Developing an AI-based Learning Management System: Benefits, Limitations, and Best Practices to Follow

Having a well-oiled learning management system is a must for any organization that provides educational services, whether they teach students or their own employees. Building an artificial intelligence-powered learning platform allows you to take teaching services to another level.

When correctly applied, AI can automate and streamline content management, create personalized curricula and training sessions, improve student engagement, and more. The trick is to determine which LMS tasks need AI enhancement.

In this article, we discuss how AI can improve an LMS, overview which capabilities to enhance, and share our best practices for AI implementation.

This article will be useful for teams that plan to develop an AI-based learning management system from scratch or enhance an existing LMS with AI capabilities.

How can AI empower your LMS?

Artificial intelligence is no longer a fancy and overly expensive technology — it’s a useful tool that enhances and automates lots of business activities. The Global AI Adoption Index 2021 [PDF] by IBM reveals that 31% of organizations deployed AI-based applications in 2021, while 43% were exploring this possibility.

Enhancing software with AI usually helps businesses generate revenue or improve services by automating routine tasks and speeding up the analysis of large amounts of data. When applied in an LMS, AI capabilities can improve system performance and make your product more competitive. For example, you can achieve the following benefits from an AI-based LMS:

1. Automate routine administrative tasks. Managing an LMS is time-consuming because of scheduling lessons, monitoring users, processing requests for technical support, and other monotonous and repetitive processes. To handle these tasks, you can implement AI modules that are capable of:
   • creating personalized complex curricula
   • helping users resolve common issues
   • tagging and categorizing learning content
   • generating personalized reports
   • onboarding new teachers and students
2. Analyze accumulated data. An LMS collects tons of data on students’ progress, scores, studying challenges, common issues with the system, etc. This information can provide valuable insights to an organization that uses an LMS, but it can be hard for a human to analyze massive amounts of data. An AI system, on the other hand, can process tons of information much faster than a human and detect insights that are unobvious to a regular employee.
3. Improve content accessibility. An AI module can accelerate the process of translating content and adapting it for other languages and cultures. Tutors will only need to review the reworked content. Also, AI can make educational tools more convenient for users with disabilities by reading out the content, describing it, or writing down student responses during tests.
4. Enable real-time user assistance and knowledge assessment. With remote education, students experience a lack of real-time connection with tutors, especially outside lectures. If they have questions or need to discuss assessment results, they can’t do it right away. AI can fill this communication gap by providing real-time answers to simple questions and helping students resolve issues. It can also analyze students’ responses, provide personalized feedback, and recommend training courses.
5. Transform and curate content. The more content an LMS accumulates, the more effort tutors and administrators have to put in to keep it relevant, easy to use, and adapted to various operating systems and devices. AI can adapt content, analyze which lectures may need updates, and alert tutors about this need. And AI will do it more quickly and efficiently than a human or a determined algorithm.

Moreover, AI can take a given piece of content and adapt it to create custom learning sessions for corporate training, for instance. Unlike students, employees don’t have much time to study, so they need short and efficient training sessions. AI can analyze an employee’s capabilities, needs, and goals and transform existing content into a series of short lectures.

Enhancing these LMS tasks with AI sounds promising and beneficial both for the organization that uses an LMS and for developers. Organizations can improve their services by introducing AI, and developers can create cutting-edge and competitive products. However, not all LMS features need to be improved with AI. To save your time and development budget, we’ve created a list of features for an AI-powered learning management system. Let’s take a look at them in the next section.

6 LMS functionalities to enhance with AI

With the great variety of improvement possibilities offered by AI, it’s not easy to pick the features that need to be improved the most. When choosing technologies to build an AI-based learning management system, consider these factors:

• The functionality or task you need to enhance
• Your organization’s experience with AI implementation
• The availability of ready-made AI models and solutions for your task

Pay attention to the following functionalities during AI-powered LMS development:

1. Virtual tutors and assistants. Instead of interacting with real tutors, learners can get explanations and help in real time using virtual tutoring and assistance. Traditional student assistance in an LMS relies on a preset list of questions and answers. Adding AI capabilities helps an LMS understand unusual requests, provide answers that are more relevant to the questions, and create an experience close to communication with a real person. For example, Cognii uses AI to process open-format responses and provide students with one-on-one learning any time they need.

