Collaboration with Intelligent Systems: Machines as Teammates
Permanent URI for this collectionhttps://hdl.handle.net/10125/112402
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Item type: Item , Idea Generation With a Majority of AI Teammates: Exploring Productivity Effects, Process Gains and Process Losses(2026-01-06) Yuan, Bithiah; Specker, Richard; Schwabe, GerhardLeveraging AI agents as teammates has gained momentum in research and practice. Yet, how human team members react to multiple AI teammates (AITMs), particularly when they are the majority, is underexplored. Therefore, we conducted an exploratory study applying experimental methods, in which 66 participants collaborated in teams of three humans and four AITMs on an idea generation task. Our results show that the AITMs were highly productive, with half of the top ideas being AI-generated, and contributed to process gains for human team members. However, we also observed process losses, such as attention blocking or reduced human self-efficacy. While participants were open to future collaboration with multiple AITMs, they preferred team compositions with an equal or fewer number of AITMs than humans. We conclude that an optimal team composition balances productivity gains with the need to mitigate process losses that arise in AI-majority teams and maintain human engagement.Item type: Item , From Automation to Collaboration: A Systematic Review of AI Use in Assessment Across Critical Infrastructure Sectors(2026-01-06) Heldridge, James; Benda, Angie; Hunter, Sam; Elson, JoelAssessments are used to help gather and analyze information to inform processes and outcomes and are rapidly being reshaped by AI. This systematic review investigates where, why, and when AI is used across the assessment life-cycle and further considers its core functions, design elements, and the ways users engage with them Thirty-eight peer-reviewed studies met our inclusion criteria, each embedding artificial intelligence directly into the assessment process. Together, government facilities and healthcare settings accounted for more than 70% of all documented use cases. Across sectors, the prevailing role of AI was that of a digital assistant, streamlining knowledge capture and evaluation supporting assessment in its role as an expert with a focus on goal-oriented collaboration. These patterns illuminate both the breadth of adoption and the potential of AI as an augmentative partner, offering a roadmap for future assessment design and research.Item type: Item , AI Teammates: Silverbacks, Quarterbacks or Knick-Knacks? The Effect of AI Teammates on Humans’ Status Perceptions and Intention to Collaborate(2026-01-06) Matziou, Georgios; Gao, YutingArtificial Intelligence (AI) based teammates are being used by organizations to enhance working teams. Research on the impacts that such AI teammates have on their human coworkers is still developing. This study explores whether and how the formation of human-AI teams (HATs) impacts humans’ intra-group status perceptions and their intention to collaborate with a new AI teammate. We propose that AI teammates will be attributed a status based on high capability and low prosocial behavior perceptions. Further, human incumbents, who perceive their own status to be based on competence and integrity, will exhibit zero-sum beliefs that negatively influence their collaboration intention with the new AI teammate, while prosocial oriented incumbents will rather exhibit increased levels of collaboration intention with the AI teammate. Hypotheses will be tested with data gathered via a 2 by 2 between-subject experiment. We expect to enhance the understanding of human-AI collaboration and the effects of status dynamics within HATs.Item type: Item , Beyond System Features: The Influence of Perfect Automation Schema on Trust Behaviors(2026-01-06) Meyers, Scott; Harris, Krista; Capiola, August; Alarcon, Gene; Jessup, SarahHuman-machine interactions are becoming increasingly multifaceted. Appropriate human trust in these contexts is critical, given these systems’ capabilities and opacities. Behavioral proxies of trust provide a lens to infer psychological trust while quantifying human behavior directly. However, it remains important to investigate the role of users’ individual differences in human-machine interaction on behaviors even after the impact of system features are accounted for. One such individual difference is perfect automation schema, a construct which comprises high expectations and all-or-none thinking. In two experiments, we explored the influence of these factors on trust-relevant behaviors in human-machine interactions. Results show factors of perfect automation schema accounted for unique variance in users’ behaviors beyond manipulations. Some patterns of results (mis)align with postulates from the Human Factors literature, and the nuance of context and machine referent are discussed for future research.Item type: Item , Redefining Team Processes in Human-AI Collaboration: