Machines as teammates: A research agenda on AI in team collaboration

By Isabella Seeber, E. Bittner, Robert Owen Briggs, Triparna de Vreede, G. Vreede, Aaron C. Elkins, R. Maier, A. B. Merz, Sarah Oeste-Reiss, Nils L. Randrup and 2 more. Information Manager (The), 2020

713 citations recorded by the source corpus.

Abstract What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas – Machine artifact, Collaboration, and Institution – and 17 dualities – significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.

Subfields: Safety Research, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f779529aeee1b0b7ad9c0f1845523ff7352ba775

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Citations in the corpus

User Perceptions and Trust of Explainable Machine Learning Fake News Detectors (2022, background): “Similarly, some researchers suggest that viewing AI application as teammates rather than tools helps our understanding of human trust in AI (Seeber et al., 2020; Wynne & Lyons, 2018).”
The opportunities and challenges of ChatGPT in education (2023, neutral): “Several organizations are investing heavily in the AI-generated chatbot and its countermeasure, and students should not be left out as part of the research teams to ﬁ nd a solution to mitigate the negative aspects of its usage (Calla-han, 2023; Chechitelli, 2023; Seeber et al., 2020).”
Quo vadis artificial intelligence? (2022, background): “In the meantime, the competence in humans and AI is concerted to a collaborative relationship [104].”
AI literacy and its implications for prompt engineering strategies (2024, background): “Therefore, as LLM-based AI systems are seen as a teammate and companions for collaboration and task-solving (Ni β en et al., 2022; Seeber et al., 2020; Siemon et al., 2022), it could be helpful in higher education to impart knowledge about the functioning of generative AI to leverage the…”
Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? (2022, positive): “These systems can augment learning and professional development in situ and can impact human and machine collaboration during teamwork (Seeber et al., 2020).”
The Impact of Artificial Intelligence on Workers' Skills: Upskilling and Reskilling in Organisations (2023, background): “AI systems can also be used in some phases of a complex problem - solving process (Seeber et al., 2020).”
How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents (2020, background): “…we expect that the user ’ s Figure 1 Step by step theoretical development perceptions of the PIA will change from initially perceiving it as a tool to eventually perceiving it as a collaborator in the process of task completion and decision-making (Seeber et al., 2019; Dellermann et al., 2019).”
The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development (2023, background): “[80], human-centered AI systems are often deployed in socio-organizational contexts that require longitudinal use [20, 41, 43], such as product design [93], game design [4], and engineering [20, Section 3.”
Design Principles for Generative AI Applications (2024, methodology): “…category (a) would disappear (e.g. due to technological improvements), category (b) would remain and imperfection would still be a critical design issue. for real user needs like explainability [44, 166], and understanding the role of the AI in the co-creative process [37, 57, 106, 123, 148, 161].”
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2020, background): “…(e.g., Cooke et al., 2016; Cuevas et al., 2007; Glikson & Woolley, 2020; Larson & DeChurch, 2020; McNeese & McNeese, 2020; Myers et al., 2019; Seeber et al., 2020; Wynne & Lyons, 2018), and we along with others feel strongly that the existing HAI provides a vital foundation of knowledge from…”