How can AI help create personalised learning paths for learners?

Following on from my blog last week on how AI can help with eLearning. I wanted to zoom in on personalised learning paths and in particular “How can AI help create personalised learning paths for learners?”

91% of employees say they want their training to be personalized and relevant.

The science behind it is to simply leverage data analysis, machine learning algorithms, and user interactions to create your personalised learning path.

Now, leveraging data analysis & machine learning algorithims might sound like a foreign language to some people. So I have tried to explain it without the industry jargon!

It is also important to maintain a balance between automation and human guidance to ensure that learners receive the best of both worlds – AI tailored content coupled with the benefits of human interaction and expertise.

AI and human intervention image

Step 1: Historical Data Analysis:

AI can gather data about how proficient you are in the subject matter of interest. This data can come from analysing your previous assessments and quiz results and other performance metrics such as evidence-based checklists.

It can also collect information about your learning preferences, such as preferred learning styles, content formats (text, video, interactive simulations), and topics you have browsed and shown interest in.

It will even analyse external data like your preferred learning times, based on when you access your learning during the day. This data is then analysed to identify your patterns, strengths, weaknesses, and learning preferences.

Based on your learner profile and historical data analysis, AI will recommend specific learning materials, courses, and resources that align with your learning goals and preferences. This could include suggesting introductory or advanced content, as well as related topics to explore.

Step 2: Learner Profiling:

L&D professionals should create a detailed learner profile that incorporates the proficiency level and preferences of the learner.

Step 3: A large Content Library:

L&D professionals can build a large and diverse content library that includes various learning materials, exercises, and quizzes. This large library should cover a wide range of topics and difficulty levels.

Step 4: Content Tagging and Metadata:

L&D professionals should tag each piece of content with metadata that includes information about its topic, difficulty level, format, and learning objectives. This metadata is crucial for AI to make informed recommendations.


Step 5: Recommendation Engine:

Develop a recommendation engine that utilizes machine learning algorithms, such as collaborative filtering or content-based filtering, to suggest personalized learning materials. The recommendation engine considers the learner’s proficiency level and preferences, as well as the metadata associated with the content, to make recommendations.

Step 6: Content Generation:

Utilise natural language generation (NLG) techniques to create personalised learning materials and exercises.

For instance, if the learner is a beginner in leadership & management and prefers video content. AI can generate video scripts with explanations and examples tailored to a beginner’s level.

Step 7: Adaptive Assessments:

AI can be used to administer assessments that adapt in real time based on your responses. If you answer a question correctly, the next question might be more challenging. Conversely, if you struggle, the AI might present a simpler question. So, you have the right balance between being appropriately challenged and engaged.

Step 8: Continuous Monitoring and Feedback:

Implement a feedback loop where the learner provides feedback on the generated materials and exercises. This feedback can be used to refine future recommendations and content generation.

Step 9: Regular Updates:

As the learner progresses and their proficiency level improves, the AI system should adjust the difficulty of recommended materials and exercises accordingly.

By following these steps and continuously refining the AI algorithms based on user feedback and performance data. Personalised learning materials, exercises, and quizzes can be tailored to meet the specific needs and preferences of each learner. Thereby enhancing their overall learning experience to create personalised learning paths.

Are you a Learning & Development professional looking to personalise training with engaging content, giving peresonalised feedback to learners and taking advantage of smart analytics? 


Here at eLamb, our specialism is helping companies utilise online learning software to train their team or customer base effectively.

Read how we did this for BMW.

Get in touch to learn how you can accelerate your development of  eLearning and employee training.

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