When AI Joins the Team! A Model of How AI Adoption Relates To Social Patterns in Software Engineering Teams

• URL: http://arxiv.org/abs/2608.03462v1
• Date: Tue, 04 Aug 2026 10:57:42 GMT
• Title: When AI Joins the Team! A Model of How AI Adoption Relates To Social Patterns in Software Engineering Teams
• Abstract summary: It is unclear whether AI adoption is associated with an increase or reduction in community smells.<n>We validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools.
• Score: 11.247082068830915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context: The growing adoption of AI-assisted development tools is changing how software teams collaborate, share knowledge, and coordinate, yet its consequences for team social dynamics remain largely unexplored. Gap: It is unclear whether AI adoption is associated with an increase or reduction in community smells,socio-technical anti-patterns reflecting coordination and communication breakdowns,and through which mechanisms. Method: Grounded in Transactive Memory Systems (TMS) theory, we validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Specialization and Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools. Community smell constructs were derived from the literature and validated through expert surveys and factor analysis. Results: AI adoption relates to community smells not in a single way, but through mechanisms depending on the work. In specialization work, AI is associated with higher knowledge-sharing peer interaction, which is in turn associated with fewer smells. In coordination work, AI is directly associated with higher communication quality, complementing rather than replacing human interaction. Contributions: We provide an empirically validated, TMS-grounded model showing that the AIcommunity-smell relationship is contingent on the type of collaboration, with a reusable instrument and evidence-based implications for research and practice.

Related papers

• Characterizing initial human-AI proof formalization workflows [66.06866890889131]
  Advances in AI systems' ability to generate code and engage in increasingly high-level mathematical reasoning promise to transform people's ability to formalize and thereby verify proofs.<n>We conduct a mixed-methods analysis into the initial impact of AI on people's formalization.<n>A qualitative survey shows that people's preferences are diverse, but with a general desire for AI assistance in formalization.
  arXiv  Detail & Related papers  (2026-06-02T22:58:19Z)
• AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support [0.514825619161626]
  Current AI systems remain largely passive due to an overreliance on explainability-centric designs.<n> transitioning AI to an active teammate requires adaptive, context-aware interactions.
  arXiv  Detail & Related papers  (2026-01-26T19:18:50Z)
• Cooperation Through Indirect Reciprocity in Child-Robot Interactions [81.62347137438248]
  We investigate whether indirect reciprocity can be transposed to children-robot interactions.<n>We find that IR extends to children and robots solving coordination dilemmas.<n>We observe that cooperating through multi-armed bandit algorithms is highly dependent on the strategies revealed by humans.
  arXiv  Detail & Related papers  (2025-11-07T07:08:32Z)
• Vibe Coding: Is Human Nature the Ghost in the Machine? [0.0]
  We analyzed three "vibe coding" sessions between a human product lead and an AI software engineer.<n>We investigated similarities and differences in team dynamics, communication patterns, and development outcomes.<n>To our surprise, later conversations revealed that the AI agent had systematically misrepresented its accomplishments.
  arXiv  Detail & Related papers  (2025-08-28T15:48:48Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• ChatCollab: Exploring Collaboration Between Humans and AI Agents in Software Teams [1.3967206132709542]
  ChatCollab's novel architecture allows agents - human or AI - to join collaborations in any role.<n>Using software engineering as a case study, we find that our AI agents successfully identify their roles and responsibilities.<n>In relation to three prior multi-agent AI systems for software development, we find ChatCollab AI agents produce comparable or better software in an interactive game development task.
  arXiv  Detail & Related papers  (2024-12-02T21:56:46Z)
• Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
  Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
  arXiv  Detail & Related papers  (2024-09-13T13:19:48Z)
• AI's Social Forcefield: Reshaping Distributed Cognition in Human-AI Teams [6.386909552513031]
  We show that AI actively reshapes the social and cognitive fabric of collaboration.<n>We show that AI participation reorganizes the distributed cognitive architecture of teams.<n>We argue for rethinking AI in teams as a socially influential actor.
  arXiv  Detail & Related papers  (2024-07-03T13:46:00Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study [11.633108017016985]
  The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers. During these collaborations, there is a knowledge mismatch between AI developers, who are skilled in data science, and external stakeholders who are typically not. This difference leads to communication gaps, and the onus falls on AI developers to explain data science concepts to their collaborators.
  arXiv  Detail & Related papers  (2021-01-13T19:33:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.