The Impact of Generative Artificial Intelligence on College Students’ Computer Thinking in the Task of Complex Computer Programming: Based on Social Cognitive Theory — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
The Impact of Generative Artificial Intelligence on College Students’ Computer Thinking in the Task of Complex Computer Programming: Based on Social Cognitive Theory
Department of Information Engineering, Inner Mongolia Mechanical and Electrical Vocational Technical College, Hohhot, China
1 Department of Information Engineering, Inner Mongolia Mechanical and Electrical Vocational Technical College, Hohhot, China
Generative Artificial Intelligence (GAI) is rapidly reshaping programming education, yet little is known about how college students cognitively, emotionally, and behaviorally engage with AI during complex programming tasks. Grounded in Social Cognitive Theory, this qualitative study explores how the use of GAI influences computational thinking (CT) among 16 undergraduates enrolled in a 15-week programming course supported by AI tools. Through semi-structured interviews and thematic analysis, five core themes emerged: (1) Cognitive Processing, in which AI scaffolds task decomposition, logical reasoning, multi-solution comparison, and iterative debugging while also introducing risks of cognitive substitution; (2) Emotional Experience, characterized by fluctuating self-efficacy, reduced frustration, heightened motivation, and anxiety triggered by AI errors or unpredictability; (3) Behavioral Strategies, where students adopt hybrid human-AI problem-solving pathways involving independent attempts, structured prompting, verification loops, and comparison-based reasoning; (4) Human-AI Collaboration Models, reflecting dynamic role negotiation in which students act as planners and evaluators while AI functions as solver, debugger, and explainer; and (5) Limitations and Risks, including error accumulation, misleading explanations, lack of contextual awareness, and emerging dependency concerns. These findings demonstrate that GAI operates as a cognitive partner that reshapes students’ CT development, self-regulatory behaviors, and learning identities. The study extends Social Cognitive Theory into human-AI interaction contexts by illustrating triadic reciprocity among personal factors, environmental AI feedback, and adaptive behavioral strategies.
Asunda, P., Faezipour, M., Tolemy, J., & Engel, M. (2023). Embracing Computational Thinking as an Impetus for Artificial Intelligence in Integrated STEM Disciplines through Engineering and Technology Education. Journal of Technology Education, 34, 43-63. https://doi.org/10.21061/jte.v34i2.a.3
Bandura, A. (1977). Social Learning Theory. Prentice Hall.
Barke, S., James, M. B., & Polikarpova, N. (2023). Grounded Copilot: How Programmers Interact with Code-Generating Models. Proceedings of the ACM on Programming Languages, 7, 85-111. https://doi.org/10.1145/3586030
Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., & Santos, E. A. (2023). Programming Is Hard-or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (pp. 500-506). ACM. https://doi.org/10.1145/3545945.3569759
Belmar, H. (2022). Review on the Teaching of Programming and Computational Thinking in the World. Frontiers in Computer Science, 4, Article ID: 997222. https://doi.org/10.3389/fcomp.2022.997222
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory . Prentice Hall.
