Elective Seminar: Human-AI Collaboration

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This course covers the state-of-the-art of effective human-AI collaboration. This includes:

• Forms of artificial intelligence (AI) and AI-enabled support: Hybrid intelligence vs. ensembling
• AI-enabled human augmentation vs. automation
• Agency of AI
• Trust in AI
• AI as a source of competitive advantage
• Future of work/ Biohacking
• Ethical consequences of AI

This course prepares students for a job market in which humans who know how to use AI replace those who don’t. Path-breaking, AI-enabled technologies, with applications like ChatGPT, Falcon, or Gemini, develop at an unprecedented speed. We can perceive them as threats or opportunities – but we can no longer deny their existence. The knowledge and skills acquired in this course enable students to understand how AI can support them individually to become more effective in their studies and work as well as companies to increase competitiveness, and also sheds light on AI’s “dark sides”. Participants will gain an understanding of the different roles “intelligent” technologies can take (augmentation vs. automation). Further, they will learn about potential work and process designs with AI (ensembling vs. hybrid intelligence). In addition, students will engage in a critical reflection on the consequences of technological advancement in the field of AI on individuals, businesses, and society and learn how to use digital, AI-enabled support in a responsible and transparent manner.

Technical and methodological competence

• Students can name and explain different forms of human-AI collaboration
• Students can discuss the advantages and disadvantages of different forms of human-AI collaboration
• Students can explain the opportunities and challenges of human-AI collaboration
• Students can design suitable work and task designs for human-AI collaboration
• Students can name the influencing factors on human trust in AI
• Students can describe and explain the concept of machine agency
• Students can reflect on their usage of AI
• Students can make well-founded recommendations for responsible use of AI

Social and communicative skills and self-skills

• Students can solve tasks independently and on time (reliability).
• Students can summarize information and present it to the target group (expressiveness and demeanor).
• The students understand the solutions of others and can make constructive suggestions for improvement and deal with feedback (ability to criticize) and reflect on their own abilities and limits (self-reflection ability).
• Ability and willingness to acquire new knowledge independently and to learn from successes and failures (learning competence and motivation).

Lectures and exercises

• Group presentations (task assignment will be provided in class)

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• Anthony, C. Bechky, B.A., Fayard, A.-L. (2023). “Collaborating” with AI: Taking a systematic view to explore the future of work, Organization Science, https://doi.org/10.1287/orsc.2022.1651.
• Balasubramanian, N., Ye, Y., & Xu, M. (2022). Substituting Human Decision-Making with Machine Learning: Implications for Organizational Learning. Academy of Management Review, 47(3), 448–465. https://doi.org/10.5465/amr.2019.0470
• Baptista, J., Stein, M.-K., Klein, S., Watson-Manheim, M. B., & Lee, J. (2020). Digital work and organisational transformation: Emergent digital/ human work configurations in modern organisations. Journal of Strategic Information Systems, 29(2), 101618. https://doi.org/10.1016/j.jsis.2020.101618
• Bouschery, S.G., Blazevic, V., Piller, F.T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models, Journal of Product Innovation Management. https://doi.org/10.1111/jpim.12656
• Choudhary, V., Marchetti, A., Shrestha, Y. R., & Puranam, P. (2023). Human-AI ensembles: When can they work? Journal of Management, 1–34. https://doi.org/10.1177/01492063231194968
• Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7
• Glikson, E., Williams Woolley, A. (2020). Human trust in artificial intelligence: Review of empirical research, Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
• Hughes, H. P. N., & Davis, M. C. (2024). Preparing a Graduate Talent Pipeline for the Hybrid Workplace: Rethinking Digital Upskilling and Employability. Academy of Management Learning & Education, amle.2023.0106. https://doi.org/10.5465/amle.2023.0106
• Jarrahi, M.H., Monahan, K., Leonardi, P. (2023). What will working with AI really require?, Harvard Business Review Digital Articles, June 8th, 2023.
• Kemp, A. (2023). Competitive advantage through artificial intelligence: Toward a theory of situated AI, Academy of Management Review, published online April 27, 2023. https://doi.org/10.5465/amr.2020.0205
• Kokshagina, O., & Schneider, S. (2023). The Digital Workplace: Navigating in a Jungle of Paradoxical Tensions. California Management Review, 65(2), 129–155. https://doi.org/10.1177/00081256221137720
• Murray, A., Rhymer, J., & Sirmon, D. G. (2021). Humans and technology: Forms of conjoined agency in organizations. Academy of Management Review, 46(3), 552–571. https://doi.org/10.5465/amr.2019.0186
• Puntoni, S., & Wertenbroch, K. (2024). Being Human in the Age of AI. Journal of the Association for Consumer Research, 9(3), 235–240. https://doi.org/10.1086/730788
• Raisch, S., & Fomina, K. (2024). Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations. Academy of Management Review, amr.2021.0421. https://doi.org/10.5465/amr.2021.0421
• Yalcin, G., & Puntoni, S. (2023). How AI affects our sense of self—And why it matters for business. Harvard Business Review, 101(5), 130–136.

In-class lecture