Techniques for using artificial intelligence to foster active learning

Adaptive learning refers to the dynamic adjustment of teaching activities to match an individual student's level of understanding, pace of progress, and specific needs.

Artificial intelligence-based systems analyse student responses, time spent solving tasks, and level of independence. Using this data, they provide personalised recommendations, additional challenges, or targeted support.

This approach promotes active learning by giving students immediate feedback, allowing them to make independent decisions about their next steps, and encouraging them to take responsibility for their own progress. Instead of assigning the same task to everyone, each student enters a zone of optimal challenge, which increases engagement and motivation.

The teacher's role remains essential: the teacher monitors progress, analyses data, encourages reflection, and, when necessary, adjusts teaching activities to ensure balanced development of competencies across the entire group.

Example of an artificial intelligence system:

ALEKS (Assessment and Learning in Knowledge Spaces) is an adaptive system frequently used in higher education, especially in mathematics and STEM fields. The system assesses understanding through an initial test and then assigns personalised tasks based on the student's results. However, expert review of the content is necessary to ensure alignment with the curriculum. Access to the system may require an institutional license or a student fee.

Example of application in higher education:

In an applied mathematics course, students begin using the ALEKS tool with an initial knowledge assessment. Based on the results, each student receives a personalised work plan and tasks suited to their level of understanding. During seminars, students exchange solution strategies and explain procedures in small groups, while the teacher uses the tool's analytical reports to moderate discussion, identify challenges, and encourage deeper analysis of the results.

In this way, artificial intelligence supports active learning through argumentation, collaboration, and reflection.