Boguslawski, S., Deer, R., & Dawson, M. G. (2024). Programming Education and Learner Motivation in the Age of Generative AI: Student and Educator Perspectives. Information and Learning Sciences, 126, 91-109. https://doi.org/10.1108/ils-10-2023-0163
Cain, W. (2023). Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education. TechTrends , 68, 47-57. https://doi.org/10.1007/s11528-023-00896-0
Celik, I. (2023). Exploring the Determinants of Artificial Intelligence (AI) Literacy: Digital Divide, Computational Thinking, Cognitive Absorption. Telematics and Informatics, 83, Article 102026. https://doi.org/10.1016/j.tele.2023.102026
Chen, C., & Chung, H. (2024). Fostering Computational Thinking and Problem-Solving in Programming: Integrating Concept Maps into Robot Block-Based Programming. Journal of Educational Computing Research, 62, 186-207. https://doi.org/10.1177/07356331231205052
Chen, E., Huang, R., Chen, H., Tseng, Y., & Li, L. (2023). GPTutor: A ChatGPT-Powered Programming Tool for Code Explanation. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos, (Eds.), Communications in Computer and Information Science (pp. 321-327). Springer. https://doi.org/10.1007/978-3-031-36336-8_50
Choi, S., & Kim, H. (2024). The Impact of a Large Language Model-Based Programming Learning Environment on Students’ Motivation and Programming Ability. Education and Information Technologies, 30, 8109-8138. https://doi.org/10.1007/s10639-024-13107-x
Csizmadia, A., Standl, B., & Waite, J. (2019). Integrating the Constructionist Learning Theory with Computational Thinking Classroom Activities. Informatics in Education, 18, 41-67. https://doi.org/10.15388/infedu.2019.03
Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A. et al. (2024a). Prompt Problems: A New Programming Exercise for the Generative AI Era. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 296-302). ACM. https://doi.org/10.1145/3626252.3630909
Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J. et al. (2024b). Computing Education in the Era of Generative Ai. Communications of the ACM, 67, 56-67. https://doi.org/10.1145/3624720
Eteng, I., Akpotuzor, S., Akinola, S. O., & Agbonlahor, I. (2022). A Review on Effective Approach to Teaching Computer Programming to Undergraduates in Developing Countries. Scientific African, 16, e01240. https://doi.org/10.1016/j.sciaf.2022.e01240
Fagerlund, J., Häkkinen, P., Vesisenaho, M., & Viiri, J. (2021). Computational Thinking in Programming with Scratch in Primary Schools: A Systematic Review. Computer Applications in Engineering Education, 29, 12-28. https://doi.org/10.1002/cae.22255
Garcia, M. B. (2025). Teaching and Learning Computer Programming Using ChatGPT: A Rapid Review of Literature Amid the Rise of Generative AI Technologies. Education and Information Technologies, 30, 16721-16745. https://doi.org/10.1007/s10639-025-13452-5
Garg, A., Nisumba Soodhani, K., & Rajendran, R. (2025). Enhancing Data Analysis and Programming Skills through Structured Prompt Training: The Impact of Generative AI in Engineering Education. Computers and Education: Artificial Intelligence, 8, Article 100380. https://doi.org/10.1016/j.caeai.2025.100380
Gong, X., Xu, W., & Qiao, A. (2025). Exploring Undergraduates’ Computational Thinking and Human-Computer Interaction Patterns in Generative Progressive Prompt-Assisted Programming Learning. International Journal of Educational Technology in Higher Education, 22, Article No. 51. https://doi.org/10.1186/s41239-025-00552-y
Hilario, E., Azam, S., Sundaram, J., Imran Mohammed, K., & Shanmugam, B. (2024). Generative AI for Pentesting: The Good, the Bad, the Ugly. International Journal of Information Security, 23, 2075-2097. https://doi.org/10.1007/s10207-024-00835-x
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B. et al. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32, 504-526. https://doi.org/10.1007/s40593-021-00239-1
Hsu, H. (2025). From Programming to Prompting: Developing Computational Thinking through Large Language Model-Based Generative Artificial Intelligence. TechTrends , 69, 485-506. https://doi.org/10.1007/s11528-025-01052-6
Husain, A. (2024). Potentials of ChatGPT in Computer Programming: Insights from Programming Instructors. Journal of Information Technology Education: Research, 23, Article 002. https://doi.org/10.28945/5240
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y. et al. (2023). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55, 1-38. https://doi.org/10.1145/3571730
Karaoglan Yilmaz, F. G., & Yilmaz, R. (2022). Examining Student Views on the Use of the Learning Analytics Dashboard of a Smart MOOC. International Azerbaijan Congress on Life, Social, Health, and Art Sciences .
