Education: Using AI to Personalise Learning Methods

Our algorithms are very similar to those of online recommendation services, which suggest books or films based on your personal interests.

To what extent can AI power this capacity to adapt?

The Holy Grail would be the ability to use AI to generate all training content. For example, a student might tell the tool that they wanted to learn the 2,136 Japanese logograms in everyday use (jōyō kanji) and the system would create made-to-measure content and exercises for them, according to their initial level, their objectives and the time they have set themselves to accomplish this task. 

Today, our algorithms draw on well-stocked libraries of pre-existing resources. Customisation is less advanced, but it is simpler to develop and evaluate.

What are the main difficulties in designing these pathways?

We need to strike a balance in learning to ensure that it is neither too easy – boring for the student, nor too difficult – discouraging for the student, and we are struggling to obtain access to “real” students for full-scale experiments, because there is an ethical issue involved in carrying out randomised controlled tests: are students who use our tools more likely to get good marks or pass their exams?

As with any optimisation problem, the learner’s objective must also be clearly defined: for example, “cramming” for a specific competitive examination, or identifying gaps in a minimum number of questions in order to expand their knowledge.

Finally, knowledge is a latent, or hidden, variable. Even if a student passes a test, we cannot be sure of their level of proficiency in a subject.

How do focus your research in order to improve these tools?

To design a learning tool, we first need to formalise the knowledge to be acquired and its prerequisites. The pioneers in this field created “knowledge graphs” in which the different concepts are represented in nodes linked by edges that symbolise prerequisite links, for example between addition and multiplication in mathematics. We had to develop such a graph for each new subject, and then define how the tool would assess the student’s knowledge.

We want to replace this laborious framework with generic models that can be applied to several subjects. At the NeurIPS 2024workshop, we described activities that use knowledge representation to determine which document to submit to a learner in order to optimise their knowledge, without using an explicit knowledge graph. 

We mainly design learner simulators, based on the results of real students in different exercises, and teacher simulators that identify the most effective strategies for personalising content.

With these AI-based tools of the future, what role will teachers play in years to come?

The COVID-19 period showed that they still have an essential role to play in training and guiding pupils, in motivating them, instilling class dynamics, reducing inequalities and so on. Personally, I see AI as a tool that will save them time and help them perfect their art.