A Mixed-Methods Study Across Team Phases(2026-01-06) Graupner, Emma; Fleischmann, Carolin; Cardon, PeterArtificial Intelligence (AI) agents introduce new challenges and opportunities across team processes. This study examines the impact of AI on transition, action, and interpersonal phases, and explores team processes central to human-AI collaboration. We surveyed 632 global virtual team members and interviewed nine AI experts. Survey results reveal that AI is seen as most useful during transition phases, less in action, and least in interpersonal phases. Usefulness ratings declined over time across all phases, especially interpersonal, indicating unmet expectations. Experts valued AI in action phases but expressed concerns about losing control during transition phases. Extending an established team processes model, we identify six processes and practices that foster effective human-AI collaboration: AI introduction, expectation management, positive storytelling, change management, navigating role shifts, and social interaction. Our findings highlight the need for targeted strategies to support these processes and manage perceptions of AI in teams.Item type: Item , Uneven but Better? Unequal AI Access Leads to Greater Dominant Style Differences and Enhanced Team Communication Effectiveness(2026-01-06) Han, Jiaxuan; Ren, RuqinThe widespread use of generative AI (GenAI) is seen as beneficial for team collaboration, yet full access for all members is often impractical in real-world settings. This study investigates how varying levels of GenAI integration influence team communication dynamics: no access (team members do not use AI), unequal access (only some members use AI), and full access (all members use AI). In a laboratory experiment with 60 two-person teams, all teams first performed a task without AI, then were randomly assigned to either the unequal or full access condition. Under unequal access, AI users adopted more dominant communication styles, creating greater style differences that, in turn, enhanced team communication effectiveness compared to both no access and full access. This research provides theoretical insights into the effects of varied GenAI integration structures on human-AI collaboration and offers practical guidance for optimizing team design in human-agent systems.Item type: Item , Understanding How Professionals Integrate Generative AI into Team Meetings: A Qualitative Study(2026-01-06) Schätzle, Anna; Abdo, Marin; Gräf, Miriam; Buxmann, PeterTeam meetings are essential for coordination, knowledge exchange, and decision-making in organizations. As Generative AI (GenAI) becomes increasingly embedded in collaborative work, its role in shaping team dynamics remains underexplored. This study examines how professionals experience the integration of GenAI in team meetings and how it affects collaboration. We conducted five focus groups with 20 experienced users to explore GenAI’s impact in real contexts. Our analysis shows that GenAI does not merely automate routine work but actively influences participation patterns, negotiation of cognitive demands, and the redefinition of role boundaries. Distinctive mechanisms include altered entry pathways for junior employees and the reshaping of information flows within meetings—insights that extend beyond assumptions of efficiency gains. While participants reported benefits such as reduced administrative burden and faster onboarding, they also highlighted challenges in transparency and interaction. The study enriches collaboration research and offers practical guidance for integrating GenAI into meeting routines.Item type: Item , Enhancing Complementary Team Performance through Intelligent Helping(2026-01-06) Goutier, Marc; Diebel, Christopher; Adam, Martin; Benlian, AlexanderEffectively leveraging artificial intelligence (AI) requires aligning human reliance on AI with humans’ cognitive strengths and limitations. Despite the objective advantages of AI in performing specific tasks, humans often struggle to correctly rely on help from AI to maximize the Complementary Team Performance (CTP), exceeding the individual performances by either humans or AI. To address this challenge, we propose the design feature Intelligent Helping, which steers human behavior by strategically providing different types of AI help and thus aligning reliance with AI’s superior judgment while ensuring that humans retain full control. We designed an experiment with four archetypes of task performances and four treatments to modulate human interaction with AI. Our results show that reliance on AI can be shaped by tailored treatments, significantly improving CTP. Intelligent Helping enables high-performing AI in which humans retain full control over decisions, providing new opportunities for effective collaboration between humans and AI.Item type: Item , Introduction to the Minitrack on Collaboration with Intelligent Systems: Machines as Teammates(2026-01-06) Elson, Joel; Seeber, Isabella; Mullins, Ryan; Oberhofer, Viviana