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

arXiv: https://arxiv.org/abs/2507.18882 · HTML: https://mlsanity.com/paper/2507.18882/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2025.

## Built on by (most cited first, 6 of 6)

- [AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms](https://mlsanity.com/paper/2512.23633/) (2025)
- [When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoring](https://mlsanity.com/paper/2603.27076/) (2026)
- [Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most](https://mlsanity.com/paper/2605.16207/) (2026)
- [Assessing the Pedagogical Readiness of Large Language Models as AI Tutors in Low-Resource Contexts: A Case Study of Nepal's K-10 Curriculum](https://mlsanity.com/paper/2604.09619/) (2026)
- [Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wild](https://mlsanity.com/paper/2606.11744/) (2026)
- [Correct Diagnosis, Better Feedback: A Symbolic-Verifier for Faithful LLM Tutoring Feedback in Logic Proofs](https://mlsanity.com/paper/2609.22553/) (2026)

## Abstract (arXiv)

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.