Date
Publisher
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.
Study at a glance
- Question: How do AI-based intelligent tutoring systems operate in educational settings, and what challenges affect their application and evaluation?
- Setting and data: 127 peer-reviewed studies published from 2010 to 2025, supplemented by 26 specialized web reports, covering evaluations with at least 100 participants over six weeks.
- Method: Systematic literature review following Kitchenham recommendations and PRISMA reporting guidelines.
Why it matters
Intelligent tutoring systems have significant potential to transform teaching and learning, but mixed results have emerged regarding their effectiveness in real educational contexts.
Key findings
- Intelligent tutoring systems can improve student performance by 20%, showing learning gains comparable to human tutoring in structured domains like mathematics and physics.
- Most evaluated dialogue and assessment models remain brittle, with minor spelling errors or phrasing shifts reducing response accuracy by over 30%.
- Evidence remains overwhelmingly skewed toward STEM and writing tasks in well-funded contexts, with a marked lack of rigorous research in humanities, arts, and under-resourced schools.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Why use AI?
Study design
