Techniques for using artificial intelligence to foster active learning

1. Introduction

Active learning involves approaches in which students do not remain passive observers but actively reflect, apply, discuss, and question content during the learning process. These approaches enable a deeper understanding of concepts, foster critical thinking, and increase knowledge retention. In higher education, the importance of active learning is further highlighted by the need to develop student autonomy and the skills required in professional life.

Despite substantial evidence of its effectiveness, active learning in practice often faces challenges, such as large student groups, limited time for individualised instructions, and varying levels of prior knowledge within the same class.

Artificial intelligence can help address these challenges, but it is important to emphasise that it does not replace teachers. Instead, it serves as support, enabling teachers to better plan activities and providing students with more opportunities for engaged and meaningful work.

Active learning supported by artificial intelligence refers to situations in which AI is used as a tool for designing and implementing pedagogical activities, rather than as an automated source of ready-made answers. Active learning occurs when students:

• Analyse and interpret information
• Make decisions and justify positions
• Exchange ideas with others
• Question their own mistakes
• Reflect on the learning process

Artificial intelligence supports this by increasing opportunities for activity, adjusting the level of cognitive challenge, providing timely feedback, and offering authentic learning situations in which students can actively apply knowledge.

2. Artificial intelligence techniques to encourage active learning in education

The application of artificial intelligence in education is playing an increasingly important role in ensuring an active and interactive learning process. Active learning is based on the idea that students take an active role in constructing their own knowledge, which requires deeper cognitive processing, participation in discussion, reflection, and the application of concepts in different contexts.

Artificial intelligence can enhance these processes by personalising content, automating analytics, supporting reflection, and enabling authentic learning experiences.

To realise the full potential of artificial intelligence in education, it is necessary to understand specific techniques for its application that directly support active learning and contribute to greater engagement, interaction, and deeper understanding of the content.

In this context, the following are basic artificial intelligence techniques aimed at developing active learning:

• Generating cognitively stimulating questions
• Adaptive learning tailored to student progress
• Automated real-time feedback
• Interactive simulations and decision-making scenarios
• Support for self-regulated learning and metacognition
• Learning analytics to encourage reflection and decision-making
• Educational chatbots for developing critical thinking and argumentation

Each of these techniques includes specific didactic possibilities that can contribute to increasing the quality of the educational process and encouraging a deeper understanding of educational content.

3. Generating cognitively stimulating questions

Cognitively demanding questions are an important element of pedagogical design in education, as they encourage students to process information at higher levels of thinking. Questions that require explanation, comparison, evaluation, or the formation of students' own interpretations support deeper learning and foster independence in learning. This shifts students from being passive recipients of information to active participants who constructively shape their knowledge.

Artificial intelligence-based systems can assist teachers in creating such questions by analysing teaching materials and automatically suggesting various types of questions. This streamlines activity preparation and allows teachers to focus more on pedagogical interaction and discussion with students during the teaching process.

Active learning occurs primarily through argumentation and reflection on different answers in interaction with peers and the teacher.

It is important to note that artificial intelligence does not assume the professional responsibility of teachers in forming and evaluating questions. Its role is to support the creation of materials that stimulate students' cognitive activity and contribute to achieving learning outcomes.

Example of an AI tool for creating complex questions:

Quillionz - an AI-based system that generates questions of varying complexity levels (according to Bloom's Taxonomy) based on uploaded educational content. See instructions for use: Quillionz Tool - Creating Questions Based on Text (EDUBlic, CARNET)

Important: All generated questions require professional review and didactic refinement by teachers. The basic version of the tool was available free of charge during the creation of this educational content, with a limited number of content generation and export options. This may change over time.

4. Adaptive learning tailored to student progress

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.

5. Automated real-time feedback

Feedback is one of the most powerful elements of learning and teaching in higher education, as it provides learners with insight into the quality of their understanding, reasoning, and application of knowledge. In traditional settings, learners often receive feedback only after completing the learning activity, which limits opportunities for learning while cognitive activity is still ongoing.

Automated real-time feedback enables learners to immediately recognise errors, receive guidance, and adjust their approach to solving tasks during the learning process, thus encouraging active analysis, reflection, and the development of self-assessment skills.

