, and dates), and retail documents (labels, receipts), digitize healthcare documents (patient records, prescriptions), and assist in the analysis of legal documents (contracts and case files). But AI makes a difference. It not just improves text recognition accuracy due to machine learning but is also able to deal with it like a human acts in a business process (know what to do with it) thanks to natural language processing technology (NLP). It may read content and compare it with other documents to check accuracy (data validation) and forward it to the head of the department for further actions (decision-making). For example, it’s reported that AI-based invoice processing times can be reduced by 90 percent, equating to a 400 percent increase in employee productivity, making turnaround time for invoices from days to minutes. Each industry has specific forms to process, and this can also be automated with AI-enabled recognition. We talk about insurance claims forms, logistics driver logs or delivery receipts, banking credit card applications or loan and mortgage forms. When needed to verify addresses at scale, AI can analyze driving licenses, passports, bank statements, and utility bills in bulk. The modern low-code/no-code document management solution can be easily integrated with AI-based OCR software with pre-trained, ready-to-go models or custom extraction models based on specific business requirements. Document Classification ML, computer vision, and NLP are technologies to categorize documents. They automatically add predefined categories, or tags to documents. Computer vision is the fastest method. It can understand the type of document without reading its text just by seeing the visual structure during the scanning phase. As to text-based classification, documents can be segmented based on the complete document, specific paragraphs, particular sentences, or even phrases. In general, business case influences how to segregate documents. Document classes might be user-defined, and document sorting is possible by type, level of confidentiality, vendor, or project containing a set of documents, and more. AI can understand various types of documents in each industry: legal documents, notarial deeds, and contracts for law firms; medical records, patient files, and clinical research documents for healthcare organizations; financial statements, loan applications, or insurance claims for banks, insurance companies, debt collection agencies, and other financial institutions. AI-based document classification can work with structured documents (tax return forms and mortgage applications), semi-structured documents (invoices), and unstructured documents (contracts). Machine learning models are able to understand whether the uploaded document is complete and flag missing or incomplete inputs and pages, and mark any documents with errors. It identifies fraudulent documents through anomalies, helping to reduce document fraud. After the classification process is finished, documents can be automatically routed to the appropriate department and respective team members. Document Summarization AI creates text summaries of lengthy documents, scanned files, and images for those who are short on time. It can even transcribe video and audio files so one can search within that content. It’s not about shrinking text but rather extracting the main points. For example, it can highlight key points like pricing and terms from a lengthy contract. AI rephrases complex sentences and technical jargon using plain language. It can explain specific clauses so simply that a reader can understand the legal implications much faster and more precisely. Interactive Q&A To Replace Traditional Search Traditional document repositories are text-searchable by keywords. But this may overwhelm. You have to comb through too many search results to find something valuable. Disappointment is usual. Can't you often find relevant records even after spending a lot of time? AI-powered search makes a difference. No more manual searching through each document to quickly find specific information within some of them. Now, searching is like an interview. The chat understands the context, asks you questions, and narrows down the results. It works best with complex requests like "What is the total amount spent with Company X last month?" or "List documents from 2023 that mention topic X." Responses are accurate because they are based on your document's content. Integration Integrated document management systems allow employees to view, edit, and save documents directly in their daily-use software. Users from different industries like retail, banking, finance, insurance, or manufacturing can work with their documents without switching applications.  For example, when a bank employee is processing a client's account in a core banking system, they can also access account opening forms, loan, or mortgage agreements stored externally. They can edit such documents right there, and their line-of-business app will automatically save the modified document back to the document management tool. A document management system, integrated with other vital systems in your organization, helps your employees avoid unnecessary challenges. Document status visibility A document management system provides an interface that helps to understand the status of each document in the workflow, like whether it’s in draft, in progress, awaiting review, under review, feedback provided, revised, awaiting approval, approved, rejected, finalized, published, archived, completed, or expired. Document versioning Since documents can be shared with several teams, departments, or even external stakeholders, the system includes a tracking feature. The log may save all changes made to a document, including who made them and when.  Nested Folders  The nesting (organization of items within a hierarchy) helps us establish relations between things. Using nesting, end-users can define the order they like for grouping related documents.  Document Security The owner can analyze who accessed each document, what changes were made, who uploaded or downloaded it, and what comments were left. They can also restore a document to its earlier versions if something goes wrong. Admins may apply access rights (view, edit, or comment) to different types of folders, subfolders, or individual documents, which is a best practice for security. An enterprise-level document management system provides powerful tools for sharing and collaborating on files within your company. Multiple team members can work on the same document simultaneously, making changes and leaving comments for each other. A good document management system lets you create rules for how your employees can share documents. Automated alerts and actions on certain conditions This feature allows a document management system to keep an eye on your documents and notifies you when something needs your attention or when a specific action needs to be taken.  For example, if a contract is about to expire, the system can send you a reminder email so you don't forget to renew it. If a document containing sensitive data is accessed by an unauthorized user, the system can immediately alert the security team.  The system can also be set up to automatically perform certain actions based on predefined conditions or schedules. For instance, it can automatically archive old documents or delete after a certain retention period as mandated by legal regulations or company policies like freeing up storage space.  Metadata extraction Metadata is extra information about a document that helps describe what's inside the document without you having to open it and read through the whole thing.  It may be the date when the document was added and the user identity of who uploaded or edited the document.  For example, claim documents with digital photographs may contain the date the photograph was taken and even geolocation. This feature makes it simple for the user to access what they seek and allows documents to be found easily.  Metadata is automatically extracted and stored for each document. The system may also offer the user to manually add metadata. 

At Belitsoft, we leverage our extensive experience in eLearning software development, AI chatbot development, LXP consulting and development, skills management software development, talent management software development, custom LMS development, and custom software development for startups. Aleks EdTech AI for math and chemistry by McGraw Hill Funding: disclosed Country: USA AI application: knowledge check, personalized path ALEKS (Assessment and Learning in Knowledge Spaces) is a web-based AI assessment and learning program for online math and chemistry (from K-12 to Higher Education). ALEKS starts with an AI-enabled initial knowledge check to determine a student's understanding of a subject and then suggests individually tailored learning paths. ALEKS is trained to identify the exact topics each student is ready to learn and does not suggest anything that is too easy and boring or too hard and frustrating for them. Santa EdTech AI for TOEIC preparation by Riid Funding: $248M Country: South Korea AI application: AI tutor The app helps prepare for TOEIC English test (TOEIC RC/LC practice questions, TOEIC video lectures, TOEIC vocabulary, and TOEIC sample vocabularies) and quickly achieve the desired score. It has 4 million downloads on the App Store and Google Play Store. Santa's level test predicts a student’s TOEIC score in three minutes, analyzes weaknesses in each part (listening, vocabulary, grammar, etc.), and recommends a personalized study course with only the necessary lectures and questions. Amira Learning SaaS Edtech Funding: $40.7M Country: USA Technology: Natural Language Processing Amira Learning represents an intelligent reading assistant that accelerates reading mastery by listening, assessing, and tutoring. It can also screen a student for the risk of dyslexia. Knowre Funding: $15.1M Country: South Korea AI application: tutoring, scaffolded support Knowres revolutionary technology identifies individual student learning gaps and fills those gaps through algorithmically generated curricula and interactive, scaffolded support. Teachers get actionable and comprehensive data to support struggling students. Founded in South Korea, it's expanded over the world. Knowre now partners with schools, private education companies, and other corporations. Back in 2021, Knowre was awarded the grand prize at Global K-Startup, a competition sponsored by Google, the Korean Internet and Security Agency, and the Korea Communications Commission. Also, it won first place in the GapApp Challenge sponsored by the New York City Department of Education. CenturyTech Funding: $12.6M Country: UK AI application: AI-based learning CENTURY is an AI-powered platform for schools, colleges, and universities. It uses learning science, AI, and neuroscience for developing customized teaching and learning plans. CENTURY helps teachers save time on marking and data analysis and make timely interventions to assist students. Century is an AI Award 2020 Winner by Reimagine Education, TechCrunch Award winner in 2016, and Connected Society Award finalist in 2019. Elsa Funding: $10.3M Country: China AI application: speech recognition ELSA (English Language Speech Assistant) is a mobile application that uses speech recognition to train pronunciation and reduce accent in short fun dialogues. The program provides instant and precise feedback on every sound. ELSA is available both for your businesses and schools. The learners' progress can be tracked in real-time from the teacher's or organization's dashboard. With over 7mln of users, ELSA keeps on expanding to new markets. The company has a Fast Company's 2020 World-Changing Ideas Award and is included in a prestigious CB Insights AI 100 list. Querium Funding: $6.2m Country: USA AI application: AI content recommendation and analytics Querium has created customized STEM courses aimed at high-school and college students to master their skills and enable them to achieve their career goals. The AI software analyzes students' answers and the duration of time required to complete a lesson. These data then give profound analytics to teachers and insight into students' performance. Querium has helped 24 million students and was recognized as the Most Promising IT and Web Company by the Rice Alliance for Technology and Entrepreneurship in Houston. Thinkster Math Funding: $4.3m Country: USA AI application: AI tutor Thinkster Math is a math tutoring application. AI math tutor integrates one-on-one teaching and math curriculum and guides students in developing and mastering their math skills. The program developers guarantee a 90% improvement in math within 3 months. It includes the initial knowledge check, custom-curated math worksheets, detailed and immediate feedback, and profound reports to teachers. Thinkster Math Wins EdTech Award Recognizing Innovation and Impact on Learning, The National Parenting Center's Seal of Approval, and continues its successful pace. Kadho Funding: $3.5M Country: USA AI application: automatic speech recognition (ASR) The renowned product of the Kadho company is Kidsense.ai. This software uses revolutionary technology in children's embedded automatic speech recognition (ASR) and speech assessment. With over five years of data, KidSense is the only voice AI tailored towards children in English, Mandarin, Korean, and more. Lately, the project was acquired by the leader in speech recognition Roybi. Cognii Funding: $118k Country: USA AI application: AI-powered virtual assistant Cognii focuses on developing AI tools for higher education institutions and corporate training for organizations. The program is powered by a virtual assistant that uses conversational and interactive methods to teach learners. The AI virtual assistant guides a student in open-format responses that improves students' critical thinking skills. Besides, Cognii provides instant feedback, deep analytics, and automatic grading feature. The company has got The National Science Foundation Grant, Best Learning Assessment Innovation Award by Reimagine Education, and EdTech Innovation of the Year in 2016. Early start in AI means a lot because the more input data you'll gather for AI till the launch, the better your product will harness its possibilities. So if you want to hit the market before AI becomes mainstream, it's smart to act now. Get a FREE consultation from our eLearning expert who works with EdTech startups to know where to start. How is AI used in EdTech? Top 6 Ideas to Consider Artificial Intelligence is a key to delivering online education with superior learning outcomes and cost-efficiency. Here is what AI-powered tools successful EdTech companies apply to achieve these goals. 