Techniques for using artificial intelligence to foster active learning

7. Artificial intelligence support for self-regulated learning and metacognition

Metacognition involves consciously monitoring and managing one’s own learning processes, including planning learning approaches, assessing understanding, and adjusting strategies according to progress. It is the ability to reflect on one’s own thinking and learning, which enables students to take greater autonomy and responsibility for achieving educational goals.

Self-regulated learning is the ability of students to organise and control their own learning process. It is a key competency in higher education because it develops academic autonomy and encourages students to take responsibility for ongoing learning and achieving quality outcomes.

It is important to distinguish this approach from adaptive learning. In adaptive learning, artificial intelligence automatically adjusts content and tasks to the student’s abilities, while in self-regulated learning, the emphasis is on the student managing their own learning. Artificial intelligence serves as support, helping the student plan, monitor, and reflect on learning, but does not assume responsibility for learning.

Artificial intelligence-based systems collect data on student progress and provide personalised recommendations that can improve learning strategies and support decision-making about next steps. This encourages students to actively engage in the learning process through self-assessment, reflection, and the development of metacognitive skills.

Example of an artificial intelligence tool:

Cerego uses artificial intelligence to monitor content understanding and recommend the optimal time for review. Through visual displays of progress, students gain insight into their strengths and challenging concepts and adjust their learning strategies. The basic version is available free of charge with limited functionality, while a license is required for institutional use and advanced features.

The teacher uploads content into the system, connects it to learning outcomes, and tracks student progress data, thus maintaining control over pedagogical processes.

Pedagogical value of the Cerego tool:

• Encourages reflection and responsibility for learning

• Strengthens long-term memory by applying distributed repetition based on learning analytics

• Motivates students with clear displays of progress and achieved goals

Example of application in a higher education environment:

In a research methodology course, students monitor their mastery of basic concepts, including sample, variable, and hypothesis, in the Cerego platform. The system analyses their responses and identifies concepts that require additional practice. If a student repeatedly misunderstands a concept, Cerego offers customised short activities, illustrations, and explanations, and suggests the optimal time for review, in accordance with principles of cognitive psychology and memory theory.

During the seminar, students can jointly analyse their learning patterns, compare strategies, and discuss the progress they notice after using the tool. At the same time, the teacher receives clear analytical reports indicating challenging content for the entire group, allowing timely adjustment of teaching activities. Such integration of technology and pedagogical reflection encourages active learning, argumentation, and systematic self-assessment, ultimately contributing to the development of lasting academic competencies.