Paper Type
Complete
Abstract
Generative Artificial Intelligence (GenAI) is transforming professional workflows, particularly in programming, where tools like ChatGPT assist with code generation, debugging, and explanations. While GenAI enhances performance, concerns about the implications for well-being and emotions while working with GenAI persist. Especially emotions in terms of positive and negative feelings play a crucial role, influencing how effectively GenAI is utilized in professional settings. Our research explores the impact of GenAI on emotions of employees in a programming context. We conducted an online experiment with 161 Python programmers, assessing performance and positive and negative feelings with and without ChatGPT assistance on two different programming tasks by performing paired t-tests and a structural model analysis. The findings indicate that ChatGPT not only significantly improves performance but also demonstrates a link between positive emotions and enhanced outcomes. These findings highlight the importance of technical and emotional factors in maximizing the potential of human-AI collaboration.
Paper Number
2317
Recommended Citation
Jacquemin, Philippe Herve; Gräf, Miriam; Bauch, Kristin; Kaur, Avleen; and Mehler, Maren, "When Feelings Meet Code: How Generative AI Affects the Emotions of Developers" (2025). AMCIS 2025 Proceedings. 4.
https://aisel.aisnet.org/amcis2025/sig_cnow/sig_cnow/4
When Feelings Meet Code: How Generative AI Affects the Emotions of Developers
Generative Artificial Intelligence (GenAI) is transforming professional workflows, particularly in programming, where tools like ChatGPT assist with code generation, debugging, and explanations. While GenAI enhances performance, concerns about the implications for well-being and emotions while working with GenAI persist. Especially emotions in terms of positive and negative feelings play a crucial role, influencing how effectively GenAI is utilized in professional settings. Our research explores the impact of GenAI on emotions of employees in a programming context. We conducted an online experiment with 161 Python programmers, assessing performance and positive and negative feelings with and without ChatGPT assistance on two different programming tasks by performing paired t-tests and a structural model analysis. The findings indicate that ChatGPT not only significantly improves performance but also demonstrates a link between positive emotions and enhanced outcomes. These findings highlight the importance of technical and emotional factors in maximizing the potential of human-AI collaboration.
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