1. Knowledge check to customize learning based on the current knowledge level When a student enters the Learning Mode for the first time, the system suggests taking a knowledge check that consists of a series of questions. The application of AI consists in the fact that the consequence of these questions is not predetermined as in traditional eLearning software but depends on every student's answer. Once a student gives consistently correct answers, they move on with more challenging questions. In the case of a wrong answer, the AI algorithm will shift them back. The goal of a knowledge check is to assess the current level of students' knowledge and adjust their curriculum to fill in their knowledge gaps. AI EdTech Startups Using It: ALEKS, Elsa aleks.com 2. Personalized learning path to effectively close knowledge gaps Based on the results of the knowledge check, the AI-based learning software suggests students their most ready-to-learn topic to master. The process of learning resembles the initial check. If a student answers correctly, the system moves them further to fully master the topic. In case a student answers wrong, the AI-enabled software gives a hint (allows revising the learning material or watching a short instructional video). After studying the entire topic, the AI-based knowledge check will automatically reassess a student to see if the topic is mastered or still needs to work on. AI EdTech Startups Using It: Century Tech, ALEKS, Carnegie Learning, Memrise. aleks.com 3. Virtual assistants and chatbots to perform as personal tutors The chatbot-style learning is built as a conversation where an AI-enabled chatbot or virtual assistant imitates a human teacher in all key activities: prompting students to answer a series of questions within the topic, tutoring them with personalized hints and tips, providing on-demand support in the moments of struggling, giving instant formative assessment, guiding students towards the topic mastery. AI EdTech Startups Using It: Knowre Math, Cognii, Riid, Amira Learning, Querium. Knowre 4. Predictive analytics to forecast and timely address possible gaps The AI algorithms analyze students' data along the learning process and detect underlying patterns in their performance. Then the data are compared with a benchmark, which is based on a predefined curriculum. Using the results of this analysis, the system makes a forecast of students' future performance. AI predictive analytics helps teachers timely detect which students require their intervention or support. And address the increasing problem earlier in the learning cycle. AI EdTech Startups Using It: Kidaptive, Dreambox dreambox.com 5. Grading and assessment to automate repeated and admin tasks To automatically grade assignments, at first, the AI software needs to learn from the available data. For that, the software gathers the metrics for grading assignments from papers graded by human teachers. Having these data, the AI engine learns to replicate the human grading process. The process iterates and improves thanks to the possibility of AI to learn constantly. For this purpose, the software stores the manual grading information for updating its own grading metrics. The advanced level of the automatic assessment process is hand-written paper grading as shown in the example below. AI EdTech Startups Using It: Gradescope, CenturyTech. gradescope.com 6. Speech recognition to engage students and improve the learning experience Speech recognition, as a subfield of AI, can be used for language learning, teaching kids to read fluently and debate, delivering lectures from teachers to students, tutoring, and other learning activities. Let's dig into the speech recognition technique in language learning. The engine "listens" to a speaker. Then compares the input data with the benchmark. The benchmark, in this case, is the voices of native speakers. Based on the comparison of the input speech and the benchmark, the AI assesses the speaker. AI EdTech Startups Using It: Elsa, Nuance, Kadho Elsa Any of the mentioned tools can be embedded into the existing LMS, LXP, or learning application. Or serve as a basis for a new eLearning project. For that, you only need a skilled team that will fulfill your ideas fast, at a high-quality level, and at a moderate cost.Let's discuss your idea! How to apply AI in Software for Education in 7 Steps There is a quick overview of 7 major steps in developing comprehensive AI software for education. Why only a quick overview? In reality, the entire process for an EdTech startup boils down to choosing the experienced and skilled software development team that will assist and support you from the first step till the launch of your product. So while your team codes, designs, and tests, the role of a startup founder majorly comes down to ideation, hiring the right talents in your team, supervising, and providing timely feedback throughout all the following stages. Step 1. Making the concept of your idea Start with creating a clear concept of how your software will improve the learning process, what it should do, how it will interact with students, and where the data will be stored. Step 2. Hiring AI developers After shaping your ideas into a well-thought concept, start searching for a development team. Before you hire dedicated developers, it's recommended to examine reviews about the software vendor and check their experience in custom eLearning software development. Step 3. Deciding on technological details Again, having hired a good team, you get high-skilled specialists who will advise you on the right solution. So you don't need to be an AI expert to launch your EdTech AI startup. Based on your project's concept with features and requirements, the team will select a suitable technological solution. Step 4. Gathering input data Most AI features, such as a personalized learning path, use existing algorithms and train them using your input data. The more input data you provide, the better the AI algorithm will perform its task. The input data can be in the format of text documents, excel files, FAQ, teaching programs, etc. Step 5. Development of your startup Your development team will perform several processes in parallel with your gathering input data. Their tasks will include the development of the front-end and back components of your software. The team of UX/UI designers will streamline the design to make it responsive, easy-to-use, and engaging. Step 6. Testing of your AI software Software testing is the next necessary stage before you receive your AI software for education. To ensure that the algorithm works in the way it should, your team will test the model using a ready-made dataset. Step 7. Integration into the education process The development team will integrate your software into the actual educational ecosystems, such as a website or a mobile app. Once the AI program is up and running, you can start measuring its KPIs to improve and succeed. Do you know that our eLearning team has already helped several EdTech startups to develop and launch their projects? We have rich expertise and a corresponding team of talents. We'll help you bring your idea to life. Get a FREE Consultation! Benefits of AI software for Education Decrease in routine tasks for teachers Automated assessment, grading, grouping students, reports, and analytics of students' performance are all performed by AI algorithms. It frees up teachers' time for mentoring struggling students and improve in-person relationships. Individual pace of learning AI software adapts the learning process to each student's level of knowledge, speed of learning, and desired goals. Also, AI can identify student's weaknesses and offer courses that suit best for filling the gaps. So the learning experience becomes highly personalized, and students get the most out of their education. High engagement rates Personalization powered by AI tailors the course for each student addressing individual goals and weaknesses. The AI-based learning process flows at a pace that is natural for a certain student. As a result, education becomes relevant, to-the-point and leads to higher students' engagement. Mentorship and tutoring for every student AI virtual assistants and chatbots help students improve their skills and close knowledge gaps without the help of a teacher. AI tutors provide a one-on-one learning experience swift and anytime. In fact, an AI-powered chatbot can answer students' questions within 2.7 seconds. Elimination of trial-and-error intimidation Trial and error is an integral part of learning. But the idea of failing intimidates many students. AI software for education is a far better way to deal with the issue. It allows teaching students in a relatively judgment-free environment, especially when AI tutors can offer solutions for improvement. All the mentioned benefits lead to a single and primary goal of applying AI in education: to increase the speed of knowledge acquisition and improve its retention. With this goal in mind, the attention turns from traditional eLearning solutions to AI LMS, AI-powered language learning apps, and LXP that harness the power of data to bring better educational outcomes. EdTech AI Market Trends The fact that cannot be denied is a steady growth of investments in AI in education The primary reasons for the focus on AI in EdTech: growing need for automating administrative tasks; rising demand for personalized education. AI in Education Market size exceeded USD 1 billion in 2020. From 2021 to 2027, it's expected to grow at a CAGR of over 40%. Among the drivers for the increasing demand for AI software for education: the increasing demand for integration of Intelligent Tutoring Systems in the learning process; rising research and development activities in deep learning; the growing focus on improving the quality of higher education; increasing demand for virtual classrooms across organizations; By application of AI in education, the market is shared the following way: Learning Platforms and Virtual Facilitators Intelligent Tutoring System Smart Content Fraud and Risk Management This is a perfect hint of what AI education startups should focus on to compete in the market. Key Players Propelling AI in Education There are 4 major players that share the overwhelming $250+ million of the AI-based learning market. Large providers of educational content Pearson, McGraw Hill, Cengage, and Knewton are the most familiar names in developing AI in higher education and K-12. For example, Knewton's Alta is a digitized platform focused on the AI-powered personalized learning experience. Pearson has created an AI department that is working on building the right algorithms using Artificial Intelligence to become more efficient and attractive to their customers. Weakness: Compared with the technological companies, the educational providers aren't specialized in technology. Strength: However, they use their in-depth industry knowledge and robust user base to expand traditional education to new territories. Higher Ed institutes Massachusetts Institute of Technology, Harvard Kennedy School, Alan Turing Institute, the Oxford University are among the leaders. They understand that in AI the early start is essential for success. Harvard Kennedy School was among the first ones in 2015 to nurture the AI Initiative. They started to assemble researchers, students, and experts to explore and develop Artificial Intelligence. In 2018 MIT announced an investment of $1 billion in a new AI college to prepare AI talents for the future. Strength: Compared with tech giants and content providers, the universities have top talents in AI and impressive research fundings. Weakness: The institutes don't create AI-powered software to sell. They are focused on training talents and upskilling society. EdTech startups Riid, Cognii, Elsa, Memrise, and Alef are only a few EdTech AI startups that have gained worldwide recognition. The AI software for education is often focused but not limited to adaptive learning systems, tutoring AI, smart grading, chatbots for education, etc. For example, Alef represents the application of Artificial Intelligence in K-12. With this AI-powered educational solution, students get personalized multimedia content, learn at their own pace. To support and assist students, teachers provide adaptive instructions along with AI-recommended interventions. Weakness: Compared with other market players, startups are usually newer and smaller. It means they are likely to have unsubstantial or irregular fundings, small talents pool, and the absence of well-established positions in the industry. Strength: EdTech startups are comparatively more market-oriented. They are more eager to experiment with new technologies and ideas. And they are more flexible in delivering their solutions. Considering the pandemic influence and the fact that the global EdTech market is expected to reach USD 341 billion by 2025, it looks like a perfect time to get a share in this market pie. To avoid common mistakes and grow your business, there are time-proven best practices describing how to successfully start an EdTech startup. Or get a direct FREE consultation from the skilled eLearning experts who have helped other EdTech startups to ground their ideas off the ground. To be successful with AI in EdTech, what you need is a robust engaging idea and a skilled well-coordinated team. We are ready to take responsibility for the latter. Just come to us with your idea!

