Explaınable Machıne Learnıng For Predıctıng Students’ Academıc Performance From Generatıve AI Usage Patterns in Hıgher Educatıon

Öz

Generative artificial intelligence is increasingly embedded in higher education learning practices, yet its contribution to academic performance remains contested. This study developed an explainable machine-learning framework for predicting students’ post-semester GPA from Generative AI usage patterns and learning-related characteristics. Using a structured dataset of 50,000 student records, the study modelled academic performance with predictors including pre-semester GPA, weekly GenAI usage hours, primary use case, prompt-engineering skill, tool diversity, traditional study hours, perceived AI dependency, institutional policy, exam anxiety, year of study, and major category. To prevent outcome leakage, non-relevant outcome variables and student identifiers were excluded before training. Ridge Regression, Decision Tree Regression, and Extra Trees Regression were compared using R², RMSE, and MAE. The results showed that Ridge Regression achieved the strongest predictive performance, indicating that academic outcomes were largely explainable through stable relationships among prior achievement, GenAI-related behaviours, and study characteristics. Feature-importance analysis revealed that prior GPA, primary GenAI use case, and prompt-engineering skill were among the most informative predictors. The study contributes to GenAI-aware learning analytics by demonstrating how explainable modelling can support evidence-based academic monitoring.

Anahtar Kelimeler

• Generative AI
• academic performance prediction
• explainable machine learning
• learning analytics
• higher education

Yükseköğretimde Öğrencilerin Üretken Yapay Zeka Kullanım Modellerinden Akademik Performanslarını Tahmin Etmek için Açıklanabilir Makine Öğrenimi

Öz

Üretken yapay zeka, yükseköğretim öğrenme uygulamalarına giderek daha fazla entegre edilmektedir, ancak akademik performansa katkısı tartışmalı olmaya devam etmektedir. Bu çalışma, öğrencilerin dönem sonu not ortalamasını, Üretken Yapay Zeka kullanım modelleri ve öğrenmeyle ilgili özelliklerden tahmin etmek için açıklanabilir bir makine öğrenimi çerçevesi geliştirmiştir. Çalışma, 50.000 öğrenci kaydından oluşan yapılandırılmış bir veri seti kullanarak, dönem öncesi not ortalaması, haftalık Üretken Yapay Zeka kullanım saati, birincil kullanım amacı, komut mühendisliği becerisi, araç çeşitliliği, geleneksel çalışma saati, algılanan yapay zeka bağımlılığı, kurumsal politika, sınav kaygısı, öğrenim yılı ve bölüm kategorisi gibi tahmin değişkenleri ile akademik performansı modellemiştir. Sonuç sızıntısını önlemek amacıyla, ilgili olmayan sonuç değişkenleri ve öğrenci tanımlayıcıları eğitim öncesinde veri setinden çıkarılmıştır. Ridge Regresyon, Karar Ağacı Regresyonu ve Ekstra Ağaçlar Regresyonu, R², RMSE ve MAE kullanılarak karşılaştırılmıştır. Sonuçlar, Ridge Regresyonun en güçlü tahmin performansını elde ettiğini ve akademik sonuçların büyük ölçüde önceki başarı, Üretken Yapay Zeka ile ilgili davranışlar ve çalışma özellikleri arasındaki istikrarlı ilişkiler yoluyla açıklanabildiğini göstermiştir. Özellik önem analizi, önceki not ortalaması, birincil Üretken Yapay Zeka kullanım amacı ve komut mühendisliği becerisinin en bilgilendirici tahmin değişkenleri arasında olduğunu ortaya koymuştur. Bu çalışma, açıklanabilir modellemenin kanıta dayalı akademik izlemeyi nasıl destekleyebileceğini göstererek, Üretken Yapay Zeka farkındalıklı öğrenme analitiğine katkıda bulunmaktadır.

Anahtar Kelimeler

• Üretken yapay zeka
• akademik performans tahmini
• açıklanabilir makine öğrenimi
• öğrenme analitiği
• yükseköğretim

Kaynakça

1. Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, Article 10. https://doi.org/10.1186/s41239-024-00444-7
2. Baidoo-Anu, D., Asamoah, D., Amoako, I., & Mahama, I. (2024). Exploring student perspectives on generative artificial intelligence in higher education learning. Discover Education, 3, Article 98. https://doi.org/10.1007/s44217-024-00173-z
3. Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), Article 296. https://doi.org/10.3390/info16040296
4. Carmi, G. (2025). Learning with generative AI: An empirical study of students in higher education. Education Sciences, 15(12), Article 1696. https://doi.org/10.3390/educsci15121696
5. Gao, Z., Cheah, J.-H., Lim, X.-J., & Luo, X. (2024). Enhancing academic performance of business students using generative AI: An interactive-constructive-active-passive (ICAP) self-determination perspective. The International Journal of Management Education, 22(2), Article 100958. https://doi.org/10.1016/j.ijme.2024.100958
6. Garzón, J., Acevedo, J., Pavón, J., & Baldiris, S. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), Article 84. https://doi.org/10.3390/mti9080084
7. Haderlie, T. C., Jr., Sewak, S., Agarwal, S., & Lee, J. (2025). Investigating the effects of generative AI on student satisfaction and self-efficacy. Marketing Education Review. https://doi.org/10.1080/10528008.2025.2576159
8. Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2025). Comparing generative AI and teacher feedback: Student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2025.2502582

