Techniques for using artificial intelligence to personalise learning for individuals

4. Pedagogical aspect: the basic concept of the teaching method for the application of AI in education

A pedagogical method refers to a planned and systematic approach to teaching and learning that results from a selected teaching strategy.

Unlike a teaching strategy, which determines a general approach to learning, a teaching method demonstrates how this approach is implemented in practice – through specific activities, interactions, and forms of work.

Within the framework of an active learning approach, where students are actively involved in creating and applying knowledge, methods such as problem-based, project-based, research-based, collaborative, heuristic, and reflective learning, as well as Socratic dialogue, learning through games, simulation, experiential, and discovery learning, are most often used. These methods encourage deeper understanding, critical thinking, and the development of self-regulation in learning.

The application of artificial intelligence in higher education enables the enhancement of existing pedagogical methods and the development of new forms of learning.

Artificial intelligence does not change the method itself but strengthens its application – making it more flexible, efficient, and better adapted to individual needs. Tools based on artificial intelligence contribute to the personalisation of learning, monitoring of progress, analysis of the learning process, and timely provision of feedback.

In this way, artificial intelligence becomes a pedagogical resource that allows teachers to gain a deeper understanding of the learning process and provides students with greater autonomy and support in achieving their goals.

Rather than changing the teaching method, artificial intelligence complements and improves it, enabling teachers to make informed pedagogical decisions and making learning more effective, meaningful, and reflective.

How does AI complement pedagogical methods?

1. Learning Analytics
   AI collects and analyses data about student activity, such as time spent learning, number of attempts, success rates, and methods used to solve tasks. Based on this data, it identifies learning patterns and generates visual representations of progress or recommendations for teachers about which content requires further clarification. This gives teachers insight into learning processes that would otherwise remain invisible, while students receive personalised guidance to support their progress.
2. Adaptive Learning 
   Adaptive AI systems continuously analyse student results and behaviour, automatically adjusting the level of difficulty, content order, and pace of work. Each student learns according to their own abilities, and the system dynamically suggests additional tasks, examples, or explanations when it detects difficulty in understanding. This approach enables personalised and flexible learning that evolves in real time.
3. Automated Feedback 
   AI-based tools analyse student texts, presentations, or code and generate feedback on accuracy, structure, argumentation, or style. Students receive immediate, constructive guidance for improvement, which encourages self-regulated and reflective learning, while teachers can focus their time on deeper analysis and mentoring.
4. Simulations and Experiential Learning 
   AI enables the creation of authentic digital simulations and scenarios in which students can experiment, make decisions, and observe the consequences without real risk. This develops students’ ability to apply theoretical knowledge in real-world contexts and reflect on their decisions.
5. Support for Collaborative Learning 
   AI tools and systems facilitate communication and teamwork by analysing group members’ contributions and suggesting ways to distribute tasks more evenly. AI can identify key topics in group discussions, summarise shared conclusions, and suggest next steps in a project. In this way, AI strengthens coordination, accountability, and transparency in collaborative learning.
6. Encouraging Reflection and Metacognition 
   Virtual assistants can ask reflective questions, encourage students to describe their learning strategies, and help with self-assessment of achievements. AI thus acts as a dialogue partner (virtual assistant) that encourages reflection on the learning process, not just its outcomes. This contributes to the development of metacognitive skills, planning, and more conscious management of one’s own learning.

Artificial intelligence complements pedagogical methods by automating technical and analytical processes, freeing teachers to focus on aspects of teaching that require human judgment, empathy, and pedagogical creativity.

In this way, artificial intelligence does not replace the teacher but enables a deeper understanding of the learning process and provides students with continuous support, personalisation, and opportunities for reflection.