The course will be centered around analyzing educational data using AI methods and methodological and system-focused perspectives on designing AI systems for education.
The course will start with an introduction to data mining techniques (e.g., prediction, structure discovery, visualization, and relationship mining) relevant to analyzing educational data. We will then continue with topics on Large Language Models in Education and personalization in AI in educational technologies (e.g., learner modeling and knowledge tracing, self-improving AIED systems) while showcasing example applications in areas such as content curation, automatic assessment and dialog-based tutoring. We will also touch upon ethical challenges associated with using AI in student facing settings.
Face-to-face meetings will be held every fortnight, although students will be expected to work individually on weekly tasks (e.g., discussing relevant literature, working on problems, preparing seminar presentations).
Students will be expected to:
This is a research driven, hands-on class. Your participation is important.
The final assessment will be a combination of:
No written exams! Our focus is on learning and continuous evaluation.
Lectures: Thu 13:15-15:00 (ML F38)
Exercise Sessions: Thu 15:15-16:00 (ML F38)
Discussion forum: (Moodle)
Literature: No textbook is required, but there will be regularly assigned readings from research literature, linked to the course website.
Prerequisites/Notice: There are no formal prerequisites for this class. However, it will help if the student has taken an undergraduate or graduate level class in statistics, data science or machine learning. This class is appropriate for advanced undergraduates and master students in Computer Science as well as PhD students in other departments.
| Lecture | Date | Topic | Course Materials | Events |
| 1 | 19.09.24 | Introduction | ||
| 2 | 03.10.24 | Classical Educational Data Mining (Learning Analytics and Algorithmic Bias) |
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| 3 | 17.10.24 | Generative AI for Education - I | ||
| 4 | 31.10.24 | Generative AI for Education - II | ||
| 5 | 07.11.24 | Intelligent Tutoring Systems | ||
| 6 | 28.11.24 | Adaptivity and Personalization (Learner Modeling, Knowledge Tracing) |
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| 7 | 12.12.24 | Human-centred AIED (Guest lecture) |
You can ask questions on Moodle. Please post questions there, so others can see them and share in the discussion. If you have questions which are not of general interest, please don’t hesitate to contact us directly.
| Lecturers | Mrinmaya Sachan |
| Teaching Assistants | Kumar Shridhar, Jakub Macina, Sankalan Pal Chowdhury, Peng Cui |