9. Hon, K. K. L. (2026). Generative AI in higher education: A systematic review of its effects on learning outcomes and academic performance. Journal of Educational Technology Systems, 54(3), 537–560. https://doi.org/10.1177/00472395251400089
10. Jadon, L. (n.d.). Impact of AI on students [Data set]. Kaggle. https://www.kaggle.com/datasets/laveshjadon/ai-impact-on-students
11. Jia, W., Pan, L., & Neary, S. (2025). Effect of GenAI dependency on university students’ academic achievement: The mediating role of self-efficacy and moderating role of perceived teacher caring. Behavioral Sciences, 15(10), 1348. https://doi.org/10.3390/bs15101348
12. Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20, Article 2. https://doi.org/10.1007/s40979-024-00149-4
13. Khosravi, H., Shibani, A., Jovanovic, J., Pardos, Z. A., & Yan, L. (2025). Generative AI and learning analytics: Pushing boundaries, preserving principles. Journal of Learning Analytics, 12(1), 1–11. https://doi.org/10.18608/jla.2025.8961
14. Kim, J., Klopfer, M., Grohs, J. R., Eldardiry, H., Weichert, J., Cox, L. A., II, & Pike, D. (2025). Examining faculty and student perceptions of generative AI in university courses. Innovative Higher Education, 50, 1281–1313. https://doi.org/10.1007/s10755-024-09774-w
15. Lee, D., & Palmer, E. (2025). Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22, Article 7. https://doi.org/10.1186/s41239-025-00503-7
16. Lünich, M., & Keller, B. (2024). Explainable artificial intelligence for academic performance prediction: An experimental study on the impact of accuracy and simplicity of decision trees on causability and fairness perceptions. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1031–1042). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658953
17. McGrath, C., Farazouli, A., & Cerratto-Pargman, T. (2025). Generative AI chatbots in higher education: A review of an emerging research area. Higher Education, 89, 1533–1549. https://doi.org/10.1007/s10734-024-01288-w
18. Misiejuk, K., López-Pernas, S., Kaliisa, R., & Saqr, M. (2025). Mapping the landscape of generative artificial intelligence in learning analytics: A systematic literature review. Journal of Learning Analytics, 12(1), 12–31. https://doi.org/10.18608/jla.2025.8591
19. Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R. (2025). Mastering knowledge: The impact of generative AI on student learning outcomes. Studies in Higher Education. https://doi.org/10.1080/03075079.2025.2487570
20. Park, J., & Choo, S. (2025). Generative AI prompt engineering for educators: Practical strategies. Journal of Special Education Technology, 40(3). https://doi.org/10.1177/01626434241298954
21. Qian, Y. (2025). Pedagogical applications of generative AI in higher education: A systematic review of the field. TechTrends, 69, 1105–1120. https://doi.org/10.1007/s11528-025-01100-1
22. Qiang, M., Liu, Z., & Zhang, R. (2026). Explainable AI in education: Integrating educational domain knowledge into the deep learning model for improved student performance prediction. Scientific Reports, 16, Article 9515. https://doi.org/10.1038/s41598-026-40538-y
23. Rodríguez-Ortiz, M. Á. (2025). Machine learning and generative AI in learning analytics for higher education: A systematic review of models, trends, and challenges. Applied Sciences, 15(15), Article 8679. https://doi.org/10.3390/app15158679
24. Sousa, A. E., & Cardoso, P. (2025). Use of generative AI by higher education students. Electronics, 14(7), Article 1258. https://doi.org/10.3390/electronics14071258
25. Uppal, K., & Hajian, S. (2025). Students’ perceptions of ChatGPT in higher education: A study of academic enhancement, procrastination, and ethical concerns. European Journal of Educational Research, 14(1), 199–211. https://doi.org/10.12973/eu-jer.14.1.199
26. Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), Article 343. https://doi.org/10.3390/educsci15030343
27. Wecks, J. O., Voshaar, J., Plate, B. J., & Zimmermann, J. (2024). Generative AI usage and exam performance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4812513
28. Weng, X., Xia, Q., Gu, M., Rajaram, K., & Chiu, T. K. F. (2024). Assessment and learning outcomes for generative AI in higher education: A scoping review on current research status and trends. Australasian Journal of Educational Technology, 40(6), 37–55. https://doi.org/10.14742/ajet.9540
29. Yang, X., Liu, X., & Gao, Y. (2026). The impact of generative AI on students’ learning: A study of learning satisfaction, self-efficacy and learning outcomes. Educational Technology Research and Development, 74(1), 235–248. https://doi.org/10.1007/s11423-025-10540-8
30. Yusuf, A., Pervin, N., Román-González, M., & Noor, N. M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, Article 21. https://doi.org/10.1186/s41239-024-00453-6
31. Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Educational Technology in Higher Education, 21, Article 34. https://doi.org/10.1186/s41239-024-00467-0