Techniques for using artificial intelligence to personalise learning for individuals

2. Pedagogical aspect: the fundamental concept of the teaching strategy for applying artificial intelligence in education

The application of AI systems and tools in teaching should not be an end in itself, but must be pedagogically considered and aligned with the goals, methods, and techniques of teaching, as well as the individual characteristics of students.

In a didactic sense, the teaching strategy represents the broadest plan and direction of the educational process. It determines how the general goals of education will be achieved and guides the selection of pedagogical methods and techniques. In other words, the strategy defines a general approach to learning and teaching, while the methods and techniques specify the ways it is implemented.

In the context of an AI application, the teaching strategy forms the general framework for its integration. For example, AI may be used to encourage self-regulated learning, develop ethical awareness, personalise instruction, strengthen collaborative skills, or improve teaching efficiency. In this context, AI is not a substitute for the teacher but a pedagogical tool that enables a deeper understanding of the learning process and its adaptation to the individual needs of students.

To understand how AI can contribute to education, it is useful to consider its development within different theoretical approaches to learning. According to Ouyang and Jiaou (2021) and Yalcinalp et al. (2024), the evolution of AI applications in education can be viewed through three paradigms.

The first paradigm is based on a behavioural approach in which AI acts as an instructor that guides the learning process, assigns tasks, and evaluates answers. In this framework, the student is the recipient of knowledge, and technology serves as a mediator that encourages repetition, practice, and automated evaluation.

The second paradigm stems from cognitive and constructivist learning theories. AI becomes a student's support through dialogue, interaction, and feedback. The student actively participates in constructing knowledge, and AI acts as a collaborator that facilitates understanding, reflection, and co-creation of meaning.

The third paradigm is associated with connectionism and complex-adaptive approaches to learning. In this framework, the student takes on a leading and reflective role in the learning process, while AI acts as a partner that enables personalised, self-regulated, and data-driven learning. This paradigm emphasises flexibility, autonomy, and the continual adaptation of the learning environment to the individual needs and interests of students.

Examples of teaching strategies supported by artificial intelligence:

• Personalised learning strategy supported by artificial intelligence 
  
  • Goal: Adapt the learning process to the individual needs, prior knowledge, and pace of each student. 
    
  • Application of artificial intelligence: Adaptive systems and learning analytics automatically adjust content and activities to each individual (e.g., Khan Academy, Smart Sparrow).
  • Pedagogical value: Enables self-regulated learning and provides teachers with insight into each student's progress. 
  • Examples from teaching practice: In working with students, adaptive systems have been shown to help those with different learning paces by tailoring tasks to their needs. Unlike approaches where everyone solves the same task, adaptive systems allow students to work on tasks of varying difficulty and type, while teachers maintain insight into their progress through learning analytics reports.
• Problem-based learning strategy
  • Goal: Encourage critical thinking and the application of knowledge through solving real-world problems. 
  • Application of artificial intelligence: Tools for generating problem scenarios and analysing the solution process (e.g., ChatGPT). 
  • Pedagogical value: Students actively research and justify solutions, while artificial intelligence provides support and feedback on their progress. 
  • Examples from teaching practice: The ChatGPT tool can quickly generate multiple versions of the same problem scenario (with different contexts, complexity levels, and perspectives), significantly reducing preparation time compared to manual task creation. Unlike traditional, pre-assigned tasks with a single fixed scenario, this approach allows students to actively question the situation, seek clarification, explore alternative solutions, and critically evaluate answers, fostering a deeper understanding of the material. 
• Inquiry learning strategy
  • Goal: Develop the ability to ask questions, plan, and conduct research. 
  • Application of artificial intelligence: Tools for searching and analysing scientific sources (e.g., Elicit) and tools such as ChatGPT for generating tabular summaries, suggesting graphical representations, and interpreting results. 
  • Pedagogical value: Students develop research competencies and critical thinking with technological support. 
  • Examples from teaching practice: Elicit helps students find relevant papers and abstracts more quickly during the literature review phase, while ChatGPT is used for initial suggestions on analysis structure and possible data visualisations. Compared to manual database searches, this approach shortens the technical part of the work and leaves more time for critical reading and discussion of findings with the teacher.
• Collaborative learning strategy
  • Goal: Encourage collaboration and joint knowledge construction. 
  • Application of artificial intelligence: Tools that facilitate teamwork, moderate communication, and provide feedback on team members' contributions (e.g., Google Workspace AI, Microsoft Copilot). 
  • Pedagogical value: Develops collaborative and communication skills and promotes shared responsibility in learning. 
  • Examples from teaching practice: In team assignments, tools like Copilot are used to suggest the structure of shared documents, summarise discussion flow, and monitor team members' contributions. Compared to the traditional group work without digital support, this approach makes it easier for teachers to monitor the process (who did what) and gives students a clearer view of their own and their colleagues' contributions.
• Reflective and metacognitive learning strategy
  • Goal: Encourage students to reflect on their own learning process and self-regulate. 
  • Application of artificial intelligence: Tools for monitoring progress and generating personalised feedback (e.g., Notion AI). 
  • Pedagogical value: Artificial intelligence helps students recognise their learning patterns, plan progress, and strengthen accountability for results. 
  • Examples from teaching practice: Notion AI supports students in keeping reflective journals by helping them summarise notes and highlight key points. Students can then, together with the teacher, analyse how well these summaries match their own learning experiences. Compared to traditional reflective journals, this approach makes it easier for students to notice patterns (e.g., when their learning is most effective) and plan the next steps in their learning process.
• Experiential and simulation learning strategy
  • Goal: Enable students to acquire practical knowledge and skills through experience and simulations. 
  • Application of artificial intelligence: Tools for generating simulations and scenarios (e.g., ChatGPT, which can generate various scenarios, roles, and possible outcomes of decisions such as clinical cases, classroom situations, or business problems), and tools for creating videos with virtual avatars (e.g., Synthesia). 
  • Pedagogical value: Students learn through authentic situations, reflection, and decision-making in a safe digital environment. 
  • Examples from teaching practice: Using ChatGPT to create simulation scenarios with different possible outcomes based on the student's choices (what happens if the student selects one solution or another) allows students to analyse the consequences of different decisions in a safe environment. The Synthesia tool is used to quickly create short video scenarios with virtual characters, eliminating the need for live recording. Compared to traditional text-based tasks, simulations powered by AI tools allow students to gain a deeper experiential understanding of situations and practice decision-making with less risk of errors. 

All these educational strategies represent didactically grounded approaches to learning and teaching in which AI plays a supportive rather than a replacement role. 
Its value lies in its ability to personalise the learning experience, facilitate reflection, enhance collaboration, and provide educational stakeholders with insights into educational processes that could not previously be accurately monitored.