After implementing AI, Georgia State University reported a 21,4% reduction in summer melt and 3,3% higher enrollment rate, resulting in 3 million dollars of tuition revenue. Colleges and universities get from 26 to 35% of revenue from tuition and fees. The greatest challenges that heavily cut their revenue are high dropout rates, summer melt, and ineffective “one-size-fits-all” education. Stakes are too high not to consider AI as a way to reduce costs and improve educational efficiency. Even though the benefits are obvious, only 41% of the institutions have an AI strategy. So the institutions who implement Artificial Intelligence in education first will get a competitive advantage. AI-enabled software to improve enrollment & retention, exam proctoring, and campus security Colleges and universities increasingly rely on AI algorithms for engaging prospective students, building personalized learning paths, offloading time-consuming administrative and academic tasks, and even creating a safe campus. And here are the tools designed for these goals:  AI-enabled tool for student enrollment aimed to lower summer melt and raise the enrollment rate. AI-powered prediction tool for increasing students' retention to identify what students are at risk to drop out and timely support them. AI-powered learner performance progress tracking tool to find students’ knowledge gaps and quickly follow up. AI-enabled exam proctoring tool to monitor students during remote online tests. AI-based tool for creating a secure campus to guarantee students’ security and data privacy. 1. AI-enabled engagement tool for student enrollment 86% of college presidents say that enrollment numbers are at the top of their most pressing issues.  The impact of COVID-19 backs up the concern: 45% of students report they are not emotionally ready to enroll.  30% report their inability to afford it.  The use of AI tools leads to enrollment going up and costs going down. A remarkable use case comes from Georgia State University. The university uses the AI chatbot to proactively send personalized reminders and messages. admithub.com An AI-powered chatbot also answers questions and guides students through a university's admissions and enrollment processes.  it informs prospective students about financial aid programs from the university (e.g. grants, scholarships, loans);  and also answers common questions about deadlines, documents, etc. The AI-powered chatbot communicates with students through the text messaging app on their cell phones. An AI chatbot gathers a knowledge database based on students’ conversations. After getting a question, a bot responds in seconds. When a bot doesn’t know an answer, the question goes to a human. Analyzing human answers, the AI adds new data to the knowledge database and learns this way. As a result, the chatbot can handle an increasing number of questions without human input. Another example of using AI in higher education is the AI-powered re-enrollment platform used at the Northern Vermont University.  It’s used to engage students who have previously dropped out but think about completing their education.  The software records a voice call made by a coach. NLP converts the speech into text. Then AI analyzes the conversation and especially the sentiment, making predictions about the engagement rate of a person.  To understand the sentiment, AI looks for specific nouns or verbs as well as positive or negative words, the frequency of these words to derive the overall tone. As a result, a personalized profile about each prospective student is created. It includes recommendations for future conversations, such as informing about the graduation period and cost or reminding of applying for grants. Based on the analysis of the conversations, predictive analytics forecast their chances to enroll and retain. reupeducation.com 2. AI-powered prediction tool for increasing students' retention The AI-based predictive tool analyzes academic, nonacademic, and operational data to identify students who are at risk to drop out. Using predictive analytics, the University of South Florida raised its retention rate to 91% and the six-year graduation rate from 68% to 75%.  To increase the retention rate, the predictive tool uses all pre-matriculation data, integrated with the data from the Learning Management System (LMS), the Student Information System (SIS), an online registration system, finance and HR systems.  Based on these data, AI software anticipates what a student's average score for the first semester is going to be. If the model predicts 3.6 GPA, and a student receives 2.6 instead, that indicates that educators’ support and intervention are recommended. The same prediction model improves the graduation rate, with the only difference that an AI-powered prediction tool uses records and data retrieved from all the previous semesters. beacon.io 3. AI-powered learner performance progress tracking tool AI-powered student performance tracking software analyzes the learning performance of every student based on their grades and competencies, and provides educators with an intuitive report.  To receive information about student's performance and their grades, the software aggregates data from LMS and grading systems.  All the gathered grades are available in a single assessment dashboard. The educators are able to monitor the performance progress of the entire cohort or every student individually. Also, the system flags the students at risk, allowing educators to follow up quickly. To analyze students’ competencies, educators need to gather and rate all the competencies that need to be mastered till graduation. AI parses these data to create a competency map.  A competency map shows what knowledge gaps a student has, which allows educators to detect and resolve the issue as early as possible. The corners of the competency “web” show the maximum level of competencies mastering. Also, the competency map allows tracking the progress over time comparing the student performance during the current year (the current level of mastered competencies is shown in green) with the previous period (shown in blue). competency.ai 4. AI-powered exam proctoring software The goal of AI in exam proctoring is to detect suspicious behavior during the test. Especially during remote testing, when a proctor cannot control in person how each student behaves. The program records test-taker's activity through their computer’s camera, microphone, and web browser. The software validates a student's identity using facial recognition or keystroke analysis. It can also scan a room to ensure the student is alone if it’s required. Colleges and universities opt for hybrid models that couple live remote human proctors with AI proctoring intervention in cases of suspicious behaviors, cheating, or the necessity of aid to a student.  The AI software captures compromising events, highlights session issues, analyzes the test results, and provides reports. A human proctor sees all the information in real time, including incidents detected by AI, and can react according to the situation.  examroom.ai 5. AI-based tool for secure campus Phishing and ransomware remain key campus threats. Why? Because in colleges and universities, there is a lot of personally identifiable information and intellectual property. AI can identify networking traffic, assess what “normal” looks like on a university network and do it at a larger scale that humans can accomplish.  For example, if a faculty member normally arrives at 8 a.m. and works until 7 p.m. and then maybe logs on at 9 p.m. from home, you wouldn’t expect that individual to be up at 3 a.m. connecting from China. AI can monitor those patterns to finddeviation. An AI-driven network security monitoring software uses a combination of supervised machine learning to study the actions of analysts from the IT team. It learns not only to detect deviations, but also the attributes within the deviation event that influenced the analysts’ actions. Based on what it learns, the AI provides recommendations to the analysts. securonix.com Another real threat is an active-shooter-on-campus. New technologies can help security officials anticipate crisis events and mitigate potential threats to students, staff and property. Through the use of HD cameras, featuring built-in artificial intelligence and deep learning technologies, colleges and universities can recognize faces and guns, and identify car plates.  The AI-powered video surveillance identifies unauthorized entries, prevents thefts and break-ins, protects perimeter indoor and outdoor, monitors vehicle access.  So it doesn’t require constant monitoring by humans. hikvision.com As security teams collect information in the AI-powered security platform over time, they start to identify trends (by comparing the recognised events with the risk matrix) and direct their staff to respond accordingly. livesafemobile.com AI-enabled software to improve students’ outcomes and prepare them for future career Digital transformation in education brings visible positive effects in a short time.  94% of colleges and universities report the improvement in student experience,  67% manage to meet the needs of the future workforce using up-to-date technologies, including Artificial Intelligence. Here are the tools that favor improving students’ learning experience and prepare them for future career: AI-powered tool for students’ 24/7 support and timely aid to instantly answer common questions, to help adapt to a complexity of a college or university and help with financial issues. AI-enabled tool for creating a personalized learning path to master the domain knowledge and fill in the knowledge gaps quickly and effectively. AI-based tool for career preparation to align students to future workforce needs and even find them a job. 1. AI-powered tool for students’ support and timely aid Colleges and universities use AI to answer routine students’ questions, schedule their tasks and activities, remind them about important events, notify them about loans, and even recommend courses based on how students with similar data profiles performed in the past. A perfect use case of an AI automation tool for higher education is "Jill Watson," an AI-powered teaching assistant at Georgia Tech University.  Developed by Ashok Goel, who launched an AI course in the university, it was supposed to help him during the deployment of the new course and provide students with immediate answers without human intervention.  When students asked some common questions about the learning process, timing, projects, the AI assistant Jill responded fast and anytime unlike human assistants who may be busy or offline. AI software can also provide proactive personalized assistance, which includes sending reminders, giving individual insights about courses or campus life, providing learning support, etc. For example, the AI chatbot for education Genie automatically creates a checklist for exam preparation based on a student’s schedule. Another application of AI for supporting students is just-in-time financial aid. Colleges and universities can use data about students to give them microloans or advances to get to the end of the semester and not drop out. tonywang.page 2. AI-enabled tool for creating a personalized learning path To build a personalized learning path, an AI-powered platform usually starts from a knowledge check. It’s not a predefined series of questions. Every next question depends on a student’s previous answer. Thus, questions’ difficulty maintains on the low level if a student answers mostly wrong, and goes more and more difficult if a student gives all correct answers.  Once the current level of knowledge is determined, AI builds a personalized learning path that starts from the most ready-to-learn topic. When going through a learning path, students get suggestions as to where they have gone wrong if the answer is incorrect. They can also see a detailed explanation and even the term dictionary. The AI-based platform reassesses students approximately every 20 topics and at least 5 hours in the learning path with knowledge checks to see what topic they’ve mastered. It’s a fully automated process that happens without teachers’ intervention. The knowledge check will go over the topics students have recently learned. If a student does well, these topics will move to the mastered category. The topics where a student still makes many mistakes will be added to the learning path to be reviewed again.  aleks.com 3. AI-based tool for career preparation Colleges and universities leverage AI technology to align students’ skills with workforce needs. Technology companies, such as IBM and Microsoft, are piloting AI-based platforms and career advisors that can be used by institutions to provide customized insights and recommendations to help alumni pursue their career goals. To get started, a student’s profile automatically fills with skills data based on the CV, related experiences or student’s assessment of their professional skills. Students can validate any of the automatically suggested skills and add additional skills that they believe they have. The AI-powered career assistant maps skills to corresponding roles and then calculates job matches and career paths.  If certain skills needed for applying for a position lack, a student can use recommended learning materials to master these skills. Interested in one of the AI-driven tools for colleges and universities? We know how to implement them in your learning environment. Talk with an expert to learn more!

Is AI considered Software? "AI" (artificial intelligence) is the knowledge and techniques that enable machines to mimic humans' cognitive functions (learning, problem-solving, perception, and decision-making). But when you implement AI concepts into a program or application, that becomes AI software. For example, an application like a chatbot that uses NLP is AI software you can run, and interact with. AI is like the theory, and AI software is the practical application of that theory. Just like how mathematics isn't software, but a calculator app (which uses mathematical algorithms) is software. Even when we talk about AI models, those models are part of the software, but not standalone software by itself. A trained AI model is more like a file containing structured data, weights and other data in a format like .pt (PyTorch), which a program (AI software) loads and executes. The training process (which produces the model) is done before it becomes part of AI software. Once trained, a model is just a structured dataset that doesn't "run" by itself without AI software that loads and executes it. AI can exist as a service (AIaaS), which is still software delivered via the cloud. But again, that's the software implementation of AI. Comparison between Traditional Programming and Machine Learning (ML) Machine Learning (ML) contrasts with traditional programming. While traditional programming relies on programmers to define explicit, scenario-specific logic and instructions, ML enables machines to learn autonomously and make decisions without detailed instructions for each task. Comparison Using The Example of Marketing Automation in E-commerce Traditional Programming Standard programming techniques involve creating precise instructions. For example, for a common application, SQL queries are used to target specific demographic groups based on predefined criteria, such as age, purchase history, and gender. In this case, a marketer specifies the target audience, and the programmer manually crafts the necessary query. A diagram showing the input, logic, and output of a traditional software program ML-Based Approach ML changes the traditional approach by employing past data to train models and identify patterns. Once trained, these models can then predict outcomes on new, unseen data. For instance, ML models autonomously determine target customers for marketing campaigns, based on insights from data patterns. This simplifies the marketing process and reduces the need for manual targeting and segmentation programming. A diagram illustrating a high probability of customers purchasing sports equipment after buying sports wear Advanced Segmentation with ML Instead of manually segmenting audiences based on predetermined criteria, an ML model can analyze complex data patterns to identify new target groups. This process reveals insights that traditional analysis may