# A survey of generative AI adoption and perceived productivity among scientists who program

arXiv: https://arxiv.org/abs/2512.19644 · HTML: https://mlsanity.com/paper/2512.19644/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2025.

## Built on by (most cited first, 3 of 3)

- [scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns](https://mlsanity.com/paper/2603.17893/) (2026)
- [LLMoxie: Exploring Agentic AI for Scientific Software Development](https://mlsanity.com/paper/2607.02703/) (2026)
- [Aleena: Alignment Agent for Research Software Engineering Collaborations](https://mlsanity.com/paper/2607.08043/) (2026)

## Abstract (arXiv)

Programming is essential to modern scientific research, yet most scientists report inadequate training for the software development their work demands. Generative AI tools capable of code generation may support scientific programmers, but user studies indicate risks of over-reliance, particularly among inexperienced users. We surveyed 868 scientists who program, examining adoption patterns, tool preferences, and factors associated with perceived productivity. Adoption is highest among students and less experienced programmers, with variation across fields. Scientific programmers overwhelmingly prefer general-purpose conversational interfaces like ChatGPT over developer-specific tools. Both inexperience and limited use of development practices (like testing, code review, and version control) are associated with greater perceived productivity -- but these factors interact, suggesting formal practices may partially compensate for inexperience. The strongest predictor of perceived productivity is the number of lines of generated code typically accepted at once. These findings suggest scientific programmers using generative AI may gauge productivity by code generation rather than validation.