AI-based systems provide learners with immediate, informative, and personalised feedback during the task-solving process. This approach encourages active learning because the learner:

• Immediately notices the error and its cause,
• Questions their own thinking,
• Decides how to adjust their approach,
• Adopts educational content through iteration and real-time reflection.

The instructor acts as an expert moderator who interprets the data collected by the system and encourages discussion about errors and different problem-solving strategies.

Example of an AI tool:

Gradescope is an AI-based system widely used in higher education for grading and analysing student assignments, including essays, math problems, and computer code. The system allows for digital scanning of paperwork or uploading assignments in PDF format, and then automatically analyses and groups similar answers. It recognises correct and incorrect solution patterns and provides personalised feedback in a short time.

The quality of the feedback requires pedagogical supervision by the instructor. Implementing advanced functionalities requires an institutional subscription.

Example in higher education:

In the course “Linear Algebra”, students solve tasks involving calculating determinants and solving systems of linear equations. After submitting solutions via Gradescope, the system automatically analyses the procedures and identifies the most common mathematical errors, such as incorrectly performed elementary transformations or mistakes in calculating determinants.

Students receive immediate feedback that points to the specific step where the error occurred, along with a brief guideline encouraging them to check the logic of their procedure. After correcting their solutions, students compare their initial and improved approaches and explain what additional thinking led them to the correct solution.

The teacher uses the overview of the most common errors to initiate a discussion about different strategies for solving systems of equations and the importance of checking procedures. Such discussion fosters a deeper understanding of mathematical concepts and encourages students to reflect metacognitively on their own learning.

6. Interactive simulations and decision-making scenarios based on artificial intelligence

Interactive simulations and decision-making scenarios are a powerful form of active learning because they enable learners to gain knowledge through their own actions. Rather than passively following a presentation, learners make decisions in a digital environment, observe the consequences, and continuously adjust their problem-solving strategies. Artificial intelligence-based systems adapt the simulation’s progression based on learners’ decisions, ensuring a personalised learning experience.

This form of learning is especially valuable in higher education, as it fosters the development of professional competencies that are difficult to acquire through purely theoretical approaches, such as critical thinking, solving complex problems, making decisions in uncertain situations, and taking responsibility for the outcomes.

Interactive simulations directly link key elements of active learning with real professional environments.

Example of an AI tool in a higher education setting:

Body Interact is a simulation system that uses artificial intelligence to model clinical situations in health sciences. Students actively participate in diagnostics and therapeutic decision-making, while the virtual patient’s condition changes in real time based on their actions. The tool requires an institutional subscription for full access and advanced features, while the basic version offers a limited number of scenarios free of charge. The effectiveness of teaching activities supported by this tool depends on the teacher’s pedagogical preparation and moderation.

Pedagogical value:

• Students learn from the consequences of their own decisions, encouraging a deeper understanding of cause-and-effect relationships.
• The simulation environment allows for safe learning from mistakes.
• Reflection, argumentation, and team decision-making are encouraged.
• Knowledge is applied in authentic professional environments.

This tool enables active experiential learning that goes beyond theoretical content acquisition and directly develops the professional competencies needed in higher education in health fields. Artificial intelligence dynamically adapts the simulation to students’ actions, thereby strengthening responsibility in learning.

In an emergency medicine course, students in groups care for a virtual patient with acute respiratory distress. 

The simulation is followed by a structured discussion in which students explain their decisions and analyse different approaches. This fosters critical reflection on actions and encourages students to reflect on their own assessments.

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.

8. Artificial intelligence and learning analytics as support for active and self-regulated learning

AI-powered learning analytics refers to the systematic collection and analysis of data on student learning using artificial intelligence algorithms. These algorithms enable a deeper understanding of the learning process, identify behavioural patterns, and predict potential risks in achieving educational goals.

The results of such analysis are used to provide personalised recommendations to students and timely pedagogical interventions by teachers. As a result, students gain clearer insight into their own learning patterns, make more informed decisions about next steps, and reflect on their progress.

