Techniques for using artificial intelligence to personalise learning for individuals

1. Introduction

Education at all levels is increasingly moving toward differentiation and personalisation of learning. This approach creates more flexible and meaningful educational experiences that better recognise each student's individuality.

In this process, artificial intelligence (AI) is playing an increasingly important role. It enables the collection and analysis of data on student progress, interests, and difficulties and, based on these insights, generates personalised content and feedback.

Since technology is not an end in itself, its true value is realised only when integrated with pedagogically thoughtful approaches and methods that provide students with meaningful, active, and motivating learning experiences. For example, AI can foster reflective learning by automatically generating self-assessment questions, support collaborative processes by analysing contributions within team activities, or facilitate the monitoring of student progress through dynamic reports and data visualisations.

Today, a wide range of AI tools and systems are available on the market. Their successful application in higher education depends largely on teachers' ability to recognise the pedagogical potential of each AI tool and system and to understand its capabilities and limitations.

The following will present specific techniques for applying AI in education and examples of AI tools that support them. Special attention is given to ways these techniques can be connected to pedagogical approaches, encouraging diversity within the group and deeper individualisation of each student's educational experience.

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.

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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.

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6. Pedagogical aspect: basic concept of pedagogical techniques for applying artificial intelligence in education

Pedagogical techniques are concrete procedures, activities, and tools used to achieve the goals defined by teaching strategies and pedagogical methods in practice. While a teaching strategy determines the direction and method of teaching and learning, a pedagogical technique demonstrates how this method is implemented through specific steps, activities, and didactic approaches at the operational level in the educational process.

In the context of artificial intelligence in education, pedagogical techniques include the use of AI tools and systems that enable a deeper understanding of the learning process and its adaptation to individual needs.

These tools can support content personalisation, analysis of student activities, automated progress monitoring, and the provision of real-time feedback.

As previously emphasised, the value of integrating artificial intelligence into the educational process does not stem from the technology itself but from a pedagogically thoughtful application aligned with goals, learning outcomes, and teaching strategies and methods.

Therefore, the application of any technology, including artificial intelligence, in education requires consideration of its purpose and pedagogical justification.

Before introducing a specific AI tool or system, it is necessary to:

1. Assess the pedagogical potential of AI tools and systems – understand how they can support the achievement of educational goals, develop competencies, and improve the learning process.
2. Analyse examples of good practice in the application of selected AI tools and systems in education, ensuring their use is based on proven experiences and aligned with pedagogical principles and the needs of a specific teaching context.

This approach ensures that the application of AI technology is pedagogically grounded and that the professional and ethical responsibility of teachers in the teaching process is maintained.

AI-based pedagogical techniques can significantly enrich teaching practice, especially in areas related to the personalisation of learning and support for student self-regulation.

By using AI systems that monitor student activities, analyse learning patterns, and offer recommendations, it is possible to adapt content and learning pace to individual students more precisely. In addition, artificial intelligence enables the development of techniques that encourage reflection, collaboration, and formative assessment, making the teaching process more dynamic and student-centred.

Below are examples of specific pedagogical techniques that use artificial intelligence tools and systems to support the achievement of the goals of individual learning methods.

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8. Examples of pedagogical techniques using artificial intelligence

Examples of selected pedagogical techniques in education demonstrate specific ways in which AI-based tools and systems can foster active, reflective, and personalised learning. For each tool, the pedagogical potential and key advantages over other options are listed, highlighting its relevance in the educational context.

Note: The selected examples include free or partially free AI tools and systems available for use in educational settings at the time of writing this course. Given the rapid changes in these technologies, it should be noted that their functionalities and availability may change over time, and free versions often have certain limitations regarding the number of uses or access to advanced features.

Guided Socratic Dialogue Using AI

Technique Description:

This technique uses AI to promote critical thinking, argumentation, and reflection through structured dialogue. The system asks thought-provoking questions, guides the discussion, and encourages students to think more deeply about a particular problem or topic. The teacher acts as a moderator who guides and evaluates the argumentative flow of the discussion.

