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

2025-04-29

AI Summary

This study investigates how developers’ role perceptions of AI development tools—conceptualized either as “tools” or “human-like teammates”—influence their technology acceptance. Method: Drawing on 38 in-depth interviews and a survey of 102 practitioners, the research employs thematic analysis and factor analysis to systematically characterize developers’ mental models of AI in software engineering. Contribution/Results: It identifies, for the first time, two distinct mental models—tool-oriented and partner-oriented—and a two-dimensional role framework (supportive vs. expert). Findings reveal that attributing multiple complementary roles (e.g., assistant, advisor) to AI significantly enhances perceived usefulness and ease of use, and positively predicts adoption intention. Based on these insights, the study proposes adaptive design principles for AI4SE tools tailored to users’ mental models, thereby advancing theory and practice for human-centered intelligent development environments.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: AI & Epistemology

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

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.

Problem

• How developers view AI tools' roles in software engineering
• Impact of AI role perceptions on technology adoption
• Design strategies for AI tools to match user mental models

Innovation

• Identifies AI roles as tool and teammate
• Groups AI roles into Support and Expert
• Links multiple AI roles to adoption benefits