This approach directly encourages active learning and the development of academic autonomy, as the student becomes an active decision-maker in their own learning.

AI-powered learning analytics supports self-management of learning, but does not manage learning for the student.

Example of an AI tool:

IntelliBoard is a learning analytics system that can be integrated with Moodle and other higher education learning management systems. It uses elements of artificial intelligence, particularly machine learning methods, to analyse student engagement and performance and identify patterns that indicate the need for additional support. It provides students and instructors with visual progress reports and recommendations for improving the learning process. More advanced analytical functionalities are available in the commercial version, and an institutional license is required for full use.

Example in a higher education environment:

In the course “Introduction to Programming”, students regularly solve short problem tasks in a digital learning management system (LMS) that uses artificial intelligence to analyse data about their activities. The system monitors the frequency of access to materials, the level of success in quizzes, and the timeliness of assignment submissions, and shows students visual indicators of progress in relation to learning outcomes.

If the system notices that an individual student is lagging in a certain type of task or shows a decline in engagement, it provides a personalised recommendation, such as:

• Review of additional materials
• Targeted exercises
• Reminder of a deadline or activity in which they did not participate

Based on these insights, the student plans further activities, adjusts their learning strategy, and reflects on progress over time. The teacher uses aggregated data to initiate discussions about effective learning methods and to provide timely support to students who need additional help.

This approach encourages active learning because students rely on information about their own progress, make informed decisions, and take responsibility for the quality of their learning.

The use of AI-powered learning analytics contributes to active learning because students actively monitor their progress, adjust learning strategies, and develop responsibility for achieving learning outcomes. At the same time, the teacher uses analytical insights to direct support to students, ensuring timely and effective adjustments in the teaching process.

9. Educational chatbots for the development of critical thinking and argumentation

Educational chatbots or virtual assistants based on artificial intelligence enable learners to engage in interactive dialogue about learning content. During communication, the chatbot asks questions, encourages argumentation, and offers counterexamples or additional explanations. Learners are not passive recipients of information; they are expected to analyse information, formulate positions, assess the logic of arguments, and draw informed conclusions.

This type of activity is directly related to active learning because it requires cognitive engagement, critical thinking, reflection, and taking responsibility for one's own understanding of the content. In this context, artificial intelligence does not provide ready-made answers but encourages a structured thought process.

Educational chatbots can:

• Ask challenging and reflective questions
• Encourage comparison of different interpretations
• Prompt learners to defend their arguments
• Offer different perspectives and challenge assumptions
• Increase learner involvement and motivation

Chatbots (virtual assistants) do not replace teachers but expand the possibilities of guided dialogue in higher education.

Example of an artificial intelligence tool:

ChatGPT can be used as a cognitive assistant in learning to encourage analytical thinking, discussion, and reflection. By posing open-ended and challenging questions, it guides learners toward a deeper understanding of the topic.

Limitations:

• Requires clear instructions and supervision from the instructor to avoid imprecise or unfounded interpretations.
  The instructor must verify the credibility of the information.
  The instructor ultimately formulates the questions and criteria for discussion.

Example of application in higher education:

In a course on the ethics of artificial intelligence, students have a structured dialogue with a chatbot about the challenges of applying artificial intelligence systems in medical diagnostics. Each student must:

1. State an initial position.
2. Defend it against opposing arguments posed by the chatbot.
3. Write a short reflection on whether and how their position changed during the discussion.

In the seminar, students develop a discussion based on insights from their interaction with the chatbot, compare arguments, and critically evaluate sources of information.

Active learning is achieved through defending positions, evaluating information, and metacognitive reflection.

There are many other AI chatbots (AI virtual assistants) available on the market, such as Gemini, Claude, Perplexity, Copilot, Deepseek, and Mistral_AI.

Image. A display of logos of leading AI chatbots (Source: Created using Gemini, 2025)

10. Techniques for applying artificial intelligence to encourage active learning

Active learning involves approaches in which students are not passive observers but actively think, apply, discuss, and question content during the learning process. These approaches enable a deeper understanding of concepts, foster critical thinking, and increase knowledge retention. In higher education, the importance of active learning is further highlighted by the need to develop student autonomy and the skills required for professional life.

