Content
The course provides an introduction to human–AI interaction in operations management. It covers
methods and tools including predictive analytics, machine learning, generative artificial intelligence,
and agentic AI, with a focus on how these technologies interact with human decision-making in
operational contexts. The course presents theoretical foundations and practical perspectives on
human–AI collaboration, addressing topics such as trust, transparency, delegation, and system-
level performance in areas including forecasting, planning, and operational decision support.
Learning outcomes
The module provides a structured overview of human–AI interaction in operations management,
focusing on how artificial intelligence systems are designed, interpreted, and integrated into
operational decision-making. Through lectures, exercises, and selected hands-on activities,
students will develop an understanding of core AI technologies, including predictive models and
generative AI, and their implications for human judgment, trust, and performance in operational
contexts such as forecasting, planning, and resource allocation. The course emphasizes
the interaction between human decision-makers and AI systems, addressing topics such as
transparency, human override, biases, and system-level performance. Practical exercises and
a final group project will enable students to analyze, design, and critically evaluate human–AI
decision-support systems in operations management, demonstrating their ability to apply both
technical and managerial insights to real-world problems.
Examination
The assessment is through 3 graded homework assignments (15% each), one practice lab
module(5%) and one final project (10 page report) including a group presentation (50%,15mins
+5min discussion)
Prerequisites
Logistics and Supply Chain Management
Teaching & learning methods
After participating in this module, students are able to understand key concepts and methods
related to artificial intelligence and human–AI interaction in operations management, and how
these approaches support operational decision-making. They are able to analyze and evaluate AI-
based decision-support systems, interpret their outputs, and assess their implications for human
judgment, trust, and performance.
Students further comprehend the strengths and limitations of different human–AI interaction
designs, including issues related to transparency, delegation, and human intervention. Through
exercises and a project-based assignment, they are able to apply these concepts to practical
operational decision problems, and to design and critically assess human–AI decision-support
solutions in an operations management context.
Media & reading list
Moodle, reading list
Donohue, K., Katok, E., & Leider, S. (Eds.). (2018). The handbook of behavioral operations.
Hoboken, NJ: Wiley.ISBN#978-1-119-13830-3; Mitchell, M.
(2019). Artificial intelligence: A guide for thinking humans. New York, NY: Farrar, Straus and
Giroux.ISBN: 978-0374257835; Agrawal, A., Gans, J., &
Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Boston,
MA: Harvard Business Review Press.ISBN: 978-1633695672; Cohen, M. C., &
Dai, T. (Eds.). (2025). Artificial intelligence in supply chains: Perspectives from global thought
leaders. Cham, Switzerland: Springer.ISBN:978-3-032-07054-8 (Alternative: Cohen, Maxime C.
and Dai, Tinglong et al.Supply Chain Management in the AI Era: A Vision Statement from the
Operations Management Community (2026). Stanford University Graduate School of Business
Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=5792542 or http://
dx.doi.org/10.2139/ssrn.5792542
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