(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 course follows an experiential learning approach: Students apply and experience the concepts of human-AI collaboration using AI tools to solve real-world challenges. Rather than studying human-AI collaboration in the abstract, students plan and complete hands-on projects with AI, and they critically reflect on both the result and the collaboration itself as they experience these concepts in their own work.

Since we try to keep the content up to date, the concrete topics of this practical seminar are subject to change. Further information about the content and registration on https://wi4.win.kit.edu/63.php.

The total workload for this course is structured as follows:

• Attendance at lectures/exercises (presence time) (3 SWS × 15 weeks): 45 h

• Preparation and follow-up of course content: 50 h

• Exam preparation and participation in the assessment: 40 h

Total workload: 135 h (corresponds to 4.5 ECTS credits)

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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

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