: 10.62796/pijst
Nilisha Singh, Phd Scholar (Education), Glocal University, Saharanpur, U.P.
Published Date: 05-07-2025 Issue: Vol. 2 No. 7 (2025): July 2025 Published Paper PDF: Download Abstract- Artificial Intelligence (AI) has transformed adaptive learning systems (ALS) by enabling personalized educational pathways that respond dynamically to learner behaviors, preferences, and performance. Unlike traditional teacher-driven models, which emphasize standardized content and uniform pedagogy, AI-based adaptive systems integrate intelligent tutoring, predictive analytics, and real-time feedback to optimize learning efficiency. These systems build detailed learner profiles, customize instructional content, and provide immediate, context-sensitive guidance, thereby enhancing engagement and improving outcomes. Comparative analyses indicate that adaptive approaches reduce completion time, increase motivation, and foster knowledge retention, whereas traditional models often fail to accommodate individual differences. However, challenges such as data privacy, equity of access, technological dependence, and ethical concerns complicate widespread adoption. While AI-based models provide scalability and efficiency, they may also exacerbate inequalities between resource-rich and under-resourced institutions. Balancing technological innovation with pedagogical principles remains essential to ensuring equitable and effective learning experiences. Future directions highlight the potential of machine learning, intelligent tutoring, and personalized pathways to refine adaptive learning, provided that ethical safeguards and inclusive policies are implemented. This comprehensive study concludes that adaptive learning, powered by AI, represents a promising complement—not a replacement—to traditional educational approaches.
Keywords- Adaptive Learning Systems, Artificial Intelligence, Intelligent Tutoring, Traditional Education, Data Privacy, Equity, Educational Technology.
Published Date: 05-07-2025 Issue: Vol. 2 No. 7 (2025): July 2025 Published Paper PDF: Download Abstract- Artificial Intelligence (AI) has transformed adaptive learning systems (ALS) by enabling personalized educational pathways that respond dynamically to learner behaviors, preferences, and performance. Unlike traditional teacher-driven models, which emphasize standardized content and uniform pedagogy, AI-based adaptive systems integrate intelligent tutoring, predictive analytics, and real-time feedback to optimize learning efficiency. These systems build detailed learner profiles, customize instructional content, and provide immediate, context-sensitive guidance, thereby enhancing engagement and improving outcomes. Comparative analyses indicate that adaptive approaches reduce completion time, increase motivation, and foster knowledge retention, whereas traditional models often fail to accommodate individual differences. However, challenges such as data privacy, equity of access, technological dependence, and ethical concerns complicate widespread adoption. While AI-based models provide scalability and efficiency, they may also exacerbate inequalities between resource-rich and under-resourced institutions. Balancing technological innovation with pedagogical principles remains essential to ensuring equitable and effective learning experiences. Future directions highlight the potential of machine learning, intelligent tutoring, and personalized pathways to refine adaptive learning, provided that ethical safeguards and inclusive policies are implemented. This comprehensive study concludes that adaptive learning, powered by AI, represents a promising complement—not a replacement—to traditional educational approaches.
Keywords- Adaptive Learning Systems, Artificial Intelligence, Intelligent Tutoring, Traditional Education, Data Privacy, Equity, Educational Technology.
