Mapping Students' Artificial Intelligence Usage Profiles in Computer Programming Courses: A Quantitative Descriptive Study

Yerry Soepriyanto, Henry Praherdhiono, Agus Wedi, Made Duananda Kartika Degeng, Asrofi Asrofi, Dany Wijaya Kesuma

Abstract


The advent of artificial intelligence (AI) has transformed the way students learn programming in recent years; however, a clear picture of how this technology is actually used on a day-to-day basis has rarely been systematically mapped. This study aims to map the artificial intelligence usage profiles of undergraduate students in Educational Technology within the Visual Programming course. A quantitative descriptive method was employed, involving 172 students as respondents. Data were collected via a 23-item Likert-scale questionnaire (1–5), and subsequently categorised using Azwar’s formula to determine low, moderate and high levels of usage. The results indicate that ChatGPT is the most widely used AI tool amongst students, accounting for 76.2% of respondents. Students use AI intensively, primarily to deepen their understanding of concepts they have not yet grasped, rather than merely to complete tasks instantly. Overall, students exhibited a high-category AI usage profile, with 77.3% of all research subjects falling into this category. These findings suggest that students do not merely use AI passively, but rather utilise it as an active and reflective learning companion; this can therefore serve as a basis for universities to formulate more targeted AI integration policies within their visual programming.

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References


Alanazi, M., Soh, B., Samra, H., & Li, A. (2025). PyChatAI: Enhancing python programming skills—An empirical study of a smart learning system. Computers, 14(5), 158.

Awasthi, A., Le, N., Deng, Z., Agrawal, R., Wu, C. C., & Van Nguyen, H. (2024). Bridging human and machine intelligence: Reverse-engineering radiologist intentions for clinical trust and adoption.

Azwar, S. (1993). “ Kelompok subjek ini memiliki harga diri yang rendah”; kok, tahu...? Buletin Psikologi, 1(1993).

Azwar, S. (2022). Penyusunan skala psikologi edisi 2. Pustaka pelajar.

Baek, C., Tate, T., & Warschauer, M. (2024). “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI. Computers and Education: Artificial Intelligence, 7, 100294.

Bassner, P., Lenk-Ostendorf, B., Beinstingel, R., Wasner, T., & Krusche, S. (2025). Less stress, better scores, same learning: The dissociation of performance and learning in AI-supported programming education. Computers and Education: Artificial Intelligence, 100537.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Hadihabibi, M., Setyosari, P., & Soepriyanto, Y. (2023). Pengaruh flipped classroom beraktivitas gamifikasi tradisional terhadap self regulated learning pada pembelajaran pemrograman visual. JKTP: Jurnal Kajian Teknologi Pendidikan, 6(1), 026–036.

Hartley, K., Hayak, M., & Ko, U. H. (2024). Artificial intelligence supporting independent student learning: An evaluative case study of ChatGPT and learning to code. Education Sciences, 14(2), 120.

Idrisov, B., & Schlippe, T. (2024). Program code generation with generative AIs. Algorithms, 17(2), 62.

Kamalı‐Arslantaş, T., & Yürüm, O. R. (2026). Decoding the Use of Artificial Intelligence in Programming Education: A Topic Modeling Analysis. Computer Applications in Engineering Education, 34(3), e70213.

Kosar, T., Ostojić, D., Liu, Y. D., & Mernik, M. (2024). Computer science education in ChatGPT era: Experiences from an experiment in a programming course for novice programmers. Mathematics, 12(5), 629.

Praherdhiono, H., Soepriyanto, Y., Kurniawan, C., Slamet, T. I., Rosyidah, F. N. A., & Hajjah, R. I. (2026). Basic, Structured, or Strategic? A Diagnostic Typology of Generation Z’s Untrained AI Prompting Skills. Teaching, Learning, and Development, 4(1), 109–116.

Rico-Juan, J. R., Sánchez-Cartagena, V. M., Valero-Mas, J. J., & Gallego, A. J. (2023). Identifying student profiles within online judge systems using explainable artificial intelligence. IEEE Transactions on Learning Technologies, 16(6), 955–969.

Soepriyanto, Y., Kuswandi, D., Kurniawan, C., Hamudi, R. W. D., Arifudin, A., & Nindigraha, N. (2021). Gamification melalui sipejar sebagai inovasi pembelajaran pemrograman visual.

Sugiyono, D. (2013). Metode penelitian pendidikan pendekatan kuantitatif, kualitatif dan R&D.

Sugiyono, P. P. (2016). Metode Penelitian Kunatitatif Kualitatif dan R&D. Alfabeta, Bandung.

Yalwa, A. S., Othman, M. S., Yusuf, L. M., & Almarshadi, M. S. (2026). Generative AI in Higher Education: A Systematic Review with Emphasis on Programming and Computer Science Education. International Journal of Advanced Computer Science and Applications, 17(5).

Yilmaz, R., & Yilmaz, F. G. K. (2023). 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, 100147.


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ISSN: 2987-2448