overlook, potentially making the marketer's strategy more effective and less manual effort. Necessity of Technical Expertise in ML However, it's important to note that ML models still require initial programming, setup, and ongoing maintenance, often by data scientists or ML engineers. Collaboration between marketers and technical professionals is still necessary. Marketers provide input on campaign goals and parameters, while data professionals develop, train, and maintain the ML models. ML's Superiority Using machine learning in marketing has significant benefits, particularly in targeting strategies. ML algorithms can analyze large sets of data more efficiently and effectively than traditional methods. They can uncover complex patterns, trends, and customer behaviors that human analysts might miss. This leads to more precise and sophisticated audience segmentation. Long-Term Benefits of ML While ML models require initial resources, they can ultimately reduce the workload for marketers and programmers. Automated processes for data analysis and audience segmentation free up human resources for more strategic tasks. ML enables personalized marketing at a scale challenging to achieve manually. It tailors marketing messages and offers to specific segments based on customer preferences and behaviors, increasing marketing campaigns effectiveness. In the long run, using ML can be more cost-effective. Refined targeting minimizes waste in marketing spend, as campaigns more accurately engage the intended audience. ML also provides a competitive edge. Companies that leverage advanced analytics and predictive modeling can often outperform competitors in terms of customer engagement and conversion rates. Comparison Using The Example of Sales Predictions Limitations of Traditional Predictive Software While traditional software can make basic predictions based on historical data, its predictive capabilities are not as advanced as those of machine learning. For instance, traditional software might predict that if sales in January have been around $100,000 for the past few years, then the next January's sales will likely be similar. This form of prediction is a basic extrapolation, assuming that past patterns will repeat under similar conditions. Advanced Pattern Recognition in ML Machine learning, however, employs algorithms to identify complex patterns and relationships in data that are not immediately obvious or predictable through simple extrapolation. ML models adapt their predictions based on new data, continually refining their accuracy. An ML model predicting sales, for instance, might consider historical sales figures, changing customer preferences, market trends, economic conditions, and adjusts predictions with new data. Traditional rule-based systems lack this adaptability. Data Processing: Traditional vs. ML Approaches Both ML models and traditional software can access the same data, such as past sales figures, customer preferences, market trends, and economic conditions. The key difference lies in how they process and utilize this data. Traditional software operates based on explicit rules set by programmers. For example, a programmer might establish a rule like, "If past sales were X and the economic conditions are Y, predict sales to be Z." This approach is limited by the programmer's ability to anticipate and code for every potential scenario. In contrast, ML models learn directly from the data. They autonomously detect complex patterns and relationships within the data, with no need for explicit programming for each scenario. ML algorithms can discover subtle correlations and trends that may not be evident or predictable in advance. Adaptability of ML vs. Static Traditional Software A significant difference is that traditional software does not adapt or learn independently. If market dynamics change or new trends emerge, the software will continue to operate based on its original programming until a programmer updates the rules. Meanwhile, ML models continuously update their understanding and predictions as new data arrives. This allows them to adapt to changes in patterns and trends without human intervention. Probabilistic Nature of ML Models However, there is a nuance with machine learning models not found in traditional programming. Unlike the deterministic outputs of traditional software, ML models provide probabilistic estimates. This means they predict the likelihood of various outcomes rather than offering absolute certainties. As such, continuous evaluation and potential retraining of these models are essential to maintain their accuracy. Two types of ML: Supervised Versus Unsupervised Learning Models Supervised Learning Model (Labeled Data) Supervised Learning Applications In supervised learning, models learn from data that has predefined labels, and algorithms must find structure and patterns in the data with guidance on what outcomes to predict. Supervised learning is used in a wide range of applications, including but not limited to voice recognition (learning to understand and transcribe speech), and medical diagnosis (learning to identify diseases from symptoms and test results). A diagram showing a typical supervised learning prediction workflow Let's use teaching a supervised machine learning model to identify dog breeds as an example Preparing a Labeled Dataset The first step is to collect a large dataset of dog images, labeling each image with the correct breed. For example, Golden Retrievers are labeled as "Golden Retriever," Poodles as "Poodle," and so on for each breed you want the model to recognize. Feature Analysis and Training the Model The model examines the features in each image, such as color patterns, ear shape, size, fur texture, and other physical characteristics distinctive to each breed. During training, the model is fed these images and their corresponding breed labels. The model's task is to learn the patterns and characteristics indicative of each breed. For example, it may learn that Beagles often have a certain ear shape or that Huskies commonly have a specific fur texture. Improving ML Accuracy As the model goes through more images, it improves at recognizing and understanding the subtle differences between breeds. It adjusts its internal parameters to reduce prediction errors, like mistaking a Labrador for a Golden Retriever. Recognizing dog breeds is more complex than simply identifying if an image contains a dog. Breeds can have subtle differences, and there's significant variation within each breed. Hence, the model needs to learn to focus on breed-specific characteristics while ignoring individual variances. Testing the Trained Model on Unseen Data After training, the model is tested with a set of images it hasn't seen before. For instance, if it has never seen a picture of a dog breed like a Dalmatian during training, it may struggle to identify a Dalmatian. The more diverse and comprehensive the training data (different breeds, colors, sizes, backgrounds), the better the model becomes at correctly identifying dog breeds. During the training phase, the model iteratively adjusts its parameters to minimize the difference between its predictions and the actual labels in the training data. This process is typically quantified using a loss function, which the model aims to minimize. Some breeds may be harder to distinguish, requiring a larger or more diverse set of training images. Over time, adding more images and examples of difficult-to-distinguish breeds can improve the model's accuracy. The quality of a supervised learning model depends heavily on the training data. Poor quality data can lead to issues like overfitting, underfitting, or biased predictions. Unsupervised Learning Model (Unlabeled Data) Applications and Advantages of Unsupervised Learning Unsupervised learning is helpful in scenarios when we want to discover new patterns in data that were not previously considered. Common applications include market segmentation, anomaly detection, and organizing large datasets. Unlike supervised learning, where models learn from data that has predefined labels, unsupervised learning algorithms work with data that has no labels. The algorithms must find structure and patterns in the data without guidance on predicting outcomes. The focus is on understanding the structure and distribution of the data. Example of K-means Clustering K-means Clustering is a classic example of an unsupervised learning algorithm. It partitions the data into 'k' distinct clusters. The algorithm assigns each data point to the nearest cluster. The goal is to minimize variance within each cluster and maximize variance between different clusters. Feature-Based Clustering in Unsupervised Learning Imagine, you possess a large collection of dog photos without breed labels. Your task is to organize these photos meaningfully without knowing the breed of each dog. The unsupervised learning model analyzes photo features like dog size, fur length, ear shape, and color patterns without prior knowledge of dog breeds. Pattern Recognition and Grouping in Unsupervised Learning The model then tries to find patterns among these features. It might notice that some dogs have long fur and floppy ears, while others have short fur and pointy ears. Based on these observed patterns, the model starts grouping the photos. Dogs with short fur and pointy ears might be grouped together, while those with long fur and floppy ears might be placed in a different group. The model measures the similarity of features to create these clusters. Each cluster represents a set of dogs that look similar. In an ideal scenario, these clusters might end up representing actual dog breeds, like one cluster having mostly Labrador Retrievers and another mostly German Shepherds. However, the model doesn't know the specific breeds; it only recognizes similar groups. Dimensionality and Visualization in Data Clustering Unsupervised learning models represent data points in two or three dimensions for visualization and easier comprehension. For instance, in dog images, one axis might represent fur color, another the tail length, and so on. Data points close together are considered more similar and may belong to the same group or cluster. High-Dimensional Data and Dimensionality Reduction Techniques While data can have many dimensions (with each dimension representing a feature or characteristic of the data points), humans are best at visualizing in two or three dimensions. By plotting the data points based on these features, we can visually inspect the data. Data points that are similar will be closer together in this space, while different ones will be further apart. For example, images of a specific dog breed cluster together based on shared features like ear size and fur length. In practice, data often has over 3 dimensions (high-dimensional), making direct visualization impossible. Techniques are used to reduce the number of dimensions while preserving as much of the significant structure in the data as possible. As the observer, you can look at these clusters and recognize the breeds based on your knowledge. You can also adjust the criteria or the number of clusters (like determining the desired number of groups) to better match what you know about dog breeds. Looking for AI solutions customized to meet the unique requirements of your business? Get in touch with us to find out how we can support your project. You can't write the code for a car to navigate a roundabout, but AI can Driving a car with software is impossible. You can't write the code with an if-then-else statement, case statement, or switch statement for a car to navigate a roundabout. But software can't train a car to drive. That's what AI does. While conventional software is programmed to perform a task, AI is programmed to learn to perform the task. Code is the primary artifact of “traditional” software. In AI software, code is not the primary artifact. Yes, when we build AI software we have to write code. But the primary artifact of AI is the data (data collection, data labeling, data analytics using algorithms to spot patterns). Row Data Collection The software cannot drive a car. But software can collect data. Cars have near-field sensors, microphones, cameras, lidar, and radar. And then AI just starts learning. It learns how to make a right turn, how to make a left turn, how to go straight, how to recognize a stop sign, how to recognize a traffic light. Because of patterns, because it's got all this data. Once it sees a thousand stop signs, it can recognize a stop sign itself. That stop sign could be straight on. It can be cocked a bit, it can be bent, and it recognizes it because it's seen it enough times. “The reason that cars can drive now and they couldn't drive themselves 10 years ago or 20 years ago is because of the cloud. The cloud changed the game with AI” James A. Whittaker The cloud changed the game because all that data that AI needs to learn takes a bit of storage. You know, Google had a cloud, a proprietary cloud that did nothing but search. Amazon had a private proprietary cloud that did nothing but e-commerce. Facebook had a proprietary cloud that did nothing but store social data. This is where the modern AI was born - in these clouds. Alongside these technological giants, cloud migration companies have emerged to help various businesses and organizations leverage cloud capabilities. You know, all that camera data from all those cars going through all those roundabouts takes a bit of storage! And the algorithm has to have access to it all. Before the cloud, it didn't. Data labeling Data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding meaningful labels to provide context so that a machine learning model can learn from it. For example, you prepare 10 000 pictures of cats and label them "these pictures have cats". Then you prepare a bunch of pictures that don't have cats so they have the label "not a cat". And over time, the AI figures it out itself. If you've ever taught a child to read letters, you show them the flashcards over and over. They guess it right, you're like "Hey, good job!" Guess it's wrong "No, you did wrong." We do the same thing with AI. We show it a bunch of examples, and when it gets it wrong, you change how data is labeled so it knows it got it wrong. It’s reinforcement learning. “Let me make a prediction… Whereas programmers, developers in modern times are the most central to a team developing software, my prediction is data scientists are going to take over as the most important part of an AI project. Not coding. Because you have to recognize good data from bad data. You have to be able to label it correctly. Those labels help the algorithms to understand what's going on” James A. Whittaker Machine Learning Algorithms You take your data, you label it, you organize it as well as a human can. And then you stick the algorithms on it. Algorithms are used to analyze data, to gain insight, and to subsequently make a prediction or create a determination with it. For example, look at the reinforced learning algorithm that provides recommendations for you on YouTube. After watching a video, the platform will show you similar titles that you believe you will like. But if you watch the recommendation and do not finish it, the machine understands that the recommendation would not be a good one and will try another approach next time. Machine learning is a set of algorithms that enable the software to update itself and "learn" from previous outcomes with no programmer intervention. In summary, a traditional algorithm takes some input and some logic as the code and drums up the output. As opposed to this, a Machine Learning Algorithm takes an input and an output and gives some logic, which can then work with new input to give one an output. I think instead of universities studying the nuances of programming languages, we're going to be studying the nuances of algorithms… The nuances of data structures, how control structures work, whether to use an if-statement or a switch-statement or a lookup table are not going to matter. The skill that is going to matter is your understanding of probability statistics”James A. Whittaker Finding Patterns A good data scientist can look at data and say, “That's probably the algorithm we should start with.” But that's the process: get the data and start running the algorithms through it and hope that those algorithms start finding patterns. AI use