Virtual assistants are usually powered by AI chatbots that employ machine learning algorithms, text analysis, and natural language processing (NLP). Some chatbots use AI voice generation to provide voice assistance. More elaborate virtual tutors also use AI to generate a 3D model of a teacher in virtual reality. To explore more cases of ML use cases, check out our article on using machine learning for automotive industry.

2. Interactive voice recognition. Voice assistance is particularly important for learning new languages, teaching people with writing and reading impediments, and teaching kids that can’t write yet. To interact with such students, an LMS needs to be able to read the text aloud, recognize a student’s speech, assess it, and respond.

AI can efficiently perform these tasks by combining speech recognition, NLP, and voice generation. Keep in mind that you don’t always need to enhance all processes related to oral communication with AI. For example, you can teach the LMS to form questions in English using rule-based automatic question generation [PDF] algorithms.

3. Personalized training sessions. The educational process is more efficient when it’s adjusted to the needs and wants of a certain learner. While a traditional LMS provides some adjustment options when choosing a training course, AI makes this process more flexible and personalized.

Using predictive analysis and cognitive neuroscience will help you build platforms like Century Tech. It uses AI to personalize training sessions by gaining insights on learners’ performance, achievements, and qualifications.

You can also use predictive analysis to automate course selection and scheduling. Predictive analysis does all the work for teachers by analyzing a student’s learning path and suggesting relevant courses.

4. Attendance management. Monitoring student attendance at remote lectures takes time and effort. Some students may also cheat by attending only the start of an online class. One way to solve this issue is by proving attendance with biometric checks. But this solution is rather expensive, since all students will need some sort of biometric scanner.

AI algorithms for image recognition provide a more elegant solution. With a collection of student photos as a dataset, AI can detect students’ faces in a video feed. It can also calculate attendance time, making it impossible for students to cheat. Such solutions are already widely used in 48,000 public schools in India.

5. Intelligent dashboards. Classifying and analyzing data gathered by an LMS can help you understand how users interact with it and find improvement opportunities. For example, you can create dashboards with students’ performance, the most requested training courses, the average length of a training session, the most common questions and issues, etc.

Powering such dashboards with AI-based predictive and prescriptive analytics helps you to not only overview historical data but also discover patterns in the way students study and optimize training courses.

6. Assessment of learner engagement. The level of a learner’s engagement may indicate issues with a training course, the poor quality of educational materials, issues with a tutor, etc. But this metric is particularly hard to assess in online education. Traditional methods of assessment — interviews and surveys — can’t always show real engagement issues.

Deep learning algorithms can calculate engagement based on a learner’s history of interactions with the LMS and educational content. They can also compare these interactions to the peer group, suggest reasons for changes in engagement, and propose improvements.

Best practices for developing an AI-powered LMS

Developing an AI-based learning system requires a great deal of caution and expert knowledge of both the technology and hidden obstacles in the development process. Here are some tips on how to improve the development process:

Evaluate the need for AI. Though AI can automate and improve many educational activities, that doesn’t mean that it should. Adding AI to any possible LMS functionality also adds to the software development time and budget. That’s why it’s best to analyze end users’ needs and the educational process before planning AI enhancements.

For example, virtual assistance and knowledge testing are popular features in an AI-based LMS. But it will be inefficient to implement these features into solutions with simple, straightforward navigation systems and no need to process unusual requests.

Look for a public dataset and customize it. Preparing data for training an AI algorithm is the key to its correct work. Collecting, labeling, and classifying all this data from scratch is an extremely time-consuming task, but you don’t always have to do it. Instead, search for public datasets that more or less fit your goal and adjust them for your client’s needs.

You can start with this list of free datasets that has collections for educational software in general, image processing, NLP, machine learning algorithms, and more. Customizing these datasets will help you speed up development and save the project budget. To learn more about working with images, check out our article on AI-based image processing.

To customize a public dataset, you need to collect, sort, and label data of your real students already collected by an LMS. If you lack data on a particular type of students, activities, etc., you can use AI to generate new records similar to the ones you already have. This way, you can get more training data and reduce the possibility of creating a biased AI model.

