A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges

TL;DR

This paper systematically reviews AI-based Intelligent Tutoring Systems, analyzing their applications, effectiveness, and challenges in educational settings, and highlights the need for more rigorous research methods.

Contribution

It provides a comprehensive synthesis of recent ITS research, identifying key challenges and proposing directions for future studies to improve effectiveness and evaluation.

Findings

Mixed effectiveness results in real settings

Persistent challenges in implementation and evaluation

Need for more rigorous experimental designs

Abstract

AI-based Intelligent Tutoring Systems (ITS) have significant potential to transform teaching and learning. As efforts continue to design, develop, and integrate ITS into educational contexts, mixed results about their effectiveness have emerged. This paper provides a comprehensive review to understand how ITS operate in real educational settings and to identify the associated challenges in their application and evaluation. We use a systematic literature review method to analyze numerous qualified studies published from 2010 to 2025, examining domains such as pedagogical strategies, NLP, adaptive learning, student modeling, and domain-specific applications of ITS. The results reveal a complex landscape regarding the effectiveness of ITS, highlighting both advancements and persistent challenges. The study also identifies a need for greater scientific rigor in experimental design and data analysis. Based on these findings, suggestions for future research and practical implications are proposed.