cases fall into one or more of seven common patterns. The 7 patterns are hyper-personalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. For example, an algorithm can find such a pattern as “fraud/risk” demonstrating that things are out of the ordinary or expected. “And this is a key skill that distinguishes a good Data Scientist from a mediocre Data Scientist. It's picking the right algorithms, understanding those patterns, and then iterating, combining algorithms to generate patterns” James A. Whittaker Feedback loop Another fundamental difference between AI and conventional software is that software never changes. We build software, we release it to the field and it just does the same repeatedly. But once it gets out in the wild, it doesn't really change unless humans update it. AI changes. An artificially intelligent car will go through a roundabout and it might discover something new. It might discover a car driving the wrong way on the roundabout. And once it figures out what to do - that's new data. That's the thing about AI: it keeps learning even after it's released. “The conventional software didn't wake up one day and said, "You know what? Fuck that shit. "I'm tired of processing those inputs. I'm gonna do something else. James A. Whittaker“ That's not the way conventional software works. The AI software does work that way. AI software gets better itself. The feedback loop is a cycle without an end: AI observes user actions and system events and captures data for analysis. AI analyzes this data against historical trends and data from other sources, if necessary. AI predicts outcomes and recommends specific actions. The loop starts over again. The system continues to refine its recommendations based on the latest feedback (whether the user accepted the recommendation and what happened after). Rule-based chatbots vs AI chatbots One illustrative example of the difference between traditional software and AI-driven software is the contrast between rule-based chatbots and AI chatbots—a distinction we're well-versed in, thanks to our extensive experience in custom chatbot development, both rule-based and AI-driven. Rule-based chatbots work with simple instructions. They follow a script like, "If the user says 'A,' then reply with 'B.'" You'll find them handy for frequently asked questions or basic customer service tasks. Think of them as those voice-operated phone menus that guide you through a list of options. However, these bots have a big limitation: they don't learn or adapt. If a user asks something outside the script, the bot won't have an answer. This can make user interactions feel robotic and often frustrating, requiring ongoing tweaks from developers. On the flip side, AI chatbots are a lot smarter. They use tech like machine learning and language understanding to figure out what users really want. Over time, they actually get better at helping people thanks to the data they collect. They can even notice patterns in questions from different users and refine their answers. These bots can handle multiple languages and tailor their responses to individual users. Plus, they know when a problem is too complex and a human needs to step in. That's why businesses that want more natural, intelligent interactions are going for AI bots. Our example of Artificial Intelligence software in use today As you see, the most important potential for AI is to be a recommendation engine. Having solid experience in custom eLearning development, it is not surprising that our offshore software development company is looking for ways to implement AI in eLearning projects. The core idea of AI in eLearning is the implementation of a Recommendation Engine on the eLearning platform. This tool recommends micro-learning content to the user based on their learning experience and other data that a user might provide (including search history or specific requests). From the UI/UX perspective, it looks like an AI-powered chatbot or an AI-powered dashboard like YouTube or LinkedIn has. Such chatbot assistants can automatically imitate tutors, understand the level of expertise of a learner, and pick information that is well fitted to a particular level. For example, it can recommend skills to acquire for each learner and then match them with the corresponding courses. That is just a small fraction of what we do. Recommendation Engine is developed to address a specific business need. It's hard to find a one-size-fits-all solution. Enhance efficiency and customize operations with our AI software development services, designed for your specific data and business needs. Contact our experts for project support

How can your business benefit from implementing AI in L&D and HR processes? Our team with 15+ years of expertise in custom eLearning development will analyze your business needs and recommend the best solution. Talk with an expert for an insight! The number one threat for CEOs is the “talent risk” or the difficulty to find and keep the right talent and skillsets. Nearly 90% of executives and managers say their companies either already face skill gaps or expect them within the next five years. The most common tactic for addressing skill gaps is upskilling and reskilling for internal mobility (cited by 56% of respondents), and, of course, external hiring. However, almost 70% of HR professionals are still not satisfied with their organization’s ability to meet their internal mobility goals. At the same time, top-performing companies such as IBM, Hilton, Unilever, and others already have adopted Artificial Intelligence tools to successfully manage this challenge.  AI-enabled Software to Solve Talent Management Challenges Attracting and retaining enough employees at all levels to meet the needs of a company's growth, developing a robust leadership pipeline, and transferring key knowledge are among the top talent management challenges.  AI helps to raise to a new level the hiring, upskilling, and reskilling processes.  AI-powered talent management software is often based on three key components: AI-based tool for workforce skills assessment. Managers use it for a clear understanding of the skills and capabilities of their workforces to be transparent with employees about the skills that are needed, especially when the workers don’t possess them.  AI-driven career path with integrated LMS/LXP for upskilling & reskilling. The tool identifies the skill set a person needs to excel in their role and their skill gap, then connects the person with training content from LMS or LXP that corresponds to that gap. AI-powered workforce forecasting tool. It can accurately forecast talent gaps which your company may face in near future. It benchmarks your employees' skills set with competitors to align with industry trends. AI provides recommendations for internal mobility or redeployment strategies. 1. AI-based tool for workforce skills assessment AI provides HR managers with a greater understanding of an employee's skill set than they would get by assessing the feedback from manager surveys. Using machine learning algorithms, talent management software automatically grades and ranks the job-ready skills of every employee based on their profiles and competency matrix. Before the implementation of this approach, skills need to be described only once by humans. And then these descriptions will be parsed by a machine. Thanks to the implementation of AI, a manager will end up with a list of candidates with their ranked level for the skills the company needs. retrain.ai matrix lms skyhive.io 2. AI-driven career path with integrated LMS/LXP for upskilling & reskilling AI-powered talent management software integrated with eLearning software such as LXP or LMS is designed to fill skill gaps by reskilling individuals from within the organization (or even outside) to meet the demands of digital transformation and to keep pace with future skill needs.  An employee, when looking for new opportunities, uses the tool for a clear understanding of the open positions within their organization and the required skills for each position. AI algorithms analyze an employee's existing skillset, match them against the job requirements, highlight the missing skills, and recommend the appropriate courses to acquire these skills. This approach leads to more effective training because it is based on a high motivation from the outset and supported by a more personalized learning experience. By implementing Artificial Intelligence in education of employees, internal career growth opportunities enable businesses to more effectively retain top talents. fuel50.com retrain.ai skyhive.io 3. AI-powered workforce forecasting tool AI-driven workforce and skills forecasting software performs human capital analysis and compares results with the real-time industry average. It predicts emerging jobs and evolving skills that companies need to invest into their future-ready teams. retrain.ai How to implement AI in your HR and L&D processes? Companies that are aiming at the more effective strategy to address skill gaps may consider taking these actions: Start with creating a competency matrix and perform employees’ current skill set analysis. Understanding which skills to develop in the workforce requires comparing the supply of each skill with the business’s strategic goals. Integrate your HR software with eLearning software using the Talent Management framework. Organizations will likely need to join homegrown learning resources with eLearning content from multiple providers (online platforms, universities, and eLearning content providers).  Get expert HR/eLearning software integration consulting. They gather business requirements for digital transformation, perform an audit of the current level of workflow automation, help you make the right software decision, and perform implementation using AI where it offers better value for money). 

Discuss your idea of creating an AI-powered language learning tool with Belitsoft's eLearning experts. Interested in adding innovative tools to your eLearning software? We offer assistance, including AI-based chatbot development services, and have the expertise to guide and support you. Major Online Language Learning Challenges and Ways to Solve Them To create your own language learning app, it's necessary to understand what major challenges online learners face and choose the tools to overcome them. Find below the top 4 challenges in language learning with a short review of features solving these challenges. The features and tools based on AI will be described in detail later on.  1. Lack of motivation Even highly motivated people are getting bored after several lessons. The main reasons for it are too long lessons, boring exercises, unfitting difficulty level, insufficient rules explanation, or absence of personalization. How to solve: Placement test to detect the current level and start learning from that point; Diversity of exercise types to alter different types of activities and avoid boredom; Smart notifications to remind learners about today's lesson at the best time; Gamification to create a competitive spirit and award for diligent learning; Personalized learning path to offer only relevant materials for studying. 2. Not covering grammar well Intuitive learning is good at the beginning when grammar is simple and it's enough to focus on vocabulary. But once the topics get more complicated, learners need deeper explanations.  In the meanwhile, 20% of learners report not getting enough grammar instruction from the platform. And without understanding grammar, a learner will always make the same mistakes. So the task of a well-thought language learning app is to cover all the aspects of language learning. How to solve: Scaffolded support to give on-demand explanation during exercises or in case of mistakes; Chatbots to offer a rule explanation upon learner’s request. 3. Poor knowledge retention Language learning apps are aimed to teach fast. For example, Duolingo states that 34 hours of studying using the app equals one semester at the university.  The problem is all newly learned information is easily forgotten if not repeated. How to solve: Data-driven spaced repetition technique to repeat new information with certain intervals for better knowledge retention; Regular assessments to detect learners' knowledge gaps and address them. 4. Overcoming the language barrier Most students are afraid to make mistakes and sound bad when speaking. Even when people meet natives they are afraid to test their skills. Learning languages online helps overcome this fear by starting to practice with AI instead of a real person. Later on, learning techniques that involve other people can be added. All the conversations are close to real-life scenarios to practice speaking skills that with high probability will be needed in real life. How to solve: Speech recognition techniques to train learners to speak during the learning process Applying immersive learning with AR and VR to create real-life situations Social learning to enable learners to communicate with peers Most of the mentioned tools that allow solving these top challenges are based on Artificial Intelligence. Let's see now how they work. 6 Key Applications of AI in Language Learning By resolving major learners' problems and fears, the creators of AI-powered language learning programs make such software more appealing for users and as a result for investors.  Let's find out which AI tools you should consider for that. Placement test To offer learners the course of the difficulty level corresponding to their actual skills, provide them with a thoroughly designed placement test at the start of learning.  The AI-based placement test is not a series of questions predefined by admins. The system adjusts the difficulty of every new question based on your previous answer.  After a learner answers correctly, a more complex question comes next. In the case of a mistake, the next question will be simpler. The purpose of a placement test is to evaluate a learner's knowledge level and detect weaknesses that shall be practiced first. Elsa Сhatbots (text and voice) Chatbots for communication practice is another useful application of AI-powered language learning apps. It will imitate real-life conversation allowing learners to improve their communication skills. The language learning chatbots use AI algorithms to understand the context of the conversation and answer in a unique way. It means that every learner gets a different answer for a similar inquiry.  Duolingo The latest trend is AI chatbots based on speech recognition for teaching the right pronunciation. An AI-powered voice chatbot speaks with a learner in real-time. The conversations cover topics from everyday life. A learner simply uses the mic of the smartphone, and the AI listens and assesses pronunciation instantly, without downtime. Then AI scores a learner based on a benchmark (native speakers' voices) and highlights the mistakes. A learner can click on the sentence and get more detailed feedback on pronunciation, intonation, and fluency. Elsa Personalized learning path To satisfy and retain learners, the app must offer a personalized approach to learning. The AI algorithm assesses each learner's strengths and weaknesses and provides a personalized set of learning materials. Using AI in language learning helps to reduce the learning time thanks to a more immersive and tailored approach to learners' needs. Elsa In addition, the algorithm applies a spaced repetition to ensure that learners repeat new information for long-term memorization. AI-powered spaced repetition technique adjusts the time when a piece of content shall be revised based on the difficulty level of the mastered material. Here is how AI-driven spaced repetition may look like for users. Every topic has five difficulty levels. After completing all five levels, the topic is considered finished (highlighted with gold). But passed some time (defined by AI), the icon gets shattered, which means that the material needs to be reviewed. Duolingo Automated assessment The most advanced example of implementing AI for automated assessment is the Duolingo English Test. This test defines the language level according to the international standard benchmark.  