Anonymize user data. Data privacy and security in AI-based software causes a lot of concerns because such solutions use lots and lots of real user data. Moreover, they accumulate data while they work. To protect it and alleviate ethical and legal concerns, you need to anonymize personal records that the AI uses. Anonymized data doesn’t contain any personally identifiable information and therefore can’t violate anyone’s privacy.

To anonymize data, you can apply generalization, perturbative and non-perturbative masking, local suppression, and other techniques.

Conduct enhanced testing. Testing of AI-based solutions shouldn’t be limited to usual functional and non-functional tests. You also need to ensure the AI algorithms have as few biases as possible. Developing a completely objective system is impossible — it will always reflect the views of developers, people that prepared the dataset, or people whose data the dataset contains.

We are blind to our own biases and therefore can’t see them in our software. Or at least we don’t consider them an issue. That’s why it’s worth testing your solution on as many datasets as you can and assessing its work periodically. It’s especially important during custom AI-based LMS development for a large-scale organization that needs to educate people from different cultures and with different upbringings, religions, etc.

Watch for emerging requirements. Many governments discuss policies for applying artificial intelligence in schools, universities, and private training. Once applicable national or industry laws, regulations, or standards are released, you’ll need to adjust your software to meet them. That’s why it’s best to start paying attention to policy discussions now and taking them into account while building an AI-based LMS. Also, keep in mind that you still have to comply with the GDPR, NIST SP 800-53, ISO 27001, and other general-purpose IT regulations, standards, and laws.

However powerful it is, AI is not without its limitations and can’t successfully tackle all challenges of online education. Let’s see when it’s best to choose other technologies during AI-based LMS platform development.

Limitations of AI for education

As any promising technology, AI has its weaknesses and limitations. Below, we overview the key limitations to consider when developing an AI-based learning management system.

Ethical dilemmas of using AI for education. The very idea of substituting human tutors with AI raises many concerns because of biases built in the algorithms. The key ethical issues are:

• Risk of discrimination. AI developers train their algorithms using data from a large group of people. However, if trained on a poorly balanced dataset, an AI model may overlook the needs of people with different educational and cultural backgrounds and various disabilities.
• Poor adjustment to cultural specifics. Adjusting training courses to learners’ cultural and national backgrounds is a big part of the activities for any organization, especially an international one. Yet an AI has a hard time taking into account such differences if it wasn’t specifically trained to do so.
• Total surveillance over learners. To create personalized training sessions, AI has to analyze data on each learner’s activity in the LMS. Some end users can be suspicious of close monitoring and constant data collection, so they need to be assured the LMS has strong cybersecurity and data anonymization mechanisms.

Inability to teach creative and cognitive skills. As Will Smith’s character from I Am Legend says, a robot can’t write a symphony or create a masterpiece. Well, it can, but only by analyzing other symphonies and creating something similar. That’s why there’s no point in trying to use AI to enhance educational courses aimed at developing someone’s creative traits or teaching them how to study by themselves.

Inability to teach decision-making for complex problems. In areas with a high level of responsibility like healthcare, engineering, or manufacturing, it’s best to combine the possibilities of algorithms and human teachers. AI can analyze data and provide one or several solutions to an issue, but it’s up to a human to assess them and choose the most suitable option. And a teacher can explain how to do it much better than AI.

Lack of face-to-face communication. Digital tutors, 3D models, and virtual reality can simulate real-life communication, but it still feels artificial for any human. Face-to-face discussion usually helps learners study more efficiently [PDF]. Also, emotional connection, motivation, and praise for good work usually improve student performance. Hence, your LMS has to provide various ways for teachers and students to communicate.

Lack of unified government and industry requirements. As with many new technologies, there are very few laws and regulations on AI use for education so far. Most countries are only now working on them. For AI developers, it means they have to figure out their own way to implement AI into their software in a secure, reliable, and unbiased way.

Conclusion

AI can help organizations take their educational experiences to the next level. With relevant technologies and development approaches, you can make AI perform management tasks, rework and adapt content, help students, and more. Most importantly, building an AI-based LMS frees time that people waste on simple routine activities and allows them to focus on challenging issues.

Choosing relevant technologies and development approaches helps you reduce the project cost, release your software faster, and make sure that it fits the needs of your customers. That’s why it’s worth getting an expert AI development team like ours to create an intelligent LMS.

Reach out for a personal consultation on how to develop your AI-based learning platform!