A Preliminary Study on the Impact of AI in the Creativity and Collaboration in Software Teams

• URL: http://arxiv.org/abs/2607.16744v1
• Date: Sat, 18 Jul 2026 10:07:56 GMT
• Title: A Preliminary Study on the Impact of AI in the Creativity and Collaboration in Software Teams
• Abstract summary: Generative artificial intelligence (GenAI) tools are reshaping software engineering work, increasingly acting as cognitive partners.<n>This paper examines how software engineering professionals perceive and integrate AI tools focusing on creativity and collaboration within teams.
• Score: 7.9711199981607885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative artificial intelligence (GenAI) tools are reshaping software engineering work, increasingly acting as cognitive partners rather than purely instrumental aids. Most AI tooling remains optimized for individual use, yet software development is inherently collaborative and creative, raising open questions about how AI affects team-based work. This paper examines how software engineering professionals perceive and integrate AI tools focusing on creativity and collaboration within teams. We conducted semi-structured interviews with 13 software professionals from four companies, each representing a distinct team. Our analysis identifies three central themes - AI Use, Consequences of AI, and Collaboration and Team Dynamics, alongside Emotions as a cross-cutting dimension. Key findings include: AI broadens developers' creative repertoires but may narrow independent ideation; developers increasingly consult AI instead of colleagues, weakening peer learning and mentoring; and teams are developing emerging triadic collaboration patterns in which developers, colleagues, and AI reason together.

Related papers

• How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior [8.956327421839205]
  We show how hidden intentions and emotional dimensions intertwine with concrete actions during AI-assisted programming.<n>Using AI assistants often makes developers more focused on actively creating code, evaluating, and verifying generated results.<n>Some developers reported impostor-like feelings, expressing guilt or self-doubt about relying on AI.
  arXiv  Detail & Related papers  (2026-03-28T14:37:19Z)
• "Can you feel the vibes?": An exploration of novice programmer engagement with vibe coding [42.82674998306379]
  "vibe coding" refers to creating software via natural language prompts rather than direct code authorship.<n>This paper reports on a one-day educational hackathon investigating how novice programmers and mixed-experience teams engage with vibe coding.
  arXiv  Detail & Related papers  (2025-12-02T13:32:23Z)
• A Viable Paradigm of Software Automation: Iterative End-to-End Automated Software Development [41.295627885484855]
  We present a vision of an iterative end-to-end automated software development paradigm AutoSW.<n>It operates in an analyze-plan-implement-deliver loop, where AI systems as human partners become first-class actors.<n>The results indicate that AutoSW can successfully deliver executable software.
  arXiv  Detail & Related papers  (2025-11-19T09:57:49Z)
• Look: AI at Work! - Analysing Key Aspects of AI-support at the Work Place [39.146761527401424]
  We look at the areas of AI that the applications are concerned with.<n>This includes the importance of high-quality data for training learning-based systems.<n>In terms of the psychological factors we derive research questions to investigate in the development of AI supported work systems.
  arXiv  Detail & Related papers  (2025-09-02T12:51:23Z)
• The SPACE of AI: Real-World Lessons on AI's Impact on Developers [0.807084206814932]
  We study how developers perceive AI's influence across the dimensions of the SPACE framework: Satisfaction, Performance, Activity, Collaboration and Efficiency.<n>We find that AI is broadly adopted and widely seen as enhancing productivity, particularly for routine tasks.<n>Developers report increased efficiency and satisfaction, with less evidence of impact on collaboration.
  arXiv  Detail & Related papers  (2025-07-31T21:45:54Z)
• How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering [8.65285948382426]
  We propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types.<n>Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development.
  arXiv  Detail & Related papers  (2025-01-15T12:53:49Z)
• The AI Collaborator: Bridging Human-AI Interaction in Educational and Professional Settings [3.506120162002989]
  AI Collaborator, powered by OpenAI's GPT-4, is a groundbreaking tool designed for human-AI collaboration research. Its standout feature is the ability for researchers to create customized AI personas for diverse experimental setups. This functionality is essential for simulating various interpersonal dynamics in team settings.
  arXiv  Detail & Related papers  (2024-05-16T22:14:54Z)
• Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
  We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)<n>We identify benefits, harms, and future research directions for the four main themes.<n>We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Human AI Collaboration in Software Engineering: Lessons Learned from a Hands On Workshop [1.14603174659129]
  The study identifies key themes such as the evolving nature of human AI interaction, the capabilities of AI in software engineering tasks, and the challenges and limitations of integrating AI in this domain. The findings show that while AI, particularly ChatGPT, improves the efficiency of code generation and optimization, human oversight remains crucial, especially in areas requiring complex problem solving and security considerations.
  arXiv  Detail & Related papers  (2023-12-17T06:31:05Z)
• Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration [39.96202614397779]
  Recent research has explored the potential for artificial intelligence to support and augment humans in data storytelling. There lacks a systematic review to understand data storytelling tools from the perspective of human-AI collaboration. This paper investigated existing tools with a framework from two perspectives: the stages in the storytelling workflow where a tool serves, including analysis, planning, implementation, and communication, and the roles of humans and AI.
  arXiv  Detail & Related papers  (2023-09-27T15:30:50Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork [54.309495231017344]
  We argue that AI systems should be trained in a human-centered manner, directly optimized for team performance. We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves. Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance.
  arXiv  Detail & Related papers  (2020-04-27T19:06:28Z)

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.