Application method:

As part of seminar teaching in the course Ethics in a Digital Environment, students participate in a dialogue with a virtual assistant who poses challenging questions about the ethical issues of artificial intelligence. After the dialogue, students analyse their positions in smaller groups, compare them with the responses of the AI system, and formulate conclusions jointly.
This approach enables the development of critical thinking and argumentation skills through interactive, guided inquiry.

Pedagogical value:
By using a virtual assistant in a guided dialogue, students develop skills in argumentation, reflection, and ethical reasoning. The system functions as a virtual interlocutor that stimulates thought processes and a deeper understanding of the content. The activity also supports the development of self-regulated learning and empowers students to express and defend their positions in an academic context.

Example of an artificial intelligence tool:

• ChatGPT, with its language model, enables dialogue that adapts to the level and interests of students, imitating the logic of the Socratic approach to questioning and argumentation. The system asks questions of varying complexity, responds to students' answers, and encourages further reflection, making it a highly suitable tool for developing critical thinking and reflective dialogue.
  Unlike traditional digital questionnaires or forums, ChatGPT enables interactive, dynamic, and personalised communication, in which the student is an active participant in the thought process rather than a passive recipient of information.
  Due to its accessibility, flexibility, and linguistic adaptability, ChatGPT is a relevant tool for implementing pedagogical techniques that emphasise dialogue, reflection, and argumentation, especially in higher education.

Adaptive learning based on data analysis

Technique description:

Artificial intelligence analyses data on student learning and automatically adjusts the content, pace, and complexity of the material to meet individual needs. This approach enables personalised learning, providing each student with content that matches their prior knowledge, pace, and learning style, without additional burden on the teacher.
Adaptability is achieved through continuous analysis of student interactions and results, allowing the system to dynamically adjust tasks, difficulty levels, and recommendations for further learning. This leads to more efficient and meaningful learning, with technology actively supporting pedagogical goals.

How to use it:

In education, adaptive learning can be applied in subjects with large numbers of students or those that require gradual acquisition of concepts, such as mathematics, programming, or statistics. The system collects data on task completion, response time, and accuracy, and then uses this data to adjust content and suggest additional activities. The teacher can monitor each student's progress and direct further activities according to their needs.

Pedagogical value:

Implementing adaptive learning helps students develop independence, metacognitive skills, and responsibility for their own progress. Artificial intelligence assists teachers in recognising learning patterns and individual student needs, enabling targeted monitoring and support, and providing students with a personalised learning experience.

Examples of artificial intelligence tools and systems:

• Century Tech — an adaptive learning system that effectively combines learning analytics and pedagogical design. The tool allows real-time monitoring of student progress and automatically adapts content to individual needs, supporting personalised and self-regulated learning.
• DreamBox Learning — an adaptive learning platform focused on mathematics.
• SchoolAI – adapts content and activities according to user progress and provides real-time feedback. The platform combines adaptive learning and interactive communication, making it suitable for higher education. It is available in a free version with basic functionalities, with limitations on the number of users and activities.

Automatic Generation of Quizzes and Knowledge Testing 

Technique description:

Artificial intelligence enables instructors to quickly create personalised quizzes, tests, and knowledge assessment tasks, adjusting content and difficulty to each student's level.

Examples of AI tools and systems:

• Curipod — a tool for creating quizzes and interactive tasks,  
• Quizizz — a platform for creating and managing online quizzes,
• ChatGPT — can generate questions and tasks by topic.

Writing and Feedback Using AI 

Technique description:

Artificial intelligence assists students in writing term papers and research papers by automatically detecting grammar, style, clarity, and plagiarism issues, supporting the development of academic and communication skills.

Examples of AI tools and systems:

• Grammarly — a tool for correcting and improving text,  
• Notion AI — assists in structuring and writing content,
• Copyleaks — a plagiarism detection system.