Despite substantial evidence of its effectiveness, active learning in practice often faces challenges, such as large student groups, limited time for individualised instructions, and varying levels of prior knowledge within the same class.

Artificial intelligence can help address these challenges, but it is important to emphasise that it does not replace teachers. Instead, it serves as support, enabling teachers to better plan activities and providing students with more opportunities for engaged and meaningful work.

Active learning supported by artificial intelligence refers to situations in which AI is used as a tool for designing and implementing pedagogical activities, rather than as an automated source of ready-made answers. Active learning occurs when students:

• Analyse and interpret information.
• Make decisions and justify positions.
• Exchange ideas with others.
• Question their own mistakes.
• Reflect on the learning process.

Artificial intelligence supports this by increasing opportunities for activity, adjusting the level of cognitive challenge, providing timely feedback, and offering authentic learning situations in which students can actively apply knowledge.

11. Conclusion

The application of artificial intelligence in higher education offers numerous possibilities but also requires responsible consideration. The quality of an artificial intelligence system largely depends on the data used for its development. If this data is limited, unrepresentative, or biased, the system's results can lead to undesirable consequences in the educational process.

There is also a risk that students may uncritically accept the system's recommendations, reducing their engagement in making educational decisions and reflecting on their own knowledge. The development of active learning requires that technology remains a support, not a substitute, for students' independent thinking and academic responsibility.

Another key issue concerns privacy protection. The collection and analysis of large amounts of student data must comply with ethical standards and legal regulations to ensure the transparency and security of personal information.

Final reflection:

Artificial intelligence techniques for stimulating active learning are already present in many educational systems, but their effectiveness depends on the pedagogical context, the quality of integration into teaching, and the competence of teachers in their use. The teacher remains the central figure in the educational process, and technology serves as a tool to enhance student interaction, motivation, and the development of academic competencies.

• Consider which of the tools presented could be applied in your own teaching to increase student engagement, improve their understanding of the material, and at the same time preserve the pedagogical quality and ethics of the educational process.

12. Literature

• Bonwell, C. C., & Eison, J. A. (1991). Active learning: Creating excitement in the classroom (ASHE-ERIC Higher Education Report No. 1). Jossey-Bass.
  
  • Description: A classic work that defines and promotes active learning techniques to increase student engagement in the classroom.
    
    
• Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
  
  • Description: A detailed study of the development of intelligent systems that support personalised learning.
    
    
• VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
  
  • Description: An analysis of the effectiveness of intelligent tutoring systems compared to human tutors in education.
• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016) Intelligence Unleashed: An argument for AI in Education 
  
  • Description: An overview of the potential of AI to transform education, emphasising support for teachers and students.
• Dillenbourg, P. (2013). Design for classroom orchestration. Computers & Education, 69, 485–492.
  
  • Description: A discussion of the teacher's role in leading and directing digital and AI-assisted activities in the classroom.
    
    
• 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.
• Adiyono, A., Jasiah, J., Ritonga, M., & Al-Matari, A. S. (2024). ChatGPT and active learning: A new paradigm for student participation in the classroom. In M. Lahby (Ed.), Empowering Digital Education with ChatGPT: From Theoretical to Practical Applications (pp. 189–209). Chapman and Hall/CRC.
  
  • Description: This paper analyses how ChatGPT encourages active learning through increased interaction, personalisation and the teacher's role as a mentor in the classroom.
    
    
• U.S. Department of Education (2024). Artificial intelligence and the future of teaching and learning: Insights and recommendations.
  
  • Description: The US government provides guidance on integrating artificial intelligence into education, emphasising metacognition and support for teachers.
    
    
• Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communications, 12, 621.
  • Description: A meta-analysis of 51 studies showing that ChatGPT significantly improves students' learning outcomes, perceptions of learning and development of higher cognitive skills.
    
    
• Chapwanya, O. (2025). Exploring the teacher's role amid rising generative AI. STEL Journal, 4(3), 45–67.
  
  • Description: This paper analyses the importance of the teacher's role in the age of generative artificial intelligence, emphasising pedagogical effectiveness and emotional support.