Intelligent Tutoring Systems: Enhancing Personalized Learning through AI

Authors

  • Rakesh D S
  • Gyanendra Kumar Gupta

Keywords:

Intelligent Tutoring Systems, Personalized Learning, Artificial Intelligence in Education, Adaptive Learning, Scalability, Machine Learning

Abstract


The proliferation of online education has underscored a critical limitation of traditional Learning Management Systems (LMS): their inherent inability to provide genuinely personalized, adaptive learning pathways. This paper examines the role of Artificial Intelligence (AI)-driven Intelligent Tutoring Systems (ITS) as a transformative solution to this challenge. By leveraging computational models of pedagogy, student cognition, and domain knowledge, ITS offer a scalable framework for delivering tailored instruction, real-time feedback, and dynamic content adaptation. This research synthesizes recent advancements in machine learning, particularly in natural language processing (NLP) and deep reinforcement learning, that enhance the cognitive and affective capabilities of these systems. The analysis focuses on the architectural components of modern ITS, their efficacy in improving learning outcomes, and the persistent challenges related to scalability, model transparency, and ethical data usage. The conclusion posits that the strategic integration of ITS within online educational ecosystems is pivotal for achieving scalable, equitable, and highly effective personalized learning at a global level

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Published

2025-10-27

How to Cite

1.
D S R, Gupta GK. Intelligent Tutoring Systems: Enhancing Personalized Learning through AI. J Neonatal Surg [Internet]. 2025Oct.27 [cited 2025Dec.3];14(2S):761-76. Available from: https://jneonatalsurg.com/index.php/jns/article/view/9433

Issue

Vol. 14 No. 2S (2025): Journal of Neonatal Surgery

Section

Original Article

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