Human-AI Interaction in Operations Management (Master Program)

(Prof. Dr. Stefan Minner, Yuxuan Zhu, Ben Mischeck)

Tuesday and Thursday, 16:45 – 18:15, Room N1135

Course Description

The course provides an introduction to behavioral operations with a focus on human-AI interaction. It examines how operational decisions are made when AI-based decision support is available, and how behavioral factors, such as trust, cognitive biases, and reliance, shape decision processes and outcomes. Methods and tools, including predictive analytics, generative AI, and agentic AI, are introduced and their use in operational decision support systems is discussed. Practical behavioral experiments will allow the exploration of the impact of AI support on human decision-making. Application areas cover demand forecasting, inventory and capacity planning, supplier and operations coordination, as well as production and service scheduling. 

Recommended Prerequisites

Logistics and Supply Chain Management

Learning Objectives

After participating in this course, students are able to: 

• Understand key behavioral mechanisms and their role in human-AI decision-making in operations management.
• Evaluate AI-based decision support systems, interpret outputs, and assess implications for human decision-making.
• Design, run, and interpret behavioral experiments to compare human decisions, AI recommendations, and human-AI collaboration.
• Apply theoretical concepts to practical decision problems and assess human-AI decision support in an operations management context. 

Methods

The module provides an overview of human-AI interaction in operations management by combining lectures with hands-on behavioral experiments. Students complete homework assignments and a project-based group assignment in which they design and implement an experimental setup, analyze behavioral data, and derive implications for the design of human-AI decision support in operations management.

Grading

The assessment is based on 3 graded homework assignments (15% each), a practical exercise (5%) and one final group project (10-page report), including a presentation (50%).

Literature

• Cohen, M. C., & Dai, T. (Eds.). (2025), Artificial Intelligence in Supply Chains: Perspectives from Global Thought Leaders, Springer. ISBN: 978-3-032-07054-8.
• Donohue, K., Katok, E., & Leider, S. (Eds.). (2018), The Handbook of Behavioral Operations, Wiley. ISBN: 978-1-119-13830-3.
• Agrawal, A., Gans, J., & Goldfarb, A. (2018), Prediction Machines: The Simple Economics of Artificial Intelligence, Harvard Business Review Press. ISBN: 978-1633695672.