A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918104164990976 |
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| author | Zerkouk, Meriem Mihoubi, Miloud Chikhaoui, Belkacem |
| author_facet | Zerkouk, Meriem Mihoubi, Miloud Chikhaoui, Belkacem |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18882 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges Zerkouk, Meriem Mihoubi, Miloud Chikhaoui, Belkacem Information Retrieval Artificial Intelligence Human-Computer Interaction 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. |
| title | A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges |
| topic | Information Retrieval Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.18882 |