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 A. Schmidt, K. Väänänen, T., P. O. Kristensson, A. Peters, S. Mueller, J. R. Williamson, & M. L. Wilson (Eds.), Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-23). ACM. https://doi.org/10.1145/3544548.3580919
Kim, B., & Kim, M. (2024). The Influence of Work Overload on Cybersecurity Behavior: A Moderated Mediation Model of Psychological Contract Breach, Burnout, and Self-Efficacy in AI Learning Such as ChatGPT. Technology in Society, 77, Article 102543. https://doi.org/10.1016/j.techsoc.2024.102543
Kohen-Vacs, D., Usher, M., & Jansen, M. (2025). Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors. International Journal of Artificial Intelligence in Education, 35, 3166-3184. https://doi.org/10.1007/s40593-025-00496-4
Liao, J., Zhong, L., Zhe, L., Xu, H., Liu, M., & Xie, T. (2024). Scaffolding Computational Thinking with ChatGPT. IEEE Transactions on Learning Technologies, 17, 1628-1642. https://doi.org/10.1109/tlt.2024.3392896
Liu, M., Zhang, L. J., & Biebricher, C. (2024). Investigating Students’ Cognitive Processes in Generative AI-Assisted Digital Multimodal Composing and Traditional Writing. Computers & Education, 211, Article 104977. https://doi.org/10.1016/j.compedu.2023.104977
Lodi, M., & Martini, S. (2021). Computational Thinking, between Papert and Wing. Science & Education, 30, 883-908. https://doi.org/10.1007/s11191-021-00202-5
Marcionetti, J., & Castelli, L. (2023). The Job and Life Satisfaction of Teachers: A Social Cognitive Model Integrating Teachers’ Burnout, Self-Efficacy, Dispositional Optimism, and Social Support. International Journal for Educational and Vocational Guidance, 23, 441-463. https://doi.org/10.1007/s10775-021-09516-w
Massaty, M. H., Fahrurozi, S. K., & Budiyanto, C. W. (2024). The Role of AI in Fostering Computational Thinking and Self-Efficacy in Educational Settings: A Systematic Review. IJIE (Indonesian Journal of Informatics Education), 8, 49-61. https://doi.org/10.20961/ijie.v8i1.89596
Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc.
Pinski, M., & Benlian, A. (2023). AI Literacy—Towards Measuring Human Competency in Artificial Intelligence. Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 165-174). Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2023.021
Ponzini, D., Adorni, G., Delzanno, G., & Guerrini, G. (2024). Toward the Use of Generative AI to Develop Computational Thinking by Supporting Problem Decomposition. Ital-IA 2024: 4th National Conference on Artificial Intelligence .
Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Applied Sciences, 13, Article 5783. https://doi.org/10.3390/app13095783
Salomon, G., & Perkins, D. (2005). Do Technologies Make Us Smarter? Intellectual Amplification with, of, and through Technology. In R. J. Sternberg, & D. D. Preiss (Eds.) Intelligence and Technology: The Impact of Tools on the Nature and Development of Human Abilities (pp. 71-86). Lawrence Erlbaum Associates Publishers.
Sharma, K., Papavlasopoulou, S., & Giannakos, M. (2019). Coding Games and Robots to Enhance Computational Thinking: How Collaboration and Engagement Moderate Children’s Attitudes? International Journal of Child-Computer Interaction, 21, 65-76. https://doi.org/10.1016/j.ijcci.2019.04.004
Sun, D., Boudouaia, A., Zhu, C., & Li, Y. (2024). Would Chatgpt-Facilitated Programming Mode Impact College Students’ Programming Behaviors, Performances, and Perceptions? an Empirical Study. International Journal of Educational Technology in Higher Education, 21, 1-22. https://doi.org/10.1186/s41239-024-00446-5
Sun, L., Hu, L., Yang, W., Zhou, D., & Wang, X. (2020). STEM Learning Attitude Predicts Computational Thinking Skills among Primary School Students. Journal of Computer Assisted Learning, 37, 346-358. https://doi.org/10.1111/jcal.12493
Tian, H., Lu, W., Li, T. O., Tang, X., Cheung, S. C., Klein, J., & Bissyand’e, T. F. (2023). Is ChatGPT the Ultimate Programming an Assistant—How Far Is It? arXiv: 2304.11938.
