Objectives
The objective of the course is to introduce students to the potential of artificial intelligence (AI) in supporting learning processes. Students will explore contemporary AI tools and learn how to use them effectively to enhance their own learning. Special emphasis is placed on adaptive learning systems, language models, and personalized learning strategies. The course also aims to improve students’ digital and AI literacy, critical thinking, and the responsible and ethical use of technology in academic contexts.
Academic results
Knowledge
- Knows the basic concepts of artificial intelligence and its educational applications.
- Knows the principles of adaptive learning systems and their role in personalized learning.
- Knows how large language models (e.g., ChatGPT) function and support learning.
- Knows the strengths and limitations of AI tools in learning contexts.
- Knows the fundamental principles of ethical AI use in education.
Skills
- Is able to select appropriate AI tools for educational purposes.
- Is able to apply AI tools to support their own learning objectives.
- Is able to critically evaluate the reliability of AI-generated content.
- Is able to design a simple AI-supported learning process or strategy.
- Is able to use AI tools responsibly and ethically in academic settings.
Attitude
- Is open to applying new technologies for learning support.
- Demonstrates interest in the conscious and responsible use of AI tools.
- Is committed to integrating technology into their learning in a reflective and goal-oriented way.
- Is receptive to critically addressing ethical, social, and educational issues.
- Supports lifelong learning and the continuous development of digital competencies.
Independence and responsibility
- Is capable of independently and consciously using AI tools for academic purposes.
- Assumes responsibility for decisions made using AI tools and their consequences.
- Strives for ethical, sustainable, and intentional use of technology in learning.
- Is able to reflect on their own learning processes and the role of AI in shaping them.
- Is committed to lifelong learning and the continuous development of digital competencies.
Teaching methodology
The course follows an active, student-centered approach. Teaching methods include lectures, interactive seminars, hands-on workshops, and case-based analyses. Through project-based learning (PBL), students apply AI tools to their own learning contexts. Emphasis is placed on reflective learning, collaboration, and experimentation with AI tools.
Materials supporting learning
- Selected academic articles and case studies on AI applications in education (available on the course Moodle site).
- Practice tasks and user guides for AI tools (e.g., ChatGPT, Notion AI, Khanmigo, Grammarly).
- Instructor slides, thematic videos, and case-based video materials.
- Interactive learning modules (e.g., ethical dilemma simulations, formative quizzes).
- Official documentation and help pages for AI tools (e.g., OpenAI, Google AI, Microsoft Education).
- Template and evaluation criteria for the personal learning project.
General Rules
A 2.1 és 2.2. pontban megfogalmazott célok és tanulási eredmények elérése a gyakorlatokon tanúsított aktív részvétel (részteljesítmény értékelés) alapján történő dokumentálása alapján történik.
Performance assessment methods
Egyéni és csoportmunka feladatokban való aktív részvétel
Percentage of performance assessments, conducted during the study period, within the rating
- részteljesítmény értékelés (házi feladat): 60
- részteljesítmény értékelés (aktív részvétel): 40
Percentage of exam elements within the rating
Conditions for obtaining a signature, validity of the signature
Minimum 70% attendance at scheduled classes; active engagement with the course’s online platform (e.g., downloading and submitting assignments, participating in discussion forums); achievement of at least 50% of the total attainable score from all mandatory performance assessments.
Issuing grades
| % | |
|---|---|
| Excellent | 96-100 |
| Very good | 88-95% |
| Good | 76-87% |
| Satisfactory | 63-75% |
| Pass | 50-62% |
| Fail | 0-49% |
Retake and late completion
At least one opportunity is provided during the semester to submit missing assignments. Tasks not submitted by the deadline or receiving a failing grade may be resubmitted or corrected no later than the end of the make-up week. Make-up work must comply with the formats and deadlines defined in the course requirements
Coursework required for the completion of the subject
| Nature of work | Number of sessions per term |
|---|---|
| részteljesítmény értékelés (házi feladat) | 76 |
| részteljesítmény értékelés (aktív részvétel) | 14 |
Approval and validity of subject requirements
Consulted with the Faculty Student Representative Committee, approved by the Vice Dean for Education, valid from: 02.06.2025.
Topics covered during the term
| Lecture topics | |
|---|---|
| 1. | Introduction to Artificial Intelligence and its Applications in Education - Basic concepts, historical context, the role of AI in digital learning. |
| 2. | How Large Language Models Work - Foundations of generative AI, typical use cases in student learning. |
| 3. | Adaptive Learning Systems and Personalized Education -Learning styles, automated feedback, learning paths. |
| 4. | AI Tools for Supporting Self-Directed Learning - Note-taking, quiz generators, translation, and visualization tools. |
| 5. | Ethical Issues and the Responsible Use of AI - Plagiarism, bias, data protection, transparency. |
| 6. | Workshop: Personal Learning Project with AI Tools -Selecting, applying, and evaluating tools aligned with learning goals. |
| 7. | Reflection and Insights: AI in Personal Learning -Personal experiences, changes in learning strategies, feedback. |
Additional lecturers
| Name | Position | Contact details |
|---|---|---|
| Dr. Saule Anafinova | university assistant professor | saule.anafinova@edu.bme.hu |