# AI in Software Engineering: Perceived Roles and Their Impact on Adoption

arXiv: https://arxiv.org/abs/2504.20329 · HTML: https://mlsanity.com/paper/2504.20329/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2025.

## Built on by (most cited first, 5 of 5)

- [Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study](https://mlsanity.com/paper/2602.00164/) (2026)
- [Towards Green AI: Decoding the Energy of LLM Inference in Software Development](https://mlsanity.com/paper/2602.05712/) (2026)
- [Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study](https://mlsanity.com/paper/2601.16700/) (2026)
- [SoK: From Generation to Consumption of Privacy Documents in Software Systems](https://mlsanity.com/paper/2608.12511/) (2026)
- [The Psychological Costs of Artificial Intelligence Adoption in Software Engineering](https://mlsanity.com/paper/2609.03456/) (2026)

## Abstract (arXiv)

This paper investigates how developers conceptualize AI-powered Development Tools and how these role attributions influence technology acceptance. Through qualitative analysis of 38 interviews and a quantitative survey with 102 participants, we identify two primary Mental Models: AI as an inanimate tool and AI as a human-like teammate. Factor analysis further groups AI roles into Support Roles (e.g., assistant, reference guide) and Expert Roles (e.g., advisor, problem solver). We find that assigning multiple roles to AI correlates positively with Perceived Usefulness and Perceived Ease of Use, indicating that diverse conceptualizations enhance AI adoption. These insights suggest that AI4SE tools should accommodate varying user expectations through adaptive design strategies that align with different Mental Models.