Summarising and Analysing Scientific Articles 

Technique description:

These tools use artificial intelligence to quickly summarise complex scientific texts and extract key information, significantly facilitating preparation for teaching and research.

Examples of artificial intelligence tools and systems:

• Perplexity AI — a tool for generating summaries and answering questions about texts,
• Elicit — a platform for systematic research and literature analysis,
• Explainpaper — a tool for understanding professional papers,
• Litmaps — for mapping and visually tracking connections among scientific papers within a specific research topic,
• SciSpace AI — for suggesting literature in the field.

Creating Interactive Teaching Materials 

Technique description:

Artificial intelligence supports teachers in creating visually appealing presentations, interactive slides, and infographics that promote engagement and understanding among students.

Examples of artificial intelligence tools and systems:

• Canva AI — a tool for designing presentations and graphics,
• Beautiful.ai — an AI assistant for creating presentations,
• Genially — a platform for interactive content.

Simulations and Ethical Discussions with AI Avatars 

Technique Description: 

By using AI avatars and simulations, students engage in realistic scenarios and discussions about ethical challenges, which develop critical thinking and decision-making skills.

Examples of AI Tools and Systems: 

• Synthesia — a tool for creating video avatars, 
• D-ID — generates realistic avatars and video simulations, 
• ChatGPT — moderates and leads ethical discussions.

Lecture Recording and Summarisation 

Technique Description: 

AI tools automatically transcribe and summarise lectures, making it easier for students to access key information and organise material.

Examples of AI Tools and Systems: 

• Otter.ai — automatic transcription and summarisation of speech, 
• Fireflies — records and analyses meetings and lectures, 
• Notion AI — assists in organising and summarising content.

Increasing Accessibility and Inclusiveness in Teaching 

Technique Description: 

AI tools make teaching more accessible for students with special needs (e.g., dyslexia, concentration difficulties) and adapt content to different learning styles and language needs.

Examples of AI Tools and Systems: 

• Immersive Reader — facilitates reading and understanding of text, 
• Read&Write — supports learning and reading.

Artificial Intelligence-Based Simulations for Interactive Learning 

Technique Description: 

This technique uses artificial intelligence to create realistic, interactive simulations that allow students to apply theoretical knowledge in a safe, controlled environment. Simulations can include real-life scenarios, complex problems, or ethical dilemmas, with AI avatars or virtual agents conducting dialogue and responding to student decisions.

Examples of AI Tools: 

• Synthesia — AI-powered video avatar creation for simulated situations, 
• Mursion — an AI system for virtual simulations with interactive characters, 
• Unity ML-Agents — an AI tool for simulation and training development, 
• D-ID — generates realistic video avatars for simulations.

Virtual Experiments Based on AI 

Technique Description: 

This technique uses AI and computer simulations to let learners conduct experiments in a virtual environment. AI can adjust parameters, monitor experiments, and provide real-time analysis of results. This enables deep understanding of theoretical concepts, risk-free experimentation, and access to experiments that would otherwise be expensive, dangerous, or technically demanding.

Examples of AI Tools and Systems: 

• PhET Interactive Simulations — free interactive simulations for science and math, 
• Labster — virtual laboratory simulations with AI support for STEM subjects, 
• PraxiLabs — 3D virtual laboratories with customisable experiments, 
• SimuLab — tools for simulating chemical and physical experiments.

Artificial Intelligence Support in Solving Problems and Tasks 

Technique Description: 

Virtual assistants help students understand concepts and find solutions through customised explanations and examples. This support enables better understanding and independent learning.

Examples of AI Tools: 

• ChatGPT – helps with understanding theory, solving tasks, and writing papers, 
• Wolfram Alpha – a tool for calculations and analysis of mathematical and scientific problems, 
• Perplexity AI – provides concise, clearly explained answers to questions.

Artificial Intelligence-Powered Simulations and Avatars 

Technique Description: 

AI-powered avatars and simulations enable the creation of authentic dialogue situations, communication skills training, argument analysis, and critical thinking development through realistic interactions between users and virtual characters.

