Intelligent Tutoring System Enhancing Learning with Conversational AI: A Review
Ghanchi Kabir Sarfaraj1
1Faculty of Engineering & Technology, Parul University, Vadodara, Gujarat, India
Abstract
The integration of artificial intelligence through intelligent tutoring systems (ITSs) and conversational chatbots can transform traditional educational methods by introducing new teaching and learning processes. Through the utilization of cognitive diagnostic models combined with machine learning techniques and natural language processing these systems create dynamic and adaptive learning environments, adoption of this technology encounters numerous challenges including mismatches with educational frameworks, scalability problems, and ethical concerns. The educational landscape is being shaped by emerging trends including AI-powered learning analytics emotion-aware tutoring systems and intelligent recommender systems which enhances both student engagement and personalized learning experiences. The potential impact of AI-driven education depends on its effectiveness and equality which requires interdisciplinary methods to balance ethical considerations with technological progress. This study embarks on a critical examination of fundamental elements to build education systems that are inclusive, efficient, and future-ready while exploring the responsible integration of AI technologies.Keywords: Intelligent Tutoring System (ITS), Machine Learning (ML), Automated Assessment, Conversational AI, Interactive LearningIntroduction
It describes a tutoring system based on conversational interaction, with LLMs at its core, providing customized learning experiences. It incorporates student modeling through diagnostic instruments that measure cognitive states, emotional responses, and learning preferences. Using this knowledge, it adapts instructional approaches in the form of personalized interventions and exercises to offer customized help. A proof-of-concept implementation to teach ideas about writing in English revealed the effectiveness of the system. The methodology of the study is novel but raises issues like a diagnostic element of limited scalability and the difficulty involved in the effortless integration of detailed assessments into dialogues in real time. Results indicate that the system needs more improvement to have wider applicability and effectiveness [1].
