The Psychological Costs of Artificial Intelligence Adoption in Software Engineering

• URL: http://arxiv.org/abs/2609.03456v1
• Date: Thu, 03 Sep 2026 07:15:05 GMT
• Title: The Psychological Costs of Artificial Intelligence Adoption in Software Engineering
• Abstract summary: AI is a disruptive force, introduced into settings where role identity, team norms, and the sources of job satisfaction were well established.<n>We carried out a case study in a large software development services company, one year after the company launched its AI adoption.<n>We found that software professionals experience accountability anxiety, craft identity disruption, meaning and satisfaction erosion, cognitive and workload intensification, and uncertainty distress.
• Score: 1.973459081604455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) is increasingly used to augment software engineering (SE) workflows. While code generation remains the main use case, organizations are actively seeking AI integration in other practices such as test cases generation and code reviews. Organizational AI adoption strategies seem to focus on tangible outcomes such as productivity. However, AI is a disruptive force, introduced into settings where role identity, team norms, and the sources of job satisfaction were well established before the recent advances in generative AI. Historically, technological disruptions have caused psychological and social strains in workplaces, ranging from anxiety and eroded meaning to deskilling and disrupted professional identities. The assumption that AI for SE is cost-free may not be accurate. Therefore, in this study we sought to understand the psychological costs software professionals experience during organizational AI adoption. We carried out a case study in a large software development services company, one year after the company launched its AI adoption. We collected qualitative data through meetings and semi-structured interviews (N = 21). We found that software professionals experience accountability anxiety, craft identity disruption, meaning and satisfaction erosion, cognitive and workload intensification, and uncertainty distress. Practitioners manage these costs through practices that restore control, mitigate them through protective and identity-preserving adaptations, or absorb them, carrying what neither can resolve. We contribute to AI-human collaboration in SE by repositioning AI adoption as a human transition, not only a technological and organizational one.

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)
• Safety First: Psychological Safety as the Key to AI Transformation [0.36944296923226316]
  This study examines whether psychological safety is associated with AI adoption and usage in the workplace.<n> Logistic and linear regression analyses show that psychological safety reliably predicts whether employees adopt AI tools.<n>The study underscores the need to distinguish between adoption and sustained use.
  arXiv  Detail & Related papers  (2026-02-26T17:53:37Z)
• Developers in the Age of AI: Adoption, Policy, and Diffusion of AI Software Engineering Tools [0.0]
  We study the usage patterns of 147 professional developers.<n>We find no perceptual support for the Quality Paradox.<n>Security concerns remain a moderate and statistically significant barrier to adoption.
  arXiv  Detail & Related papers  (2026-01-29T05:56:35Z)
• 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)
• Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science [48.38628297686686]
  Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture.<n>Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has become an indispensable tool for advancing cognitive research.<n>We argue that the future of AI within Cognitive Science lies not only in improving performance but also in constructing systems that deepen our understanding of the human mind.
  arXiv  Detail & Related papers  (2025-08-28T11:26:17Z)
• How Performance Pressure Influences AI-Assisted Decision Making [52.997197698288936]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs [10.844598404826355]
  One-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs requiring a graduate or postgraduate level of education.<n>Even in high-skill occupations, AI exhibits high variability in task substitution, suggesting that AI and humans complement each other within the same occupation.<n>All results, models, and code are freely available online to allow the community to reproduce our results, compare outcomes, and use our work as a benchmark to monitor AI's progress over time.
  arXiv  Detail & Related papers  (2024-07-27T08:14:18Z)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect [0.0]
  The study aims to recognize and explain some of the biggest challenges software testers face while applying AI to testing. The paper also proposes some key contributions of AI in the future to the domain of software testing.
  arXiv  Detail & Related papers  (2022-01-14T10:21:51Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• 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.