Implementation of a Personalised Learning System for At-Risk Students using Adaptive Techniques - Ben Sujin; M. Sangeetha Mani; Kailash Kumar, (International Journal of Interactive Mobile Technologies (iJIM), 2026)
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.