AI technologies (namely, Machine Learning and Natural Language Processing) are used to: automatically create thousands of assessment tests; assess and grade complex answers; align the test results with CEFR language proficiency levels (A1 - C2); gather all information to count the final score; Duolingo Proactive smart notifications Notifications reminding to train skills allow learners not to deviate from the course and help the app retain users. AI can be used to improve the effectiveness of app notifications. For that, it's necessary to figure out when to send notifications and what to tell every learner.  Having a daily activity log in the app, AI picks a time when to best send the reminder and also what to say in that reminder to return a learner back. After applying AI-enabled notifications, Duolingo reported having big gains in terms of the number of people coming back. Duolingo Scaffolded support To assist a learner in the moment of a struggle or in the case of a mistake, it's smart to use short instructional videos, integrated tips, and dictionary articles.  They can be opened by tapping or hovering over the word and can show a rule explanation, pronunciation samples, or possible word forms. Duolingo Benefits of AI in language learning The described AI tools not only eliminate challenges in online learning but also bring obvious benefits to learners and consequently to software creators. Adapting to the needs of learners With AI integrated into language learning, the system collects multiple data about learners, their skills, interests, etc. When analyzed, these data are used to make learning highly relevant to learners interests and tailored to their actual skills and expected goals. As a result, learners can study at their own pace. AI-based language learning apps and platforms repeat topics and emphasize things that learners struggle with and engage them with the tasks they’re best at.  In case a language learning platform or app is used as a part of blended learning, AI data also gives detailed information about students performance and even predicts their future performance. Providing instant feedback The AI engine instantly grades tests and even evaluates essays automatically. This allows learners to immediately analyze mistakes, repeat a topic, and probably make more tests to reinforce the acquired knowledge. Getting feedback fast lowers the intensity of expecting the results. Besides, it helps to detect weaknesses and address them effectively. The reason is when learners still remember test questions and their own answers, analyzing mistakes becomes more effective.  Producing smart content A language learning app or platform usually stores learning content in large amounts. Using this data, AI can generate bite-sized lessons that are relevant to a learner's knowledge level and interests. The use of bite-sized lessons, also known as the microlearning approach, leads to better knowledge retention and high engagement of learners who don't get bored during short learning sessions. New ways of perceiving information (visualization or simulation) can also be powered by AI, making the course more engaging and real-life. Trial-and-error learning without the fear of failing Making mistakes is normal for learning but still intimidating. Most learners start feeling ashamed or even scared. AI in language learning won't criticize learners in front of the class. AI evaluates learners without judging them. Redefining the role of teachers Now the number one problem for teachers is the lack of time. A large part of their teaching activity is focused on organizational tasks instead of students. Thanks to AI, such administrative and repeated tasks as grading, assignments, grouping, etc can be automated. Instead, teachers will be able to focus on learners and on teaching instead of spending valuable time on side tasks. Top 5 AI-powered Language Learning Apps Duolingo An American language-learning website and mobile app, Duolingo is the most popular language-learning platform in the world. Its revenue was estimated to be $180 million in 2020. The total number of users is over 300 million.  Duolingo offers 106 different language courses in 38 languages. It's fully equipped with AI algorithms to offer each learner a customized learning experience. With their language chatbots, Duolingo helps people learn a new language without the embarrassment that might happen by miscommunicating with a native speaker. Thу application has got multiple awards. For example, it's included in TIME Magazine's 50 Genius Companies and named one of Forbes's "Next Billion-Dollar Startups 2019". The AI features used by Duolingo: chatbots to imitate real-life communication with native speakers; the placement test to define the current skills level of a learner proactive reminders to notify smartly about the necessity to start learning; custom lessons to calibrate the difficulty level of exercises for a specific learner; Monetization model: The company uses a freemium model. The content is fully accessible without charge, although Duolingo also offers a premium service for a fee. Memrise A British language platform, Memrise is focused on the increase of knowledge retention by using spaced repetition of flashcards. The application won the best App winner award in Google Play Awards in 2017. Memrise can help to learn 23 languages. Among their users, there are more than 50 million people in 189 countries. The company revenue as of 2020 was estimated to be about $23 million.  The peculiarity of the app is real-time object identification. This method means that users can take a photo of an object and feed it to the app to discover the name of the object in the target language. Memrise was named as one of the Best App winners of the second edition of Google Play awards back in 2017. The AI features used by Memrise: personalized learning activity to adjust the difficulty level and content relevance for each user; speech recognition function to practice pronunciation text chatbots to practice learner's vocabulary in conversational situations Monetization model: Memrise started as a free language app with premium paid features. Later the company shifted its business model to a full paid language app. The early learning courses and content is still available for free to let a learner try the app without paying. Rosetta Stone Created by the American EdTech company, the Rosetta Stone app is reported to have now a revenue of over $101 million and more than 515 thousand subscribers. This language learning application helps users to learn 30 languages. This app uses the TruAccent speech engine to ensure users get the right articulation and uses images, text, and sound to teach words and grammar by spaced repetition method. The company cooperated with the US Army offering a special military version of Arabic to help troops in the Middle East learn the language for conversations and phrases important in a military situation.  Rosetta Stone also won multiple awards from non-profit organizations, magazines, and the software industry. The AI features used by Rosetta Stone: speech recognition to analyze learner's pronunciation of new words and provide instant feedback; real-time objects recognition to detect objects around a learner and learn new words. Monetization model: The company offers a 3-day free trial. Then a learner needs to pay for a subscription to access the content. Busuu Created in Spain and with headquarters in the UK, London, Busuu is focused on allowing users to interact with native speakers. The company was supposed to generate more than $40 million in revenues in 2020.  Busuu provides courses in 12 different languages on the web and mobile to over 100 million learners worldwide. The peculiarity of Busuu is language learning based on the combination of AI-powered learning content, interaction with other learners, and one-to-one live tutoring with professional teachers. The company also has Busuu for Organisations for universities and businesses. Organizations can give their students or employees access to Busuu Premium, track learner's progress, and use the app over time. Besides, organizations get personalized courses that include lessons about specific contexts. For example, Uber gets lessons with situations and vocabulary that a driver would encounter with their passengers. In 2016, Busuu became a launch partner for Google Home Assistant, offering voice-activated lessons in Spanish. A year later it launched a VR app for learning Spanish for the Oculus Gear and Oculus Go. And after one more year, Busuu launched a comprehension test for the Amazon Alexa platform. The AI features used by Busuu: personalized learning to provide content that is relevant to learners interests, goals, and skills; grammar training tool to review rules on-demand and practice weaknesses vocabulary training tool to timely train vocabulary for better retention Monetization model: Learners can sign up for free, or subscribe to a Premium membership to unlock further features like advanced grammar lessons, Offline Mode, McGraw-Hill Education certification or our adaptive Vocabulary Trainer. Babel Babel is a German language learning app and eLearning platform with an office in the US. The company revenue as of 2020 equaled $129 million. The number of users surpasses 20 million. Babel offers courses in 14 languages. In 2016, Fast Company recognized it as the most innovative company in education. Babbel learning focuses on real-life situations by offering interactive dialogues. Curated review sessions reinforce previously learned material by bringing it back in new contexts. The AI features used by Busuu: speech recognition technology to build correct pronunciation; personalized learning activities to engage learners; review manager tool to enhance knowledge retention based on spaced repetition method. Monetization model: After launching the first free beta version, Babbel's founders upgraded the app a year later and decided against an advertising and mixed-finance model (freemium), opting for paid content. Learners can choose among 4 subscription plans by course duration. 3 Everyday AI Tools from Google Available for All Language Learners Everyone can try the basics of using AI for language learning without a specific application, AI-powered LMS or LXP. Just make sure that you have added English to the list of languages in their smartphone or PC settings. It’s necessary so that the system could understand and transcribe the learner’s speech. 1. Google Docs speech recognition Google Docs has added the possibility of typing with voice. This voice recognition feature is a free and mobile-friendly tool. It can assist in conversational activities. Learners can use it to evaluate the intelligibility of their speech. 2. Interacting with Google Assistant Google Assistant is a perfect tool to train speaking and listening skills. A language learner can ask the assistant simple questions such as ‘What is the latest news?’, ‘What’s the weather like?’, ‘What are the best TV shows to watch for foreigners?’, etc.  This is a great way to test the intelligibility of pronunciation as well as listening and comprehension skills. 3. Using Google Maps to practice directions Tools with integrated AI are not only helpful for practicing pronunciation but also for developing speaking skills. Google Maps is a navigation, mapping app not meant for language learning. But when you study directions, names of different places, and how to get somewhere, using this app is highly helpful. To start with, a learner can study the basic commands such as: turn right/left, cross the street, next to, go straight ahead, opposite to, etc. list nearby places. After that, learners can practice their speaking capabilities by telling how to go from their location to the places listed. Takeaway There are over 1 bn people in the world learning a language. And over 56$ bn is the actual size of the language learning market. So every new startup and EdTech company has a chance to succeed and get a slice of the market. To stand out from the competition and succeed, it's wise to use the benefits of Artificial Intelligence in Education.  For effective deployment of AI, the early start is critically important. So creating a language learning app, consider which AI tools you will apply. Get free expert consultation now!

Use cases of AI chatbots for Learning and Development in corporates 1. Employee coach Corporate learning is not something people love. But it doesn’t have to be this way. And when the companies compete for better employees, good learning opportunities can be an edge. The learning process can be performed through a Facebook messenger bot which trains and quizzes employees. It is designed with microlearning approach in mind – small chunks of information for brief attention spans. The bot can adapt messages to individual employees and boasts a 98% engagement rate. The developers of such chatbots claim that corporate learning bots can save employees about 2-5 days per year which would be spent on actual work, rather than study. 2. Curriculum Customizer Personalized learning is one of the top trends right now. Just as chatbots are. No wonder people look for ways to combine them. Released a month after Facebook messenger, MOOCBuddy was a bot for finding the right Massive Open Online Course (MOOC). Services like Coursera or edX made online learning widely available but choosing the right class was still a problem. MOOCBuddy talked to people and suggested courses based on the topic, language, duration, accreditation and several other factors. It also sent reminders and updates, unless the users opted out. MOOCBuddy was likely the first chatbot of its kind, but at the moment it is no longer available. The concept is still alive, however. Magpie continues the idea. Besides suggesting optimal courses based on user’s profile, it can also recommend educational information in public domain: TED, HBR, BBC and more. The bot begins by asking user several questions to determine their job, position, and industry. That is enough to get a quick recommendation. But if the user provides details like workday structure or skills self-assessment, Magpie can create a detailed list. The bot doesn’t eliminate human involvement. The content that it suggests is taken from a database approved by living, breathing editors. Use cases of AI chatbots for improving student’s learning experience Across the world, classes have migrated online, and often educators cannot personally reach students for teaching, giving feedback, or encouraging them to enroll. It is elevating the use of chatbots in the education sector that serve now as a medium of student-teacher communication. The AI chatbot comes especially handy as: Virtual tutoring tool to personalize every lesson and engage students. Tool for students’ support 24/7 to get instant answers to common questions and to manage students’ life effectively. 1. Virtual tutoring with AI chatbots The most useful application of AI in education is automated, intelligent tutoring. The AI chatbots can help teach students using a series of messages, just like a common chat conversation, but made out of a lecture. A chatbot can test students, give questions, and assess them. Students do not need to contact their teachers and wait a few hours for the information. They can send a message directly to an educational AI chatbot and get real-time scaffolded support with instruction and continuous assessment.  