Evaluating the User Experience of AI-Assisted Programming Tools among Computer Studies College Students

This study examined the perceived user experience of AI-assisted programming tools among 60 respondents in the National Capital Region, composed of Compute Studies college students, recent graduates, and IT professionals, using a quantitative descriptive design. Most participants were beginner to intermediate BSIT students who use platforms such as ChatGPT several times a week to explain code, learn concepts, and fix bugs. Respondents generally evaluated the tools positively, giving an overall weighted mean of 3.20 (Agree). Usefulness and Productivity received the highest rating (3.28, Strongly Agree), while Code Quality and Accuracy received the lowest (3.08, Agree). Respondents perceived the tools as helpful supports that make work faster, but they also expressed reservations about the reliability and accuracy of AI-generated code. The study recommends that school guide students in double-checking the suggestions of AI-assisted programming tools.

Artificial Intelligence, AI-assisted programming tools, user experience, programming education

1. C. I. Ancog, H. . R. Lucero, I. . R. Estrada, H. A. T. Longaquit and N. . T. Mayo, "Factors Influencing AI Tool Adoption in Research Among Junior and Senior Students of QCU College of Computer Studies," International Journal of Computing Sciences Research, vol. 15, pp. 1313-1326, 6 2026. [Google Scholar] [Crossref]

2. D. C. Montejo, K. A. B. Diocares, J. C. Arac, K. H. S. Adol and S. G. Mendoza, "Evaluating the impact of AI-powered tools on programming skills development among IT students at Davao Oriental State University (DOrSU)," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - SOCIAL SCIENCES, vol. 16, no. 2, pp. 98-114, 10 2026. [Google Scholar] [Crossref]

3. I. M. Cagula-Escauso, R. . B. Ardael-Gabayno, J. Casido, . N. Del Rosario and J. . L. Plender-Nabas, "Impact of ChatGPT Integration on Programming Skill Development in Undergraduate IT Education Programs in the Philippines: A Systematic Review," SSRN, 2026. [Google Scholar] [Crossref]

4. F. Ali, A. Ahmed, M. A. Alipour and H. Terashima-Marin,, "Adoption of AI-coding assistants in programming education: exploring trust and learning motivation through an extended technology acceptance model," 2026. [Google Scholar] [Crossref]

5. U. Farooq, D. Morganti and S. Anwar, "AI Code Assistants in Programming Education: A Narrative Literature Review," Education Sciences, vol. 16, no. 6, p. 961, 6 2026. [Google Scholar] [Crossref]

6. J. T. Liang, C. Yang and B. A. Myers, "A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges," in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE '24), 2024. [Google Scholar] [Crossref]

7. F. F. Xu, . V. Bogdan and G. Neubig, "In-IDE Code Generation from Natural Language: Promise and Challenges," 4 3 2022. [Google Scholar] [Crossref]

8. S. Barke, M. B. James and N. Polikarpova, "Grounded Copilot: How Programmers Interact with Code-Generating Models," Proceedings of the ACM on Programming Languages, vol. 7, no. OOPSLA1, p. 85–111 (Article 78), 6 4 2023. [Google Scholar] [Crossref]

9. H. Mozannar, A. Fourney, . G. Bansal and . E. Horvitz, "Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming," arXiv, 2022. [Google Scholar] [Crossref]

10. S. . I. Ross, F. Martinez, S. Houde, M. Muller and J. D. Weisz, "The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development," 27 3 2023. [Google Scholar] [Crossref]

11. M. Wermelinger, "Using GitHub Copilot to Solve Simple Programming Problems," in Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE '23), 2023. [Google Scholar] [Crossref]

12. I. M. Samarappulige, K. Ekanayake and N. T. Jayathilake, "Evaluating the Educational Benefits and Risks of AI Coding Assistants Among Novice Programming Students in Sri Lanka," TechRxiv, 2025. [Google Scholar] [Crossref]

13. W. Zheng, T. Ratanaolarn and K. Sriwisathiyakun, "A quasi-experimental study of lab rotation integrated with generative AI and peer code review model to enhance students’ critical thinking and academic resilience in introductory programming," Cogent Education, vol. 13, no. 1, 2026. [Google Scholar] [Crossref]

14. M. Alanazi, A. Li, . A. Almalawi, H. Samra and . B. Soh, "Pedagogical Transformation and Teaching Practice in Programming Education Through AI Coding Assistants: Faculty Perspectives and the AI Coding Assistant Adoption Framework," Applied Sciences, vol. 16, no. 10, p. 4833, 5 2026. [Google Scholar] [Crossref]

15. F. Nizamudeen, Gatti, Lorenzo, Bouali, Nacir and Ahmed, Faizan, "Investigating the Impact of Code Generation Tools (ChatGPT & Github CoPilot) on Programming Education," in Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024), 2024. [Google Scholar] [Crossref]

16. T. Kosar, D. Ostojić, Y. D. Liu and M. Mernik, "Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers," Mathematics, vol. 12, no. 5, p. 629, 21 2 2024. [Google Scholar] [Crossref]

17. L. P. Plecerda, "Academic integrity surrounding the use of generative AI in higher education: Lenses from ICT college students," International Journal of Higher Education, 30 12 24. [Google Scholar] [Crossref]

18. M. Kazemitabaar, X. Hou, A. Henley, B. J. Ericson, D. Weintrop and T. Grossman, "How Novices Use LLM-based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment," in Proceedings of the 23rd Koli Calling International Conference on Computing Education Research (Koli Calling '23), 2023. [Google Scholar] [Crossref]

19. C. Rahe and W. Maalej, "How Do Programming Students Use Generative AI?," Proceedings of the ACM on Software Engineering, vol. 2, pp. 978- 1000, 6 2025. [Google Scholar] [Crossref]

20. R. Zviel-Girshin, "The Good and Bad of AI Tools in Novice Programming Education," Education Sciences, p. 1089, 6 10 2024. [Google Scholar] [Crossref]

21. J. M. M. Fatagani-Valerio, . J. R. G. Valerio, B. . P. Balancio and . J. L. Pimentel, "An Assessment of Student-Researcher Satisfaction with the Use of Artificial Intelligence in Thesis Writing," Psychology and Education: A Multidisciplinary Journal, vol. 46, no. 7, p. 920–926, 2025. [Google Scholar] [Crossref]

22. B. Sheese, M. Liffiton, J. Savelka and P. Denny, "Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant," in Proceedings of the 26th Australasian Computing Education Conference (ACE '24), 2024. [Google Scholar] [Crossref]

23. P. Denny, S. MacNeil, J. Savelka and A. Luxton-Reilly, "Desirable Characteristics for AI Teaching Assistants in Programming Education," in Proceedings of the 2024 Innovation and Technology in Computer Science Education Conference V. 1 (ITiCSE '24), 2024. [Google Scholar] [Crossref]

24. R. M. Llobia and . D. R. . B. Pasion, "Generative Artificial Intelligence Integrated Pedagogy and Quality of Student Output in Web Design Courses Among Information Technology," International Journal of Science, Architecture, Technology, and Environment, vol. 3, no. 5, 5 5 2026. [Google Scholar] [Crossref]

25. E. Chen, . R. Huang, H. S. Chen, . Y. H. Tseng and L. Y. Li, "GPTutor: A ChatGPT-Powered Programming Tool for Code Explanation," in Proceedings of the 24th International Conference on Artificial Intelligence in Education (AIED 2023), 2023. [Google Scholar] [Crossref]

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