Deteksi Jawaban Buatan AI pada Tugas Pemrograman Berbasis Sistem Penilaian Otomatis

Ahmad Muyassar Ibrahim

doi:10.61255/pisces.v4i1.1518

Authors

Ahmad Muyassar Ibrahim Universitas Islam Negeri Alauddin Makassar

DOI:

https://doi.org/10.61255/pisces.v4i1.1518

Keywords:

AI Detection, Academic Integrity, Artificial Intelligence, Programming, Automated Assessment

Abstract

The use of artificial intelligence (AI) by students in completing programming assignments continues to increase and has created new challenges in maintaining academic integrity. This study examines the patterns and prevalence of AI-generated answers in programming assignments submitted through a Learning Management System (LMS) equipped with automatic assessment features. The LMS used in this study was developed using the ADDIE model and integrated with an AI detection mechanism as the data collection platform. This study involved 109 students from two courses, namely Basic Web Programming and Mobile Programming, at UIN Alauddin Makassar during the Even Semester of the 2026/2027 academic year. From a total of 186 assignment submissions, the system identified 49 submissions, or 26.3%, as answers suspected to have been generated using AI. The analysis covered the distribution of flagging across courses, score comparisons before and after penalties, and the general characteristics of answers indicated as AI-generated. The results show that the level of AI use in Indonesian-language programming assignments is relatively high, with TGS-642 in the Web Programming course recording the highest flagging percentage at 41.5%. This study contributes to understanding the phenomenon of generative AI use in Indonesian programming education and offers practical recommendations for managing academic integrity.

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References

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A., & Reeves, B. N. (2024). Prompt problems: A new programming exercise for the generative AI era. Proceedings of the 55th ACM Technical Symposium on Computer Science Education, 296–302. https://doi.org/10.1145/3626252.3630909

Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2024). Detection of GPT-4 generated text in higher education: Combining academic judgement and software to identify generative AI tool misuse. Journal of Academic Ethics, 22(1), 89–113. https://doi.org/10.1007/s10805-023-09492-6

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), Article 39. https://doi.org/10.1186/s41239-019-0171-0

Chiang, C.-H., Chen, W.-C., Kuan, C.-Y., Yang, C., & Lee, H.-Y. (2024). Large language model as an assignment evaluator: Insights, feedback, and challenges in a 1000+ student course. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), 2489–2513. Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.146

Mohamed, K., Yousef, M., Medhat, W., Mohamed, E. H., Khoriba, G., & Arafa, T. (2025). Hands-on analysis of using large language models for the auto evaluation of programming assignments. Information Systems, 130, Article 102523. https://doi.org/10.1016/j.is.2024.102523

Jukiewicz, M. (2025). A comparative study of large language models in programming education: Accuracy, efficiency, and feedback in student assignment grading. Applied Sciences, 15(18), Article 10055. https://doi.org/10.3390/app151810055

Turnbull, D., Chugh, R., & Luck, J. (2021). Learning management systems, an overview. Encyclopedia of Education and Information Technologies. Springer. https://doi.org/10.1007/978-3-030-10576-1_82

Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer. https://doi.org/10.1007/978-0-387-09506-6

Adeoye, M. A. (2024). Revolutionizing education: Unleashing the power of the ADDIE model for effective teaching and learning. Jurnal Pendidikan Indonesia, 13(1), 202–209. https://doi.org/10.23887/jpiundiksha.v13i1.68624