 The Impact of Human–Artificial Intelligence Collaboration on Learning in Teams, Organizations, and SocietyPatrick Hendriks Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL) Abstract: Humans are not born with all the knowledge or skills needed to navigate the countless decisions, challenges, and uncertainties they face every day in their personal and professional lives. Instead, we rely on our ability to learn, developing strategies and acquiring competencies that allow us to adapt to our environment and grow over time. While some knowledge can be acquired through personal experimentation, many of the abilities we depend on have become too complex to be rediscovered through trial and error alone. As a result, humans have long recognized that progress, whether scientific, artistic, or otherwise, is only possible because we build on the insights and experiences of others. Put differently, we often learn far more effectively when we draw on existing knowledge rather than by “reinventing the wheel.” Through direct observation, imitation, collaboration, and instruction, people can acquire knowledge more quickly and with fewer risks than they could through solitary exploration. The same is true for indirect forms of social learning, such as reading books or searching the internet, which provide access to the cumulative experience of others across both space and time. As knowledge accumulates faster than any person can master it, technologies that help us acquire, store, and share information have become indispensable to organizational and societal progress. Artificial intelligence (AI) represents one of the latest milestones in a long line of technologies that have transformed the way people learn. However, unlike earlier breakthroughs such as the printing press or the internet, AI’s impact goes beyond reshaping how we store and share existing knowledge. With its ability to discover complex relationships in large amounts of data, AI can provide advice, make recommendations, and even contribute its own knowledge to organizational and societal learning processes. For example, AI can help discover promising cancer treatments by predicting patient responses to different immunotherapy drugs. This transformative potential has further grown with the emergence of generative AI, which can create entirely new content such as text, images, music, and videos by recombining and building on the human knowledge embedded in its training data. Rather than passively augmenting human learning, AI is actively collaborating with humans in the process of knowledge creation. As a result, few doubt that AI will continue to influence what we learn, how we learn it, and even who we learn from. However, this impact is unlikely to be uniform across all contexts. While AI has the potential to accelerate discovery and democratize information access, it may also obscure human expertise or reinforce existing biases. To guide researchers and practitioners in integrating AI in ways that augment, rather than displace, human learning, this dissertation examines the impact of AI at three levels of analysis, progressing from teams to organizations to society at large. At the team level, this dissertation explores how organizations can effectively manage human-AI teams to promote team collaboration. Based on interviews with potential end users, a prototype team-AI collaboration system was developed that allows human team members to individually configure AI agents by assigning them different roles and personalities. This system was then evaluated through a laboratory experiment in which human-AI teams collaborated on a decision-making task. The results suggest that integrating configurable AI team members into human teams can improve performance by introducing complementary perspectives. However, human participants consistently favored their own expertise for final team decisions, often disregarding superior solutions provided by AI agents. Shifting the focus from collaboration partners to environments, two studies investigate how the virtual reality (VR)-based metaverse can facilitate team collaboration. In a laboratory experiment, five teams performed a collaborative decision-making task using either a VR-based metaverse platform (i.e., Meta Horizon Workrooms) or a traditional videoconferencing tool (i.e., Zoom). The results indicate that team collaboration in the metaverse can be a viable alternative to videoconferencing tools, offering comparable (and in some areas superior) levels of effectiveness, even in teams with minimal prior VR experience. At the organizational level, this dissertation examines how organizations can coordinate the learning activities of their human members and AI to enhance overall organizational learning effectiveness. One study investigates the mutual learning dynamics between humans and AI by introducing artificial assistants (i.e., AI systems designed for recurring one-to-one collaboration) that learn alongside humans. These artificial assistants can affirm or challenge human knowledge while also contributing entirely new insights from domains beyond their human partners’ expertise. Through a series of agent-based simulations, the results show that artificial assistants can reduce learning myopia, the human tendency to favor familiar strategies over new and potentially better alternatives. Optimal outcomes occur when organizations ensure that humans and AI are equally receptive to each other’s insights, thus preventing an unbalanced learning process. A second study examines how AI not only learns but also shapes organizational processes by enacting its own beliefs. For example, AI can select job candidates based on self-learned practices, gradually reshaping the organization’s view of what makes a “good” candidate. Extending an established simulation model, the results suggest that extensive coordination of enactment activities may be unnecessary if humans and AI collaborate periodically to keep their beliefs aligned. Together, these studies highlight that effective human-AI collaboration depends on strategic managerial coordination to maximize organizational learning and adaptability. At the societal level, this dissertation explores strategies for integrating AI into society without compromising cultural diversity. One study examines how different AI integration strategies affect the evolution of cultural beliefs, using agent-based simulations to model interactions between humans and AI. The simulation results show that localized AI, designed to reflect regional or national values, may inadvertently reduce cultural diversity by blending the beliefs of neighboring social groups, challenging the assumption that localization inherently preserves unique cultural identities. In contrast, globalized AI, trained on data biased toward a dominant culture, may initially support diversity but risks long-term polarization by pushing groups with divergent beliefs toward (extreme) views that differ significantly from those of the surrounding majority. These findings underscore that AI affects culture in complex and sometimes unexpected ways, spreading beliefs while also creating personalized echo chambers. To mitigate these risks, the simulation results highlight the need for carefully designed policies that ensure AI leaves space for different perspectives and does not unintentionally reinforce social divides. The studies presented in this dissertation highlight that AI is no longer merely a passive tool but an active participant in human learning processes at the team, organizational, and societal levels. They demonstrate that AI’s ability to both complement and challenge human expertise can enhance collaboration, promote broader knowledge sharing, and mitigate human biases, but only if its integration is carefully managed. Without deliberate coordination, AI can instead reinforce inequalities, entrench dominant narratives, and undermine diversity. This dissertation contributes to the growing understanding of AI’s influence on human learning by offering practical strategies for designing, integrating, and governing AI systems that augment human capabilities. In doing so, it lays critical groundwork for future research aimed at fostering human-AI collaborations that enhance human learning and support the co-creation of knowledge without sacrificing unique human knowledge and agency in the learning process. Date: 2025-09-17 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:dar:wpaper:157166 Access Statistics for this paper More papers in Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL) Contact information at EDIRC. |
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