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Artificial Intelligence in Educational Contexts (Seminar)

Course Information (Winter Semester 2025/26)

This website provides an overview of the seminar. For official course details, enrollment information, and materials, please refer to the university course catalogue.

Instructor: Dr. Anett Hoppe

Introduction session: October 16, 3 p.m.; Lecture Hall V (HS V, MZG) 04A23 (HS V A4) (Seminarraum)

Working phase: Independent research with one optional individual consultation (20 minutes)

Block seminar: 2 days in February/March 2025 (presence mandatory)

Assessment:

  • Seminar presentation (20-30 min)
  • Synthesis paper (5-6 pages)
  • AI usage documentation
  • Active participation

Target Audience

Bachelor/Master students in Computer Science, Data Science, and Computer Science Education (maximum 14 participants)

Prerequisites

Recommended: Conceptual knowledge of machine learning and/or computer science education. Openness to interdisciplinary reflection on educational, ethical, and social implications of AI systems.

Course Overview

Artificial intelligence is rapidly transforming educational contexts—from intelligent tutoring systems and automated grading to learning analytics and personalized content generation. With 500 million daily views of educational content on YouTube and nearly one in five ChatGPT conversations related to learning, AI's presence in education is undeniable. But critical questions remain: What actually works for learning? How do different stakeholders experience these tools? Who benefits, and who doesn't?

This seminar examines current AI applications in education through technical, pedagogical, and critical lenses. Students will explore diverse research areas including LLM-based tutoring, video learning analytics, knowledge tracing, bias and fairness, collaborative learning support, and AI literacy. Through presentation and discussion of recent research papers, students develop the ability to critically evaluate whether AI educational technologies adequately address interdisciplinary concerns, stakeholder needs, ethical implications, and data sensitivity.

Topics Covered

Students will explore research areas organized around four themes:

Critical Foundations

  • Limitations and risks of LLMs in education
  • User perceptions of AI in educational settings
  • AI literacy in educational contexts
  • Bias, fairness, and educational equity

Modeling and Assessment (Understanding and Evaluating Learning)

  • Knowledge Tracing and Student Modeling
  • Automated Assessment and Grading Systems
  • AI-Powered Feedback Generation
  • Video-Based Learning Analytics and Engagement Detection

Design and Support (Supporting Learners and Teachers)

  • AI Tools for Curriculum and Course Design
  • AI for Collaborative Learning and Peer Interaction
  • Intelligent Programming Education and Code Assessment 
  • AI for Supporting Neurodivergent and Disabled Learners

Emerging Applications

  • Student Agency, Engagement, and Motivation with AI
  • Game-Based Learning with AI
  • AR/VR-AI Applications for Learning