How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior

Artificial Intelligence (AI) is reshaping how developers adopt software engineering practices, yet the multi-dimensional nature of developer-AI interaction remains under-explored. Prior studies have primarily examined dimensions observable from developer activities such as “Prompt Crafting” and “Code Editing,” overlooking how hidden intentions and emotional dimensions intertwine with concrete actions during AI-assisted programming. Understanding the interplay is essential for improving developer experience and future AI assistant designs. To understand this phenomenon, we conducted a mixed-methods study with 76 developers. We first split developers into AI-assisted and non-AI groups. Each developer performed a programming task (either Python with API management or Java with SQL). Developers retrospectively labeled their self-reported intentions, tool-supported actions, and emotions (on a 7-point valence scale) from screen recordings, supplemented by participant surveys and interviews. Our user study resulted in a novel model, named S-IASE, with four dimensions to describe programming behavior for a given development state: intention, action, supporting tool, and emotion. Our analysis reveals several aggregated and sequential behavioral patterns. For example, for aggregated patterns, using AI assistants often led developers to focus more on actively “creating” code, evaluating, and verifying the generated results; for sequential patterns, AI-assisted participants showed emotionally stable development flows, as opposed to non-AI-assisted participants who experienced more fluctuating emotions. Interviews revealed further nuance: some developers reported impostor-like feelings, expressing guilt or self-doubt about relying on AI for programming. The uncovered patterns indicate that our model can provide actionable insights for improving AI assistants’ responsiveness, training developers in AI collaboration, and designing developer-centric AI studies. Our work bridges an important gap in understanding the complexities of developer-AI interaction in the programming context and sheds light on future developer-centric research directions.