AI Where It Matters: Where, Why, and How Developers Want AI Support in Daily Work
Open accessAbstract
Generative AI is reshaping software work, yet we lack clear guidance on where developers most need support and how to design it responsibly. We report a large-scale, mixed-methods study of N=860 developers examining where, why, and how they seek or limit AI across SE tasks. Using cognitive appraisal theory, we provide the first empirically validated mapping of developers’ task appraisals to AI adoption patterns and Responsible AI (RAI) priorities. Appraisals predict AI openness and use, revealing distinct patterns: strong current use and demand for improvement in core work (e.g., coding, testing); high demand to reduce toil (e.g., documentation, operations); and clear limits for identity- and relationship-centric work (e.g., mentoring). RAI priorities vary by context: reliability and security for systems-facing tasks; transparency, alignment, and steerability to maintain control; and fairness and inclusiveness for human-facing work. Our results offer concrete, contextual guidance for delivering AI where it matters to developers and their work.
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A survey of 860 developers found that practitioners resist AI assistance in mentoring and identity-centric tasks.
“Choudhuri et al.'s (2025) survey of 860 developers found that practitioners resist AI assistance in mentoring and identity-centric tasks, suggesting that workforce preferences may naturally preserve some domains of independent cognitive engagement.”
From The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise (2026) · cites this paper as Choudhuri et al.'s (2025)
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Lovett, N. (2026) · Human Resource Development Review · doi:10.1177/15344843261470602 · Cited by 7 · see citing works
- Authors
- Choudhuri, R., Badea, C., Bird, C., Butler, J. L., DeLine, R., & Houck, B.
- Affiliations
- Oregon State University (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the IEEE/ACM 48th International Conference on Software Engineering: Software Engineering in Practice
- Publisher
- ACM
- Published
- 2026-04-12
- Pages
- 428-439
- OpenAlex
- W7168018038
- Cited by
- 1
- References
- 13
- Citations by year
- 2026: 1
- FWCI
- 10.67
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI in software development, appraisal theory, responsible AI, mentoring, software testing, software documentation
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