From Perceived Attributes to Reported Use: Generative AI Adoption for Programming-Related Learning among Taiwanese Undergraduates in Information-Related Programs
DOI:
https://doi.org/10.61360/BoniCETR262020491003Keywords:
generative artificial intelligence, programming-related learning, diffusion of innovations, self-directed learning, cognitive loadAbstract
Generative AI is increasingly used in programming-related learning, but favorable perceptions do not necessarily correspond to reported use or better learning. Drawing on Diffusion of Innovations theory and psychological boundary conditions, this cross-sectional survey examined associations among five perceived innovation attributes, usage intention, and self-reported generative AI-assisted programming activity among 1,034 Taiwanese undergraduates in information-related programs who reported prior generative AI experience. The reported-use construct referred to three programming-specific activities during the preceding four weeks and did not assess code quality, conceptual understanding, transfer, or independent programming ability. Partial least squares structural equation modeling was complemented with Explainable Boosting Machine (EBM) regression. Trialability, relative advantage, compatibility, and observability were positively associated with intention; low complexity was not significant in the linear model. Intention was positively associated with reported use, and this association was stronger at higher self-directed learning levels and weaker at higher cognitive load. EBM described model-specific nonlinear predictive patterns. Because the measures were cross-sectional and self-reported, the results characterize adoption behavior rather than learning effects.
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