Main Content
Finding, Understanding, Trusting: Human-AI Interaction in Modern Information Retrieval (Seminar)
Course Information (Summer Semester 2026)
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: April 16, 4 p.m.; Location tba
Working phase: Independent research with one optional individual consultation (20 minutes)
Block seminar: 2-3 days in June/July 2026 (presence mandatory)
Assessment:
- Seminar presentation (20-30 min)
- Synthesis paper (5-6 pages)
- AI usage documentation
- Active participation
Target Audience
Primarily Master students in Computer Science, Data Science & Computer Science Education; advanced Bachelor students may be admitted upon request. (maximum 14 participants)
Prerequisites
Recommended: Basic knowledge of machine learning and/or data science, including elementary statistics. Openness to engaging critically with current research literature.
Course Overview
In this Master-level block seminar, we explore cutting-edge topics at the intersection of information retrieval and artificial intelligence. As LLMs and multimodal models are transforming how people search for, access, and evaluate information, classic notions of "good retrieval" are being fundamentally challenged. Students will engage critically with recent research across four thematic clusters: retrieval in the LLM era (including RAG systems and AI-generated summaries), ethics and fairness in search (bias, transparency, personalization and privacy), multimodal retrieval (cross-modal, composed, and video retrieval), and domain-specific search (scholarly literature and code retrieval). A particular emphasis is placed on human-centred evaluation: students will not only present and discuss recent research papers, but also design and conduct small user studies, developing hands-on experience with empirical research methods in IR.