Implementation of a Personalised Learning System for At-Risk Students using Adaptive Techniques

Ben Sujin; M. Sangeetha Mani; Kailash Kumar

doi:10.3991/ijim.v20i03.60059

Authors

Ben Sujin University of Technology and Applied Sciences, Nizwa, Sultanate of Oman
M. Sangeetha Mani University of Technology and Applied Sciences, Nizwa, Sultanate of Oman https://orcid.org/0000-0002-3393-8965
Kailash Kumar Saudi Electronic University, Riyadh, Riyadh

DOI:

https://doi.org/10.3991/ijim.v20i03.60059

Keywords:

Learning styles (LS), adaptive technology, artificial intelligence, Educational Technology, Oman

Abstract

The increasing educational problem of unequal learning outcomes among students highlights a major challenge in modern education—at-risk students often fail to cope with standardised teaching methods, leading to low academic performance and higher dropout rates. By adapting pace, material, and teaching methods to each student’s particular needs, artificial intelligence (AI) in education has the potential to totally change individualised learning. These systems use AI algorithms to evaluate student data and behaviour, providing real-time individualised feedback, adaptive learning pathways, and customised content. The increasing diversity of student learning styles has made it crucial to create intelligent educational systems that offer tailored assistance, particularly for students who are at danger of falling short of academic standards. Therefore, this study presents Personalized Learning Systems for at-risk students using Adaptive Techniques. While correlation analysis demonstrated a substantial positive relationship between AI-based learning and pupil achievement (r = 0.64, p < 0.001), regression modelling revealed a significant impact on participation and engagement (B = 0.62, p < 0.001). Furthermore, chi-square results confirmed the importance of accessibility difficulties (y2 = 14.66, p < 0.001) and data privacy concerns (x2 = 16.79, p < 0.001). This study contributes to the growing field of AI-driven education by offering a scalable and context-aware framework for supporting vulnerable learners, particularly in developing education ecosystems such as Oman.

References

[1] Adarbah, H. Y., & Jajarmi, H. (2022). Active Learning for Omani At-Risk Students through Educational‎ Technology: A Case of Content-Based‎ Language Instruction. International Journal of Society, Culture & Language, 10(2), Themed Issue on the Socio-Psychology of Language, 82-91.

[2] Doss, A. N., Krishnan, R., Karuppasamy, A. D., & Sam, B. (2024). Learning analytics model for predictive analysis of learners behavior for an indigenous MOOC platform (tadakhul system) in Oman. International Journal of Information and Education Technology, 14(7), 961-967.

[3] Al-Bemani, A. S., Nizamudin, A. A., Balaji, G., & Amal, A. A. (2024). Optimizing academic journey for high schoolers in Oman: a machine learning-enabled AI model. Procedia Computer Science, 235, 2716-2729.

[4] Okonji, N. N., & Igwe, E. A. (2025). Prospects and Challenges of Personalized Learning through Artificial Intelligence Driven Adaptive Learning Systems in N. Journal of Education in Developing Areas, 33(1), 139-147.

[5] Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921-1947.

[6] Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068.

[7] Jiali, S., Dayo, F., Jun, G., Shuangyao, L., & Najam, S. (2024). The Impact of Artificial Intelligence on Personalized Learning in Education: A Systematic Review. Pakistan Journal of Life & Social Sciences, 22(2).

[8] Poznanskyy, R., Pryimak, L., Apelt, H., Savchuk, N., & Kopchak, L. (2025). Transforming Foreign Language Education: The Role of AI-Based Adaptive Learning Systems in Enhancing Personalized Learning Amid Global Instability. Metaverse Basic and Applied Research, 4, 8.

[9] Gupta, S., Sharma, P., Vajrala, K. R., Fatima, A., & Saxena, M. S. (2025). Integrating Artificial Intelligence in Education: Advancing Personalized Learning Within Ethical Frameworks: An Overview. EthAIca: Journal of Ethics, AI and Critical Analysis, (4), 418.

[10] Halkiopoulos, C., & Gkintoni, E. (2024). Leveraging AI in e-learning: Personalized learning and adaptive assessment through cognitive neuropsychology—A systematic analysis. Electronics, 13(18), 3762.

[11] Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial intelligence in education: AIEd for personalised learning pathways. Electronic Journal of e-Learning, 20(5), 639-653.

[12] Taylor, R., Fakhimi, M., Ioannou, A., & Spanaki, K. (2024). Personalized learning in education: a machine learning and simulation approach. Benchmarking: An international journal.

[13] Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025). Artificial Intelligence-Enabled Adaptive Learning Platforms: A Review. Computers and Education: Artificial Intelligence, 100429.

[14] Yvonne Khomo, F. L., Millham, R., & Mustafa, A. (2025). Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review. Engineering Innovations, 14, 139-146.

[15] Mamatillayeva, S. (2025). AI-ENHANCED PERSONALIZED LEARNING: THE TRANSFORMATIVE ROLE OF ADAPTIVE ALGORITHMS IN K-12 EDUCATIONAL OUTCOMES. AMERICAN JOURNAL OF EDUCATION AND LEARNING, 3(8), 328-336.

[16] Lu, L. (2025). Enhancing Students’ Self-Regulation and Autonomous Learning in Higher Education through Interactive Mobile Technologies. International Journal of Interactive Mobile Technologies (iJIM), 19(13), pp. 19–32. https://doi.org/10.3991/ijim.v19i13.56595

[17] Bin Syed Ali, S. K., & Bai Kuotian. (2024). Personalized Education via Mobile Technology Flexible Learning Frameworks. International Journal of Interactive Mobile Technologies (iJIM), 18(18), pp. 146–156. https://doi.org/10.3991/ijim.v18i18.50659