Tikva, C., & Tambouris, E. (2021). Mapping Computational Thinking through Programming in K-12 Education: A Conceptual Model Based on a Systematic Literature Review. Computers & Education, 162, Article 104083. https://doi.org/10.1016/j.compedu.2020.104083
Tlili, A., Burgos, D., & Looi, C.-K. (2023). Guest Editorial: Creating Computational Thinkers for the Artificial Intelligence Eracatalyzing the Process through Educational Technology. Educational Technology & Society, 26, 94-98.
Usher, M. (2025). Generative AI vs. Instructor Vs. Peer Assessments: A Comparison of Grading and Feedback in Higher Education. Assessment & Evaluation in Higher Education, 50, 912-927. https://doi.org/10.1080/02602938.2025.2487495
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). Digcomp 2.2, the Digital Competence Framework for Citizens: With New Examples of Knowledge, Skills and Attitudes. Publications Office European Union.
Walter, Y. (2024). Embracing the Future of Artificial Intelligence in the Classroom: The Relevance of AI Literacy, Prompt Engineering, and Critical Thinking in Modern Education. International Journal of Educational Technology in Higher Education, 21, Article No. 15. https://doi.org/10.1186/s41239-024-00448-3
Wang, T., Zhou, N., & Chen, Z. (2024). Enhancing Computer Programming Education with LLMS: A Study on Effective Prompt Engineering for Python Code Generation. arXiv:2407.05437.
Yadav, A., Hong, H., & Stephenson, C. (2016). Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12 Classrooms. TechTrends, 60, 565-568. https://doi.org/10.1007/s11528-016-0087-7
Yan, Y., Chen, C., Hu, Y., & Ye, X. (2025). LLM-Based Collaborative Programming: Impact on Students’ Computational Thinking and Self-Efficacy. Humanities and Social Sciences Communications, 12, 1-12. https://doi.org/10.1057/s41599-025-04471-1
Yang, Y., Tseng, C. C., & Lai, S. (2024). Enhancing Teachers’ Self-Efficacy Beliefs in Ai-Based Technology Integration into English Speaking Teaching through a Professional Development Program. Teaching and Teacher Education, 144, Article 104582. https://doi.org/10.1016/j.tate.2024.104582
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023a). Augmented Intelligence in Programming Learning: Examining Student Views on the Use of ChatGPT for Programming Learning. Computers in Human Behavior: Artificial Humans, 1, Article 100005. https://doi.org/10.1016/j.chbah.2023.100005
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023b). The Effect of Generative Artificial Intelligence (AI)-Based Tool Use on Students’ Computational Thinking Skills, Programming Self-Efficacy and Motivation. Computers and Education: Artificial Intelligence, 4, Article 100147. https://doi.org/10.1016/j.caeai.2023.100147
Zhang, L., KaLyuga, S., Lee, C., & Lei, C. (2016) Effectiveness of Collaborative Learning of Computer Programming under Different Learning Group Formations According to Students’ Prior Knowledge: A Cognitive Load Perspective. Journal of Interactive Learning Research , 2 , 171-192
Zhang, B., Liang, P., Zhou, X., Ahmad, A., & Waseem, M. (2023). Demystifying Practices, Challenges and Expected Features of Using Github Copilot. International Journal of Software Engineering and Knowledge Engineering, 33, 1653-1672. https://doi.org/10.1142/s0218194023410048
Zhao, G., Yang, L., Hu, B., & Wang, J. (2025). A Generative Artificial Intelligence (ai)-Based Human-Computer Collaborative Programming Learning Method to Improve Computational Thinking, Learning Attitudes, and Learning Achievement. Journal of Educational Computing Research, 63, 1059-1087. https://doi.org/10.1177/07356331251336154
Zheng, L., Zhen, Y., Niu, J., & Zhong, L. (2022). An Exploratory Study on Fade-In versus Fade-Out Scaffolding for Novice Programmers in Online Collaborative Programming Settings. Journal of Computing in Higher Education, 34, 489-516. https://doi.org/10.1007/s12528-021-09307-w