AI Assistance Usage and Programming Performance: A Data Analytics–Driven Correlational Study of First-Year IT Students

This research examined the AI assistance usage and programming performance among first-year Information Technology (IT) students at three municipal colleges in the Philippines. The study's objectives were to evaluate students' utilization of AI tools, identify usage patterns, and analyze the correlation between AI assistance usage and programming performance. A descriptive-correlational quantitative research design was used. Data were collected from 250 first-year IT students using a structured questionnaire.
The results indicate that, although students demonstrated considerable confidence in the perceived usefulness of AI (mean 3.63), their actual dependency on these tools remained relatively low (mean 2.79). Many respondents reported limited study habits, with 82% (n=205) spending only 1–3 hours per week practicing programming. The findings indicated that a significant percentage of students commenced the IT program without any prior programming knowledge or experience (79.2%), and 84% (n=210) recognized ChatGPT as their principal AI resource. Statistical analysis showed no significant differences in AI assistance usage and programming performance between male and female students (p > 0.05). Furthermore, Pearson correlation analysis revealed a statistically significant but weak positive relationship between AI assistance usage and programming performance (r = 0.1096, p = 0.001).
Multiple regression analysis further revealed that AI assistance usage alone was not a significant predictor (p = 0.182); rather, performance was primarily driven by weekly study hours (p = 0.001) and prior programming experience (p = 0.032). Crucially, a significant interaction effect (p = 0.008) confirmed that AI assistance usage effectively served as a "cognitive scaffold" for students with higher study hours, whereas high AI reliance did not yield significant gains for those with minimal practice.

AI assistance, AI-Assisted Programming, Artificial Intelligence, Human–AI Interaction, Programming performance

1. Bond, M., & Bedenlier, S. (2019). Facilitating student engagement through educational Technology: towards a conceptual framework. Journal of Interactive Media in Education, 2019(1). [Google Scholar] [Crossref]

2. https://doi.org/10.5334/jime.528 [Google Scholar] [Crossref]

3. Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies, and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187–6203. https://doi.org/10.1080/10494820.2023.2253861 [Google Scholar] [Crossref]

4. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf [Google Scholar] [Crossref]

5. Computers in Human Behavior Reports. (2025). The relationship between generative AI usage and academic performance in programming tasks. Computers in Human Behavior Reports. [Google Scholar] [Crossref]

6. Fan, G., Liu, D., Zhang, R., & Pan, L. (2025). The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches. International Journal of STEM Education, 12(1). https://doi.org/10.1186/s40594-025-00537-3 [Google Scholar] [Crossref]

7. Goodfellow, M., Lambert, A., Booth, R., & Fagan, A. (2026). From Code to questions: Leveraging generative AI to support code comprehension in introductory programming. Human-Centric Intelligent Systems. https://doi.org/10.1007/s44230-026-00150-9 [Google Scholar] [Crossref]

8. Iddrisu, H. M., Iddrisu, S. A., & Aminu, B. (2025). Gender differences in the adoption, usage, and perceived effectiveness of AI writing tools. International Journal of Educational Innovation and Research, 4(1), 110–111. https://doi.org/10.31949/ijeir.v4i1.11717 [Google Scholar] [Crossref]

9. Kazemitabaar, M., Chow, J., Ma, C. K. T., Department of Computer Science, University of Toronto, Ericson, B. J., School of Information, University of, Weintrop, D., College of Education, University of, Grossman, T., & Department of Computer Science, University of Toronto. (2023). Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 23. [Google Scholar] [Crossref]

10. https://www.terpconnect.umd.edu/~weintrop/papers/Kazemitabaar_et_al_CHI_2023.pdf [Google Scholar] [Crossref]

11. Kennedy, B., Tyson, A., & Saks, E. (2025, April 24). What Americans know about everyday uses of artificial intelligence. Pew Research Center. https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/ [Google Scholar] [Crossref]

12. Packia, P. a. A., & Murugan, T. (2025). A Qualitative Study on AI tool adoption in Higher Education: A Cross-National Perspective. Journal of Business and Social Sciences, 2025(2). [Google Scholar] [Crossref]

13. https://doi.org/10.61453/jobss.v2025no23 [Google Scholar] [Crossref]

14. Rahe, C., & Maalej, W. (2025). How do programming students use generative AI? Proceedings of the ACM on Software Engineering, 2(FSE), 978–1000. https://doi.org/10.1145/3715762 [Google Scholar] [Crossref]

15. Thiarajah, S. (2025). Integrating Artificial Intelligence in Teaching and Learning: A Systematic Literature Review. International Journal of Modern Education, 7(28), 84. [Google Scholar] [Crossref]

16. https://doi.org/10.35631/ijmoe.728008 [Google Scholar] [Crossref]

17. UNESCO. (2025, April 14). AI and education: guidance for policy-makers. [Google Scholar] [Crossref]

18. https://www.unesco.org/en/articles/ai-and-education-guidance-policy-makers [Google Scholar] [Crossref]

19. Von Garrel, J., & Mayer, J. (2023). Artificial Intelligence in studies—use of ChatGPT and AI-based tools among students in Germany. Humanities and Social Sciences Communications, 10(1). [Google Scholar] [Crossref]

20. https://doi.org/10.1057/s41599-023-02304-7 [Google Scholar] [Crossref]

21. Yin, W. (2026). The Impact of Generative Artificial Intelligence on College Students’ Computer Thinking in the Task of Complex Computer Programming: Based on Social Cognitive Theory. Creative Education, 17(01), 76–101. https://doi.org/10.4236/ce.2026.171007 [Google Scholar] [Crossref]

• Smart Iot Device for Weather And Health
• Merlarchive: A Web-Based Academic Hub for UDM With AI-Powered Natural Language Processing
• Enhanced Social Network Security System: Integrating Biometric Authentication for Improved User Verification and Privacy Protection
• Smart Budget Allocation in Public Policy: A Data-Driven Approach for Equitable Resource Distribution
• Decision Support System for Faculty Selection, Promotion, and Reclassification Using Predictive Analytics