(Generative) Artificial intelligence (AI) is dramatically changing how knowledge work is done. Across tasks and domains, AI is moving from a passive tool to an active collaborator that participates in the work itself: it proposes ideas, drafts and revises artifacts, analyzes information, and takes on tasks that used to be fully human. For instance, AI supports software development as a coding assistant, writing and content creation, data analysis and decision-making, design, research, and the delivery of digital services. Wherever it is applied, the central question is: how can humans and AI work together to create value?

Realizing these opportunities requires more than access to the technology. Human-AI collaboration fundamentally shifts people's roles, tasks, and required skills. On the one hand, users must learn to exploit the advantages of AI: delegating suitable tasks, leveraging its ability to generate results remarkably fast, and orchestrating its contributions throughout a workflow. On the other hand, because AI works so quickly and fluently produces output, critical thinking becomes more important than ever: AI is inherently uncertain, so users and developers must question and validate what it produces, explain the decisions behind their work, and take ownership of the result.

Shaping human-AI collaboration therefore becomes one of the defining challenges of our time. Effective human-AI collaboration rests on several key concepts: understanding the complementarity of human and AI strengths, delegating tasks accordingly, designing where to keep a human in the loop, recognizing and handling the uncertainty inherent in human-AI collaboration, and navigating the shift in the skills that people need.

This seminar follows a research-oriented approach: students engage scientifically with human-AI collaboration. Building on the current body of research, each student investigates a focused research question, for example by systematically reviewing the literature, developing a conceptual argument, or working on an empirical study. Students learn to situate their work in the academic literature, apply appropriate research methods, critically analyze evidence, and communicate their findings in a written seminar paper and a presentation.

Since we try to keep the content up to date, the concrete topics of this practical seminar are subject to change. 

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for Human-AI Interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19), 1–13. https://doi.org/10.1145/3290605.3300233

Bartelheimer, C., Heinz, D., Hönigsberg, S., Siemon, D., Li, M., Strohmann, T., Poeppelbuss, J., & Peters, C. (2025). Conceptualizing hybrid intelligent service ecosystems. Electronic Markets, 35, Article 44. https://doi.org/10.1007/s12525-025-00798-4

Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid Intelligence. Business & Information Systems Engineering, 61(5), 637–643. https://doi.org/10.1007/s12599-019-00595-2

Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI. MIS Quarterly, 45(3), 1527–1556. https://misq.umn.edu/misq/article/45/3/1527/1891/

Hemmer, P., Schemmer, M., Kühl, N., Vössing, M., & Satzger, G. (2025). Complementarity in human-AI collaboration: concept, sources, and evidence. European Journal of Information Systems. https://doi.org/10.1080/0960085X.2025.2475962

Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57(2), 103174. https://doi.org/10.1016/j.im.2019.103174

Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8(12), 2293–2303. https://doi.org/10.1038/s41562-024-02024-1