AIP applications in Elearning: new opportunities

Potential AI applications in education and elearning

Currently, we actively use AI in Elearning, but above all to develop educational content: create / refine the scripts, generate images, check the texts for errors, design exercises, etc. However, the main potential of AI in learning lies in its integration into educational material. In this article, we will talk about examples of AI integration application. What is very important is that almost all the examples of this list can be implemented by an internal elearning team. Advanced programming skills and massive budgets are not necessary.

6 AI applications in learning

1. Check the open answers

One of the basic applications of AI in learning is the verification of answers to open questions, text analysis and the evaluation of case studies. Take an intelligent lesson as an example (which, let's be honest, everyone has probably developed a few dozen times). Currently, we assess knowledge through quizs. Here are four tasks formulated – who aligns with Smart? Or: Here is a given task description – What is the problem? You may have corresponding tasks or drag and drop, but in the end, it is still only a quiz with different mechanisms.

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Example 1

Here is an example of a typical intelligent exercise:

The user is asked a question, for example: “Check the objectives adjustment for compliance with intelligent criteria and determine which criteria is missing: increase the level of customer satisfaction with the support service in 2025.” Then several response options are provided:

A. Specificity

B. Mesurability

C. REMAIBLIBILITY

D. Limited in time

Using AI, we can transform a quiz into an exercise that not only tests knowledge, but also helps learners understand the material and develop skills.

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Example 2

Here is an example of an exercise in fixing intelligent objectives with AI:

The user receives the task: “You are a manager and attend a conference on July 5 to give a speech. You must give a task to the designer, Alex, to create a presentation on the subject” Intelligent learning “. You already have the script.” Then the user enters his response in free form, explaining how he would affect the task, for example:

“Alex, present a presentation on the subject” Intelligent learning. “” Then, the AI provides comments on this answer, highlighting all the advantages and disadvantages. The manager enters a text field how to settle the task. Then the AI highlights the errors. At the same time, we can personalize how AI reacts. Do you want AI to just provide the right answer and highlight the errors? No problem. Do you want AI to ask guidelines so that the employee correctly reformulates the task? Easy. Do you want the employee to get a new practice scenario every time? AI can generate a new case on the fly.

A similar exercise can be done to examine case studies – even those who do not have clear “good” answers, where we are more interested in the reflection process and the reasoning of the employee.

By the way, the marketing time for this specific exercise? 10 minutes. This includes 3 minutes to download the bot and 3 additional minutes spent picking an avatar.

2. Management courses in answers

I often use AI for that myself. If I read something and I don't understand it completely, I will “question” AI to get a clearer explanation. For general subjects (such as intelligent objectives), Chatgpt works very well – it manages them perfectly. But for the content of the internal company (policies, regulations) or niche subjects (AML / CFT, specialized software), Chatgpt is short. He simply does not know the answers to our specific questions.

So why integrate AI into a course when you can just ask Chatgpt?

1. Chatgpt does not know internal policies
   But, an AI linked to the course – because we preload it with the course equipment, and he bases his answers on this subject.
2. Transparent integration
   Having this functionality built directly in the course is incredibly practical.

How employees are currently looking for clarifications with AI

If an employee does not understand something in a business class, here is what he should do:

1. Take their smartphone (because work computers usually block Chatppt and other AI tools).
2. Open the AI platform.
3. Type their question (which can sometimes be long) on a small screen.
4. Read the answer on this same small screen.
5. If necessary, ask follow -up questions.

And this assumes that they are already comfortable using AI. If they are not regular users? They will not care. Even if they are, the process is too heavy. The more steps we need, the less people complete the action. But we want employees to independently clarify misunderstandings – if simplifying this path considerably increases commitment.

In addition, AI can make more than the answer:

• Guide learners to relevant course sections.
• Store and analyze data.

By incorporating AI directly into the course, we collect and analyze user questions. This shows exactly what is not clear, allowing us to refine future iterations.

3. Conversation simulators

Three years ago, I built a MVP for a sales simulator of mobile services. You speak aloud with an AI playing the role of the customer. The dialogue is non -linear – you direct the conversation, and in the end, you get comments. The results were fascinating: the simulator really improves sales skills because AI behaves like a real customer.

4. Specialized narrow tasks

AI is not limited to the verification of the text or the voice (as in the example of the dialogue simulator) – it can analyze anything:

• Precision of Excel reports.
• Code quality.
• Public speaking recordings.
• Facial expressions and voice.

Technically, this is more complex than previous examples (although Excel reports are relatively simple), but it is quite possible. The tools for this already exist.

5. Knowledge basic search fueled by AI

Each large company has a massive knowledge base filled with essential information: files, forms, documentation. The only drawback? Try to find anything in there. Even when you locate the right document, you often need to browse pages to get your answer.

The AI can solve this: it finds the correct files and extracts the precise answer to a request. This is technically difficult, the implementation would probably require computer support and a large budget.

6. Mentors ai

These are robots who know everything that an employee may need for his work. They help with tasks and learning – by continuously pushing employees rather than waiting passively questions.

It sounds incredible, but right now, it looks like a utopia. For what?

1. The system would be too complex, with most of the processes independent of our will.
2. Development and updates would require massive resources (daily adjustments, because the system “knows everything”).
3. The risk of malfunction is too high.

Conclusion

The AI unlocks entirely new possibilities for Elearning – applications far beyond the generation of content. The key? Don't wait for the “perfect” moment. Start experimenting now. As an example, the intelligent exercise has shown, some solutions only take 10 minutes to implement. The sooner you start, the faster you will see the results. Technology is already there. It only remains to use it.

Publisher's note: the views expressed in this article reflect the author's personal opinion and are not supposed to be representative of Eli's points of view. Consult our list of Top LMS Platforms with the best AI tools for training and educationIf you want to know more about the integration of AI.