Such on-demand support helps students become independent learners by reducing student frustration and by providing appropriate guidance at the moment of struggle. Also, such a tutor chatbot opens up the teacher’s time to engage with students one-on-one. The chatbot assesses every student’s level of understanding and then provides them with the following parts of a lecture according to their progress. And because data is constantly collected along the way, the chatbot can identify the skills students need to work on to increase their score and will suggest practicing the skill again. Knowre Another popular AI chatbot application is language learning. A good example is Duolingo that has been investing in AI and machine learning to make language learning more engaging by automatically tailoring lessons to each individual — kind of the way a human tutor might. The great thing about using an AI bot is that it will get smarter the more it is used. The purpose of an AI-powered chatbot is to simulate a human for practicing scenarios that users are likely to encounter. They focus principally on functional skills and prepare students to use their language skills in the real world. The language learning chatbots use AI algorithms to understand the user context and be able to answer contextually and uniquely. It means that every user gets a different response for a similar inquiry. Duolingo 2. Virtual students’ support 24/7 with AI chatbots 64 percent of internet users consider 24-hour availability to be the best feature of chatbots. For schools, colleges, and universities, which don’t operate 24/7, chatbots are a way for students to get answers instantly whatever the time. When a teacher has a bunch of students to teach, answering repetitive questions about lesson plans, classes, and schedules is tiring and time-consuming. That’s when AI chatbots and virtual assistants come especially handy. Artificial Intelligence chatbots and virtual assistants don’t just answer simple questions or tell students what the temperature will be tomorrow, but they can organize student’s life on a personal level by proactively taking actions and managing tasks on their behalf. The most famous AI-powered virtual assistant chatbot is Genie, developed and implemented at Deakin University, Australia. Presented through a mobile application, it leverages chatbots, artificial intelligence, voice recognition, and a predictive analytics engine to deliver personalized advice and services, guided assistance, and curated content. It gives students easy access to their unit information, results, timetable, or answers to common student questions. Genie Genie is a proactive agent. So if you have an exam in two days and you haven’t been reading the material, Genie is going to remind you that the exam is coming up and you haven’t touched your material,” Deakin's chief digital officer William Confalonieri said. “To know that, we need to know that you haven’t been reading material (because the learning management system hasn’t been accessed) - we collect all that information in a predictive analytics engine that gathers data and allows Genie to react.” However, software developers realize the limits of AI and use AI chatbots to facilitate conversations with the right support staff when needed. Need your own chatbot? Contact us for a free quote! Use cases of AI chatbots for improving educators’ productivity To educational leaders who struggle to reach a generation that shuns official websites and mass emails, the use of a chatbot in education solves the issue. The reason is students feel communicating in chats more personal. Besides, chatbots are available 24/7 and respond instantly. The response time for 99 percent of queries ranges from 6 to 10 seconds. The increasing number of administrations and teachers recognize this cost-effective and valuable way to keep their students hooked and streamline educational and administrative processes more efficiently. For education stakeholders, AI-powered chatbots come especially handy as: Tool for automation of administrative tasks to save educators’ time and switch their focus on more critical tasks. Tool for gathering feedback about learning material to understand the efficiency of teaching methods and improve the curriculum. 1. Automation of administrative tasks with AI chatbots Admission & Enrollment Forward-thinking educational leaders use AI-powered chatbots both to relieve admissions staff work by answering repetitive questions and to reach students for matters like encouraging them to enroll.  Using a chatbot reduces the summer melt, the phenomenon when students who apply and are accepted to a college fail to enroll. Summer melt affects 22,8% of college-intending high school graduates each year.  By using the AI chatbot to send personalized reminders and walk students through admission processes, Georgia State University reduced summer melt by 19 percent in the first year of implementation and increased the percentage of students completing pre-enrollment processes. An AI virtual chat assistant can answer questions about documents or deadlines and give instructions. Answer common inquiries about types of financial aid (e.g. grants, scholarships, loans) and provide standard fees info.  The chatbot isn’t just the recipient of inquiries and questions – schools, colleges, and universities can use it to proactively send reminders, messages, or news. AdmitHub Retention Poor support is one of the reasons students drop out of college. This means it is necessary for every institution to always guide their students by giving them timely and accurate information. But with no optimization, it is almost impossible to ensure each student is getting proper support. That’s when AI-chatbots come to help. They are programmed to answer common questions instantly and help students with administrative topics 24/7. An AI chatbot has a knowledge database based on real students’ conversations. Once it gets a question, a bot responds in seconds. However, when a bot doesn’t know an answer, the question is sent to a human team. As a human answers new questions, the AI learns by adding new data to its database. It leads to the chatbot’s capability of handling an increasing array of circumstances and questions without human input. 2. Gathering feedback about learning materials with AI chatbot Seeing students’ performance is easy for a teacher. Understanding which of your methods contributed to achieving such performance is another thing entirely. AI chatbots are ideal for teachers and institutes to collect students’ feedbacks. Its usage upgrades the learning processes thanks to increasing the participation of students. Replacing the traditional surveys, a chatbot talks to students via a special messenger and processes their feedbacks, letting the teacher know what works well, what is ineffective, and what else they can implement. HelloTars As the answers are coming in, the AI software analyzes the semantics of what the students have said and prepares a report that a teacher or administrator can review.  NLP and ML are those technologies that can analyze the feedback and understand the sentiment, looking for specific nouns or verbs as well as positive or negative words, the frequency of certain words to derive the overall tone. Then feedbacks are divided into positive (green), neutral (grey), and negative (red) based on the words and associated emotions. As a result, educators can understand the pain points faced by dissatisfied students and find out effective ways to identify and remove those bottlenecks. Belitsoft specializes in both chatbots and e-learning. If you have a project in mind, contact us for a free quote. No strings attached. How to implement AI chatbot in education Building AI chatbots in eLearning differs a lot from basic Q&A bots and needs a thorough analysis.  Implementing an AI chatbot for educational institutions may include the following stages:  Start with the analysis of your objectives. Based on the primary needs of your administration, teachers, and students, it’s necessary to decide what features your chatbot must have and what tasks it must solve. The use cases described in this article can help you analyze. Find an expert development and consulting team. Building an AI bot capable of a human-like conversation requires highly professional programming skills, thorough analysis, and strategy. That's why many educational stakeholders decide to hire outsourcing companies to implement their ideas professionally, cost-effectively, and fast. Such services usually cover all the stages of the bot implementation and include consulting, development, and post-launch support, saving educators’ time and headache. Test your chatbot. At this stage, an AI-powered chatbot is tested to communicate with a restricted group of real students to see if it can be helpful and resolve the assigned task. Deploy and assess. Deploying a chatbot is not time-consuming. Your development team will just need to ensure that all endpoints are connected, and the bot is integrated with your entire infrastructure.  AI chatbots in the education industry can be used both to optimize the teaching process and to make the learning process more engaging and personalized for students. They can also significantly reduce the workload of the administrative staff of the educational institutions. As a result, we can expect an immense growth of the education sector, beneficiary interactions between students and educators, and a superior classroom environment. 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Want to get one of the AI-powered tools in your eLearning product or create a personalized learning platform using AI from scratch? Our LMS development company will implement the features that will bring you the maximum benefit. Let's talk! Example of AI-based Personalization in Corporate Learning The importance of effective workforce training is becoming critical. To cope with this issue, the forward-looking leaders already started to adopt progressive tools, including AI in talent management and continuous education. The management of the US-based air medical transport company (Air Methods, a 2,800-person helicopter company in Englewood, Colorado), understood the importance of well-organized continuous education to train pilots. When using traditional webinar-based training, every employee used to receive the same course, regardless of their learning speed and current skills. Such a method required a lot of time, drew little engagement, and didn’t motivate pilots to grow. So the company adopted an aviation training system built on Artificial Intelligence. Process of Implementing Personalized Learning Using AI The system tested every learner using short quizzes and games. Then AI adapted the learning path to each learner’s knowledge of a topic based on the test results.  If a pilot struggled with a certain topic, the AI LMS repeated it by presenting the information in a new way. After completing a section, every pilot was retested and progressed to the next module. Results after Implementing Personalized Learning Using AI According to the company leaders, the implementation of AI personalized learning in corporate training not only boosted the effectiveness of the training course but also helped to attract talent. The company could also significantly reduce their training and onboarding costs by cutting half the number of personal instructor-led training classes together with the duration of their onboarding program from 10 to 5 days.  Belitsoft has experience in developing personalized eLearning solutions for Healthcare, Aviation, Finance, and other important industries. And we know what tools and features can become your competitive advantage. Let's discuss your case! What is Personalized eLearning? Personalized learning is an educational approach to address the individual learning needs and interests of each learner. The goal of personalized online learning is faster knowledge acquisition and better knowledge retention. According to the L&D Global Sentiment Survey, the two hottest topics in online education now are personalization and Artificial Intelligence. But why? By personalizing your learning content with the help of AI, you make the learning process faster and more effective.  In its turn, this leads to shorter training time, reduced training costs, and higher productivity of the trained workforce.  Features of Personalized Learning Software using AI The major components of the personalized learning platforms that can be customized for each learner are: Relevant information. Using only relevant content that is based on learner’s interests leads to faster and better results. Preferred eLearning content types. By detecting the preferences of each learner, the system builds a personalized path majorly using preferred content types (video, audio, text, etc.) to increase engagement. Individual pace of learning. Thanks to adjusting the learning pace, every learner gets the necessary amount of time needed to master every topic or skill. Corresponding difficulty level. Using the right tools, especially an AI-powered skill map, the system more quickly identifies the competency level and learning gaps that impede progress, allowing learners to achieve the desired results faster. Immediate feedback. Thanks to more frequent and immediate feedback, learners can understand their performance and progress in real-time. It happens through formative assessments, quizzes, and knowledge checks. Get all benefits of customized e-learning. Our experts will consult you about selecting the best way to personalize your training course. Benefits of AI in Personalized Learning for Any Business With more companies facing the challenge of upskilling and reskilling or arranging an effective professional education and licensing for their workforce, personalized learning has become a proven method that can bring success to businesses in a scalable manner. And the most obvious benefits that using personalized learning software brings to business are: Speed up the professional education process and increase its quality. Licensing and professional education require a lot of time, which negatively affects the working process.  But personalized learning makes learning targeted. Learners receive tailor-made information they require to fill gaps and achieve learning goals quickly. Also, the information is usually bite-sized, which makes classes short and increases knowledge retention. Such an approach encourages employees to self-direct their training and favors creating a culture of continuous learning within a company. Boost engagement and motivation that leads to higher productivity. When adult learners can control and self-direct the learning process, they are likely to feel more motivated.  Learners pick up lessons skipping unwanted information that doesn’t correspond to their role, need, experience, or skill level. Besides, they take a proactive role in their education and study at a comfortable pace.  That’s why personalized learning is a good choice to introduce professional learning on a regular basis. Reduce training hours and boost ROI. Profit margin growth is the result of higher productivity and shorter training time.  Personalization usually implies a bite-sized learning (microlearning) method. So learning at the working place becomes shorter and quicker. It equates to less learning time and fewer training payroll hours. Besides, personalized learning information is consumed faster and with better results. So employees spend less time on training and demonstrate equal or even better outcomes. Want to increase revenue, boost productivity, and streamline internal mobility within a company? Personalize your LMS or LXP with the help of AI tools. Or create an AI-powered platform from scratch. We'll recommend the tools and features that will bring you the maximum benefit. Let's talk! How to Get Started with AI Personalized Learning in a Company? Deep personalization that includes key aspects of training is performed with the help of algorithms. And even though traditional algorithms can perform this task, their possibilities are limited.  Only AI-powered systems can provide a high level of personalization that improves over time. The reason is the AI engine constantly refines recommendations without the intervention of developers or admins by updating themselves based on previous outcomes. Personalized learning with AI encompasses all the core aspects of online training: personalized learning path; relevant content based on knowledge level, skills, interests, and goals; automated knowledge checks; prediction of knowledge gaps; proactive learners’ support; tutoring, etc. Here is an example of AI-powered training software with individual learning paths and personally recommended content. linkedin.com Setting up personalized learning with an AI engine requires the following steps: Step 1. Create a skill matrix. To determine the learner's knowledge and then build a learning profile, start with creating a skill matrix. For this, skills should be described by humans once. It can be done by analyzing employees’ CVs with the help of text analytics. Or employees can fill in a simple form with skills in rows and knowledge levels in columns. And then these data will be parsed by a machine. As a result, every learner will have an individual profile that reveals their current knowledge level and skills. Step 2. Aggregate data about the learning background. However, knowing only skills will not give a complete picture. Artificial Intelligence captures, gathers, and analyzes employees’ learning backgrounds from different sources within the company. This can be easily performed with the help of xAPI. With xAPI you will collect information about employees' previous and current learning experience from LMS, LXP, TMS, HRIS, etc with a great degree of detail. Step 3. Use the AI engine to find patterns. Having aggregated all the data, the AI spots trends and builds patterns for each learner.  