Keywords
GenAI, Programming, HAI, Think-Aloud

Technologies
ChatGPT, Python, Directed Content Analysis, SPSS

This project investigates how software developers think and make decisions when using Generative AI (GenAI) tools such as ChatGPT. Using a mixed-methods design that combines experimental and qualitative approaches, participants completed programming tasks either with or without AI assistance while verbalising their thoughts. The study examines how developers' cognitive models of GenAI relate to their trust, self-efficacy, reliance, decision-making processes, and task satisfaction.

My individual contribution applies Hackman and Oldham's Job Characteristics Model to examine how AI assistance affects perceived task characteristics and satisfaction across six dimensions: skill variety, task identity, task significance, autonomy, feedback, and task satisfaction. An 18-item JCM survey was administered to all 20 participants, with quantitative comparisons supplemented by directed content analysis of think-aloud data. This contribution moves beyond productivity metrics to explore whether AI tools enhance or diminish the characteristics that make programming intrinsically motivating.