@article {10.3844/jcssp.2026.938.946,
article_type = {journal},
title = {Exploring University Students' Perceptions of AI Tools in Programming Education: An Extended TAM Approach},
author = {Alfuhaid, Areej Abdullah},
volume = {22},
number = {3},
year = {2026},
month = {Mar},
pages = {938-946},
doi = {10.3844/jcssp.2026.938.946},
url = {https://thescipub.com/abstract/jcssp.2026.938.946},
abstract = {Generative Artificial Intelligence (AI) is commonly used in programming education. Tools such as ChatGPT and GitHub Copilot aid in code generation, debugging, and explanation. The benefits are clear, though concerns about learning quality and academic integrity persist.We tested an extended Technology Acceptance Model (TAM) for university programming courses. The model included perceived usefulness and perceived ease of use. It added programming experience, perceived impact on learning, social influence, and ethical awareness, and compared current users with non-users. We conducted a cross-sectional online survey with purposive sampling. Participants were 107 students in programming-related courses. A Likert-scale instrument was used to capture all constructs and demographics. We applied descriptive statistics, Cronbach's alpha, Pearson’s correlations, and multiple linear regressions. Ethical approval was obtained from all participants. Females comprised 63.6 percent of the sample. General AI use was common; however, most students did not use AI for coding. The reliability was excellent for users and moderate for non-users. Among the users, ease of use was strongly related to usefulness, and both were related to intention. Programming experience pertaining to ease of use: Perceived impact on learning related to usefulness. Social influence exhibited the strongest link to intention. In the regression, usefulness and social influence were positive predictors of intention, while ethical awareness was a negative predictor. The model explained 81.5 percent of the variance in intentions. Adoption of programming courses depends most on perceived usefulness and supportive norms. Ethical concerns can suppress intentions when guidance is unclear. Ease and experience matter primarily through their effects on usefulness. Programs should pair hands-on AI activities with explicit integrity rules and instructor modeling to convert perceived value into responsible use.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}