These data give admins or an L&D team valuable insights about learners’ performance and educational preferences. In the next step, this information will be used to suggest the most relevant content. Step 4. Build a training course based on AI-driven recommendations. By identifying learning trends, the system can provide learners with study materials in the formats and of the difficulty level that best suit their learning preferences and skills.  For example, AI informs an admin that a certain employee uses short videos as the major content resource and tends to watch about “Technical SEO”. Based on this data, the recommendation engine starts suggesting this learner more videos instead of a textual format, all around the preferred topic. Thanks to getting highly relevant content in a preferred format, more learners succeed in the training course. Step 5. Measure and assess regularly to adjust the course. Learners start training using an LMS or LXP. The performance of each learner is measured and assessed regularly.  The AI engine takes the latest results of a learner and adjusts the course. And this cycle constantly repeats. Any business that implements AI in training courses gets a strategic advantage thanks to the possibility to detect missing skills in the workforce and arrange effective upskilling and reskilling to address this problem in time. 5 Pillars of Personalized Learning using AI-powered LMS/LXP AI-based knowledge checks to detect the current knowledge level and gaps. To discover a learner’s educational background and knowledge gaps, an AI-based system starts with a knowledge check. This is a test that has no predefined consequence of questions. Every next question depends on a previous learner’s answer. In the case of correct answers, the difficulty level of questions constantly grows. In the case of an incorrect answer, the difficulty level lowers.  The AI-powered LMS or LXP tests learners’ knowledge regularly to guide and adjust learning activities in time.  After understanding the baseline proficiency, the learner is given a personalized learning path corresponding to the current knowledge level. pluralsight.com AI-selected training content to address individual learning goals Artificial Intelligence tracks how a learner is performing and progressing in the course. The previous performance is also taken into consideration.  Then AI uses the gathered data to detect the level of learners’ proficiency and their interests. The next step that AI makes is finding and recommending corresponding learning materials. So the course starts from the tasks revealing the learners' knowledge level. The proper selection of the content translates into faster knowledge acquisition and higher productivity of the workforce after completing such training.  When it comes to the personalization of content types, modern LXPs and LMSs can also suggest multiple content types.  For example, some learners get information better when watching short videos. Others learn quickly through reading. So the course will be majorly built on these preferences.  linkedin.com AI-created learning path to target detected knowledge gaps After detecting your knowledge, interests, and goals as well as selecting relevant content, AI builds a non-linear learning path. Non-linear means not predefined, chosen by learners by their needs and allowing them to complete the course at their own pace.  Learners are not forced to learn what they already know. They skip certain segments of a course and start learning what they actually need.  This is a key to higher motivation and engagement. Besides, it saves a lot of valuable time that will be spent on work thanks to efficient to-the-point learning.  valamis.com AI-powered tutoring to provide proactive assistance in learning AI virtual assistants and chatbots for education now act like tutors giving proactive recommendations about learning materials and assisting during the process. They teach by simulating a human tutor like in the learning app Duolingo and can also remind learners about assignments and due dates. Another aim of AI assistants is the selection and recommendation of the right courses for learners based on the topic, skills, duration, goals, and other factors.  Besides suggesting optimal courses based on the user’s profile, it can also recommend educational information in the public domain (TED, HBR, BBC, etc) or help find a human tutor when needed. juji.io AI-enabled feedback to give a responsive assessment immediately AI algorithms guide a learner through the learning path giving an immediate responsive assessment.  For example, a learner struggles with a certain topic, there can be additional tools like pop-up hints, dictionaries, calculators, etc. Meaningful and immediate feedback helps learners understand how they are progressing, detect weak and strong points, and get information that addresses their weaknesses. An immediate machine-driven assessment also excludes a direct interaction with an admin or instructor, which represents an effective trial-and-error method of problem-solving ( eliminating the fear and hesitation of direct interaction with an instructor).

Should you require AI chatbot development or custom LXP development, elearning software development company Belitsoft may be the solution you're seeking. Our eLearning specialist is open to discuss your challenges and goals in detail. What is an AI-powered LMS? AI-powered LMS stands for the Learning Management Platform with the support of Artificial Intelligence. Often AI LMS is named as LXP but we will use AI LMS to simplify it. When learners create profiles and start learning, an LMS gathers data about their skills, preferences, time spent on learning, performance data, etc. Then the AI engine processes the data gathered by your LMS and builds patterns of the learners’ behavior.  The more data it analyzes, the more AI learns about learners’ needs, preferences, strengths, and weaknesses, continuously improving.  So AI performs as a suggestion engine providing relevant content recommendations and highly personalized learning paths based on the skills, goals, and preferences of a learner.  The major business goal of implementing AI in L&D is increased velocity of information processing and understanding. If learners complete the course fast and get more useful information from it, they start applying new knowledge faster, which leads to increased productivity. The AI LMS can be separate software or a part of the company’s L&D infrastructure together with TMS, HRIS, HRM systems. Standard LMS vs AI LMS An AI-based Learning Management System can be made from scratch by your requirements or built on the basis of a standard LMS you already have. Here are the key differences between both types of LMS. Standard LMS AI LMS The learning path is predefined by LMS administrators and cannot be personalized for each learner. AI tracks previous learner’s performance to identify areas where a learner lacks in skill and recommends the appropriate materials. The learning path for each learner is built individually. Learners depend on the admin or curator to get feedback about their tasks, which takes time and restricts communication to working hours only. AI provides learners with real-time assistance acting as a virtual tutor, answering questions anytime. The learning process is fully managed by an administrator who creates and delivers content to learners. Often, the course creation is done manually. AI automates the admin’s job doing all the routine tasks (knowledge checks, grading, grouping, etc). It saves the admin’s time and company resources. Our eLearning team, with extensive experience in LMS development, can upgrade your standard LMS into an AI-based LMS. Let’s discuss which AI tools will bring the maximum benefit to your business and employees’ training experience. Business Benefits of AI in Learning Management System With AI in Learning Management System, your business gets the following benefits: Fast and simple onboarding The AI-based social learning feature enables new hires to share their experience and interact with peers and managers who will support a newbie and answer possible doubts. AI detects people with similar interests, positions, and skills. And suggests the list of colleagues to choose from. A learner can even choose a team to invite to the discussion if they coincide in the areas of interest. By interacting with others, new employees can learn a company's policy, rules, information about departments and processes, etc through an LMS in an interactive and engaging manner, which leads to faster and better results.  docebo.com Effective upskilling & reskilling When employees create a profile, they choose their current skills. Based on this information, AI in Learning Management System finds the closest job opportunities by skills. The learner can also see what skills to master for the desired position. This allows L&D leaders to cover competency gaps within the company they may not even realize exist by upskilling and reskilling the staff. It creates stable internal mobility processes that lead to a better company image and saved money. eightfold.ai Improved resources allocation Smart resource management with the help of an AI-based Learning Management System brings 2 advantages at once: AI automates repetitive tasks of HR and L&D teams, such as generating offer letters, collecting and maintaining documents, manually assigning tasks, creating curricula, etc. The AI LMS can manage content scheduling and delivery instead of admins. Also, the AI engine analyzes learner’s skill gaps and the required job competencies to find and deliver targeted recommendations, taking the load off the admins to manually perform competency mapping. Thanks to the targeted content recommendation by AI, your employees save many training hours, bringing a resource advantage to the company.  Improved employees’ motivation and productivity AI-driven personalization facilitates high engagement between the learner and the recommended training materials. AI refines results with every new inquiry making content search much easier for learners.  And when people find the relevant and bite-sized content, they find it easy to understand and learn quickly. It leads to faster acquisition of the desired skills and the feeling of motivation to return for more. Increased ROI An AI-based LMS saves training hours thanks to delivering only relevant and personalized content to learners. As a result, the same learning outcome is received in fewer hours. And every saved hour employees spend on the company tasks. Moreover, the acquisition of skills that a company needs transforms into profit and future competitiveness of the company. 5 AI tools that fully change the learning experience AI tools and technologies, like Machine Learning, NLP, Data Analytics, Chatbots, and Deep Learning algorithms help LMSs to provide a highly personalized learning experience, make employees’ training more engaged and effective, bringing major benefits to the business. Discover what AI tools you need to rocket up the outcomes of employees’ training. 1. AI-based tool for content recommendation AI tracks the progress of learners within the course and their previous learning behavior and performance. Based on the gathered data, AI can detect interests and the level of a learner’s proficiency. Then AI finds and recommends optimal learning material and starts the course from the tasks corresponding to the learner's knowledge level. For example, the learner is repeatedly passing the section “Social Media Marketing Tools”, the system suggests that the learner finds this topic difficult to understand or is specifically interested in it.  Accordingly, the AI engine in LMS recommends new modules around “Social Media Marketing” to either aid in resolving any confusion or for further exploration. valamis.com 2. AI-driven tool for building a personalized learning path The AI-powered recommendation engine gathers data about an employee's current skills and the skills that an employee needs to acquire for the desired job position. Then Artificial Intelligence matches the current skill-set to the job requirements, highlights the missing skills, and recommends the relevant courses for getting the required skills. Having these data, the AI-powered recommendation engine creates a smart individual learning journey. Relevant content is suggested to the learners automatically by constantly gathering data from user performance, skill-sets, and preferable content. As a result, a learner dashboard displays trending topics with suggested popular content, and the recommendation list with a curated list of relevant courses to each learner. When a course is completed, your learners will see recommendations for a new training course according to their needs. Together with helping users find better learning paths, AI can also help L&D teams understand what their employees need to learn.  fuel50.com 3. AI-powered Chatbots in Elearning AI coaching chatbots are the future of employee engagement. These AI-powered chatbot tutors enhance student engagement throughout their learning lifecycle – from recruitment and onboarding to learning, assessment, and alumni engagement. By delivering personalized content tailored to individual learners' needs, progress, and preferences, these chatbots offer a unique learning experience. Furthermore, they ensure higher completion rates by streamlining assessments across various skills and experience levels, posing non-Googleable, skill-specific questions in a conversational format. AI-powered virtual tutors can interact not only through text messages but also by voice commands. valamis.com 4. AI-based tool for automated knowledge checks After a learner completes a course, an AI-based tool automatically runs the checks of the training results.  Depending on the performance during knowledge checks, the LMS can pop up an automated report highlighting strengths and weaknesses.  Then the AI engine recommends modules to re-take to address the weaknesses. All the further recommended content and learning paths will be composed depending on the knowledge check results.  aurameir.com 5. AI-powered tool for monitoring & reporting The AI-based Learning Management System provides your L&D leaders and business stakeholders with such data as the list of best and worst performers, employees’ mastered skills, completed modules, and many others. The company’s managers can view the statistics within the company and compare them with the industry benchmark. It gives a possibility for bridging potential skill gaps in the company by arranging timely internal mobility.  retrain.ai How to get and deploy the best AI-based Learning Management System? Once you have realized what AI tools you want to implement, here are the steps to follow: Build or choose a highly customizable LMS to add AI capabilities In case you don’t have an LMS in your company, the perfect idea will be to develop an LMS from scratch. In this case, you’ll be able to add the AI tools you want from the very beginning. Or you can choose a ready-made LMS with high customization possibilities that could support advanced AI add-ons. Choose the AI tools to deploy. Based on the information given above you can choose which AI-powered tools will bring the major benefit to your company’s learning experience.  Launch a trial run. Before deploying the AI LMS to your workforce, launch a trial run for a selected group of users who will pass the course and share their feedback. This approach helps detect possible weaknesses and gaps. Deploy and improve on a regular basis. After launching the AI LMS, start collecting the results. The helpful data may include how often and how long your employees complete a course, what skill gaps your company experiences, or what content type is the most popular among your workforces. All these data collected and managed by AI, help continuously improve the course and focus time and resources on the future-proof skills to ensure the competitiveness of your business.