Designing Intelligent Tutoring Systems for Personalized Programming Education
DOI:
https://doi.org/10.55640/eijp-06-03-51Keywords:
Intelligent tutoring system, personalized programming education, adaptive feedbackAbstract
This article presents a methodological framework for designing intelligent tutoring systems (ITS) for personalized programming education in higher education. The study addresses a persistent problem in programming courses: students enter with heterogeneous prior knowledge, progress at different speeds, and require different kinds of feedback when solving coding problems. The proposed ITS model integrates five core layers: learner diagnosis, student modeling, domain modeling, pedagogical decision-making, and adaptive feedback. The methodology combines pedagogical analysis, learning analytics, diagnostic programming tasks, student modeling, and a quasi-experimental design with control and experimental groups. Model-based results indicate that a personalized ITS can improve programming competence by strengthening theoretical understanding, algorithmic thinking, code-writing quality, debugging skills, and reflective self-regulation. The article also provides a conceptual model, assessment rubrics, experimental tables, and implementation recommendations for institutions planning to introduce ITS-supported programming instruction. The contribution of the study lies in connecting intelligent tutoring architecture with programming-specific competence indicators and in proposing a practical design model that supports personalized learning paths, real-time feedback, and teacher-facing analytics.
References
Crow, T., Luxton-Reilly, A., & Wuensche, B. (2018). Intelligent tutoring systems for programming education: A systematic review. Proceedings of the Australasian Computing Education Conference, 53-62. https://doi.org/10.1145/3160489.3160492
du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10, e39630. https://doi.org/10.1016/j.heliyon.2024.e39630
Fodouop Kouam, A. W. (2024). The effectiveness of intelligent tutoring systems in supporting students with varying levels of programming experience. Discover Education, 3, 278. https://doi.org/10.1007/s44217-024-00385-3
Fortuna, A., Prasetya, F., Samala, A. D., Rawas, S., Criollo-C, S., Kaya, D., Raihan, M., Andriani, W., Safitri, D., & Nabawi, R. A. (2025). Artificial intelligence in personalized learning: A global systematic review of current advancements and shaping future opportunities. Social Sciences & Humanities Open, 102114. https://doi.org/10.1016/j.ssaho.2025.102114
Farhood, H., et al. (2025). Artificial intelligence-based personalised learning in education: A systematic literature review. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-025-00598-x
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68, 1961-1990. https://doi.org/10.1007/s11423-020-09788-z
Kayumov Oybek Achilovich, Data-driven yondashuv asosida talabalar kompetentligini baholash metodikasi, Ta’limda istiqbolli izlanishlar - xalqaro ilmiy-metodik jurnal, № 4/2026 ISSN: 3060-527X, 55-63 b.
Kayumov Oybek Achilovich, Kompetentligini baholash mezonlari va indikatorlarini ishlab chiqish, “Maktabgacha va maktab ta’limi” jurnali, ISSN: 3060-4613, 2026-yil, aprel, 4(1)-son, 203-208 b.
Kayumov Oybek Achilovich, Web-loyihalash kompetentligini shakllantirishda interaktiv va loyihaviy (PBL) metodlardan foydalanish, Zamonaviy jamiyat va innovatsiyalar ilmiy-nazariy jurnаl, ISSN: 3060-544x, № 4-sоn, 2026-yil, 63-69 b.
Kayumov Oybek Achilovich, Evaluating the Effectiveness of Adaptive Learning Systems in Developing Web Design Competence: An Empirical Study, International journal of pedagogics, ISSN: 2771-2281, Volume 06 Issue 03 (March) 2026, 362-367 b.
Kayumov Oybek Achilovich, Human–Ai Collaboration in Adaptive Learning Systems: A New Paradigm for Competence Development, American journal of social sciences and humanity research, ISSN: 2771-2141, Volume 06 Issue 03 (March) 2026, 126-131 b.
Kayumov Oybek Achilovich, Data-Driven Approaches to Enhancing Students’ Web Design Skills Using Multimodal Learning Analytics, Current research journal of pedagogics, ISSN: 2767-3278, Vol.07 Issue03 2026, 46-52 b.
Kayumov Oybek Achilovich, AI Capital As A Predictor of Students’ Digital and Web Design Competence in Higher Education, Journal of Social Sciences and Humanities Research Fundamentals, ISSN: - 2748-9345, Volume 06 Issue 03 (March) 2026, 94-99 b.
Kayumov Oybek Achilovich, A Conceptual Framework for Developing Web Design Competence Based on Multimodal Learning Analytics in Adaptive Digital Environments, European International Journal Of Pedagogics, ISSN: 2751-000X, Volume 06 Issue 03 (MARCH) 2026, 155-161 b.
Keuning, H., Jeuring, J., & Heeren, B. (2018). A systematic literature review of automated feedback generation for programming exercises. ACM Transactions on Computing Education, 19(1), Article 3. https://doi.org/10.1145/3231711
Lai, C. H., et al. (2025). Analysis of learning behaviors and outcomes for students with different knowledge levels: A case study of Intelligent Tutoring System for Coding and Learning (ITS-CAL). Applied Sciences, 15(4), 1922. https://doi.org/10.3390/app15041922
Letourneau, A., et al. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. npj Science of Learning, 10, Article 37. https://doi.org/10.1038/s41539-025-00320-7
Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2024). Automated grading and feedback tools for programming education: A systematic review. ACM Transactions on Computing Education, 24(1), 1-43.
Sharma, P., et al. (2022). Designing an intelligent tutoring system for computer programming in the Pacific. Education and Information Technologies, 27, 6197-6221. https://doi.org/10.1007/s10639-021-10882-9
Zawacki-Richter, O., Marin, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
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