"Maybe We Need Some More Examples:" Individual and Team Drivers of Developer GenAI Tool Use

2025-07-28

AI Summary

Uneven adoption of generative AI tools in software engineering impedes productivity gains and triggers role ambiguity. This study investigates individual- and team-level drivers of AI tool usage, identifying a “productivity pressure paradox”: organizational expectations for rapid efficiency gains—without commensurate learning support—can undermine actual effectiveness. Using paired interviews, we conducted qualitative analysis with 54 developers across 27 teams (matched high- and low-frequency users). Findings reveal usage disparities stem from three interrelated cognitive–behavioral dimensions: conceptualization of AI’s role (collaborator vs. replacement), propensity for exploratory learning, and perseverance when confronting challenges such as debugging failures. This framework advances theoretical understanding of the AI adoption gap and provides empirically grounded guidance for designing supportive human–AI collaboration practices in software organizations.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Despite the widespread availability of generative AI tools in software engineering, developer adoption remains uneven. This unevenness is problematic because it hampers productivity efforts, frustrates management's expectations, and creates uncertainty around the future roles of developers. Through paired interviews with 54 developers across 27 teams -- one frequent and one infrequent user per team -- we demonstrate that differences in usage result primarily from how developers perceive the tool (as a collaborator vs. feature), their engagement approach (experimental vs. conservative), and how they respond when encountering challenges (with adaptive persistence vs. quick abandonment). Our findings imply that widespread organizational expectations for rapid productivity gains without sufficient investment in learning support creates a "Productivity Pressure Paradox," undermining the very productivity benefits that motivate adoption.

Problem

• Uneven developer adoption of generative AI tools
• Productivity hampered by lack of learning support
• Differing developer perceptions and engagement approaches

Innovation

• Paired interviews with 54 developers
• Analyze perception, engagement, and challenge response
• Identify Productivity Pressure Paradox impact