Contemporary Education and Teaching Research 2737-4203 2737-4335 BONI FUTURE DIGITAL PUBLISHING CO.,LIMITED https://ojs.bonfuturepress.com/index.php/CETR/article/view/2049 7 10 2026 2026-10-30 From Perceived Attributes to Reported Use: Generative AI Adoption for Programming-Related Learning among Taiwanese Undergraduates in Information-Related Programs Tien-Chi Huang,Pei-Shan Wu,Yi-Wen Huang,Hsin-Ping Tseng,Yu-Hung Chiang 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. generative artificial intelligence,programming-related learning,diffusion of innovations,self-directed learning,cognitive load 10.61360/BoniCETR262020491003

Alamri, J. M. (2025). Antecedents of generative artificial intelligence technology adoption: Extended innovation of diffusion model with cultural dimensions and risks perceptions. Journal of Ecohumanism, 4(1), 1718–1738. https://doi.org/10.62754/joe.v4i1.5992 Alpizar-Chacon, I., & Keuning, H. (2025). Student's use of generative AI as a support tool in an advanced web development course. In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 312–318). Association for Computing Machinery. https://doi.org/10.1145/3724363.3729106 Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122 Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. Chen, J., Mokmin, N. A. M., & Qi, S. (2025). Generative AI-powered arts-based learning in middle school history: Impact on achievement, motivation, and cognitive load. The Journal of Educational Research, 118(6), 688–700. https://doi.org/10.1080/00220671.2025.2510395 Chiu, M.-L. (2025). Exploring user awareness and perceived usefulness of generative AI in higher education: The moderating role of trust. Education and Information Technologies, 30(15), 21317–21351. https://doi.org/10.1007/s10639-025-13612-7 Granić, A. (2025). Emerging drivers of adoption of generative AI technology in education: A review. Applied Sciences, 15(13), Article 6968. https://doi.org/10.3390/app15136968 Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7 Haque, S., & Hundhausen, C. (2025). Generative AI access, usage, and perceptions: An empirical comparison of computing students in the United States and Bangladesh. In Proceedings of the 2025 ACM Conference on International Computing Education Research V. 1 (pp. 109–124). Association for Computing Machinery. https://doi.org/10.1145/3702652.3744231 Huang, T. C., Chen, K. Y., Huang, Y. M., & Chiang, Y. H. (2026). Cognitive and behavioral determinants of ChatGPT adoption in education: An integrated theoretical approach. Interactive Learning Environments, 34(5), 3020–3040. https://doi.org/10.1080/10494820.2025.2559918 Jin, F., Lin, C.-H., & Lai, C. (2025). Modeling AI-assisted writing: How self-regulated learning influences writing outcomes. Computers in Human Behavior, 165, Article 108538. https://doi.org/10.1016/j.chb.2024.108538 Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., & Grossman, T. (2023). Studying the effect of AI code generators on supporting novice learners in introductory programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Article 455, pp. 1–23). Association for Computing Machinery. https://doi.org/10.1145/3544548.3580919 Kazemitabaar, M., Ye, R., Wang, X., Henley, A. Z., Denny, P., Craig, M., & Grossman, T. (2024). CodeAid: Evaluating a classroom deployment of an LLM-based programming assistant that balances student and educator needs. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Article 650, pp. 1–20). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642773 Klingbeil, A., Grützner, C., & Schreck, P. (2024). Trust and reliance on AI—An experimental study on the extent and costs of overreliance on AI. Computers in Human Behavior, 160, Article 108352. https://doi.org/10.1016/j.chb.2024.108352 Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101 Lai, J. W. (2024). Adapting self-regulated learning in an age of generative artificial intelligence chatbots. Future Internet, 16(6), Article 218. https://doi.org/10.3390/fi16060218 Li, K. (2023). Determinants of college students’ actual use of AI-based systems: An extension of the technology acceptance model. Sustainability, 15(6), Article 5221. https://doi.org/10.3390/su15065221 Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2013). Accurate intelligible models with pairwise interactions. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 623–631). Association for Computing Machinery. https://doi.org/10.1145/2487575.2487579 Luera, R., Rossi, R. A., Siu, A., Dernoncourt, F., Yu, T., Kim, S., Zhang, R., Chen, X., Salehy, H., Zhao, J., Basu, S., Mathur, P., & Lipka, N. (2024). Survey of user interface design and interaction techniques in generative AI applications [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2410.22370 Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192 Nori, H., Jenkins, S., Koch, P., & Caruana, R. (2019). InterpretML: A unified framework for machine learning interpretability [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1909.09223 Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill. Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024). The widening gap: The benefits and harms of generative AI for novice programmers. In Proceedings of the 2024 ACM Conference on International Computing Education Research—Volume 1 (pp. 469–486). Association for Computing Machinery. https://doi.org/10.1145/3632620.3671116 Raman, R., Mandal, S., Das, P., Kaur, T., Sanjanasri, J. P., & Nedungadi, P. (2024). Exploring university students’ adoption of ChatGPT using the diffusion of innovation theory and sentiment analysis with gender dimension. Human Behavior and Emerging Technologies, 2024(1), Article 3085910. https://doi.org/10.1155/2024/3085910 Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4 [Computer software]. SmartPLS. https://www.smartpls.com Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. Samala, A. D., Rawas, S., Wang, T., Reed, J. M., Kim, J., Howard, N. J., & Ertz, M. (2025). Unveiling the landscape of generative artificial intelligence in education: A comprehensive taxonomy of applications, challenges, and future prospects. Education and Information Technologies, 30(3), 3239–3278. https://doi.org/10.1007/s10639-024-12936-0 Sarsa, S., Denny, P., Hellas, A., & Leinonen, J. (2022). Automatic generation of programming exercises and code explanations using large language models. In Proceedings of the 2022 ACM Conference on International Computing Education Research—Volume 1 (pp. 27–43). Association for Computing Machinery. https://doi.org/10.1145/3501385.3543957 Shahzad, M. F., Xu, S., An, X., Asif, M., & Javed, I. (2025). Do generative AI technologies play a double-edged sword role in education? Findings from hybrid approach using PLS-SEM and fsQCA. Education and Information Technologies, 30(14), 19647–19676. https://doi.org/10.1007/s10639-025-13528-2 Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, Article 108386. https://doi.org/10.1016/j.chb.2024.108386 Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology, 55(3), 1209–1230. https://doi.org/10.1111/bjet.13425 Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7 Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2