Examples of AI Tools: 

• Synthesia – creates videos with AI avatars that simulate conversation and discussion, 
• D-ID – creates videos in which an avatar leads a conversation, 
• ChatGPT – moderates and encourages dialogue and analysis.

Artificial Intelligence-Powered Drawing and Visualisation in Teaching 

Technique Description: 

Visualisation through drawings, diagrams, mind maps, or infographics helps students structure information, connect ideas, and better understand complex concepts. AI tools can automatically generate or assist in creating visual representations based on text or ideas, encouraging creativity and critical thinking through content organisation.

Examples of AI Tools and Systems: 

• Canva AI – enables easy creation of visual materials such as diagrams, infographics, and presentations with AI support, 
• MindMeister – supports the creation of mind maps to organise and analyse ideas, 
• DALL·E – generates images and illustrations from textual descriptions, useful for creative tasks and visualisations.

Learning programming supported by artificial intelligence

Description of the technique:

Artificial intelligence tools for learning programming help students understand code, automatically correct errors, suggest solutions, and explain concepts in a simple manner. These tools enable interactive learning through examples, exercises, and personalised tasks, encouraging independent exploration and the development of logical thinking.

Examples of artificial intelligence tools and systems:

• GitHub Copilot – an artificial intelligence assistant that helps with coding by suggesting ready-made code fragments and solutions,
• Replit – an online development environment with AI support for automatic code completion and interactive tutorials,
• CodeSandbox – a tool for creating web applications with AI suggestions and project sharing,
• LeetCode – a platform for learning programming through problem solving, with AI assistance explaining solutions.

Artificial intelligence support in statistical calculations

Description of the technique:

Artificial intelligence tools help students understand and perform statistical analysis by automating calculations, visualising data, and explaining results in an understandable way. These tools can also interpret statistical tests, suggest appropriate analysis methods, and assist in preparing reports, thus facilitating the learning and application of statistics in research.

Examples of AI tools and systems:

• IBM SPSS Statistics – statistical analysis software with AI capabilities for automatic interpretation of results,
• Jamovi – a free and open statistical program with a simple interface and AI support for analysis,
• Statwing – an AI tool for simple statistical analysis and data visualisation,
• Google Sheets AI –  a spreadsheet add-in that uses AI to analyse data and perform statistical functions.

In addition to the above, there are many other educational activities for which AI tools and systems can be used.

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10. Conclusion

The use of AI-based techniques in education enables more efficient, inclusive, and personalised teaching.

Instructors and teachers do not change their pedagogical role but instead complement it with AI technology, which allows them to focus on what is most important: relationships with students, critical discussion, and the development of higher cognitive skills.

11. Literature

• Babić, S. (2024). Examining the factors influencing students’ intention to use ChatGPT as a virtual assistant for academic learning. Presentation at the 9th International Conference on Advanced Research in Teaching and Education, Berlin, Germany. 
  
  • Description: A pilot study conducted on Croatian students analysing the factors influencing the intention to use ChatGPT as a virtual assistant for academic learning, with emphasis on usefulness, relevance and motivation.
    
    
• European Commission (2023). Ethics guidelines for trustworthy AI. 
  
  • Description: European Commission guidelines for the ethical and trustworthy use of artificial intelligence in education and other sectors.
    
    
• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. 
  
  • Description: A detailed analysis of the use of artificial intelligence in education, with emphasis on personalisation, pedagogical methods and teaching effectiveness.
    
    
• OpenAI (2025). ChatGPT's study mode is here. It won't fix education's AI problems. Wired.
  
  • Description: Critical analysis of the application of ChatGPT in education, focusing on its advantages and limitations in the personalisation of learning.
    
    
• UNESCO (2021). AI in education: Guidance for policy-makers.
  
  • Description: UNESCO guidelines for the integration of artificial intelligence into educational policies and practices.
    
    
• Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39.
  
  • Description: Overview of the research on the application of artificial intelligence in higher education, with emphasis on the role of teachers, pedagogical methods and techniques.