Julian Benz 

Motivation

Recent developments in large language models (LLMs) have transformed knowledge work by providing unprecedented problem-solving support. Yet this capability comes with a critical trade-off: empirical evidence demonstrates that when users passively consume LLM-generated answers without actively engaging in reasoning and reflection, they risk intellectual deskilling—losing their own problem-solving, critical thinking, and communication abilities.
As generative AI becomes increasingly embedded in professional and educational contexts, the challenge is how to design systems that preserve—and even enhance—human capabilities alongside AI assistance.
 

Objectives

This thesis explores how LLM-based systems can be designed to promote active engagement rather than passive consumption — thereby counteracting intellectual deskilling in knowledge work. Key design dimensions under investigation include explanations and, feedback mechanisms, among others. Depending on your background, the project will pursue an empirical, design-oriented, or theoretical approach, with a focused research question to be developed collaboratively.

Profile

• Interest in interdisciplinary research of human interaction with AI systems
• Technical skills: Basic understanding of LLMs technological foundations, programming skills (depending on approach)
• Self-driven and open learning attitude and curiosity
• English skills

Contact

We offer an exciting research topic with strong relevance to both academia and practice, close supervision, and the opportunity to develop theoretical, methodological, and practical skills. If you are interested, please send a current transcript of records, a short CV, and a brief motivation (2–3 sentences) to Julian Benz (julian-david.benz@kit.edu).

Literature

• Kosmyna, Nataliya, Eugene Hauptmann, Ye Tong Yuan, u. a. „Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task“. arXiv:2506.08872. Preprint, arXiv, 31. Dezember 2025.
• Förster, M., Broder, H. R., Fahr, M. C., Klier, M., & Fink, L. (2025). Tell me more, tell me more: the impact of explanations on learning from feedback provided by Artificial Intelligence. European Journal of Information Systems, 34(2), 323-345.
• Förster, M., Schröppel, P., Schwenke, C., Fink, L., & Klier, M. (2024). Choose Wisely: Leveraging Explainable AI to Support Reflective Decision-Making. International Conference on Information Systems.