Adaptive intelligent tutoring systems for STEM education: analysis of the learning impact and effectiveness of personalized feedback
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Teaching Science, Technology, Engineering, and Mathematics (STEM) disciplines faces significant challenges due to the increasing complexity of content and the diversity in students’ knowledge levels. Traditional teaching methods, characterized by their standardized approach, fail to adapt to individual needs, limiting learning potential. This problem is particularly evident in mathematics, physics, and programming, where mastering critical concepts requires a logical and sequential progression. This study proposes an artificial intelligence-based intelligent tutoring system to provide a real-time personalized and adaptive learning experience. The system integrates advanced deep learning and natural language processing models, allowing for targeted feedback and dynamic adjustment of learning trajectories. The results show significant improvements in several key metrics. The experimental group achieved an average precision of 85% in programming and 78% in mathematics, significantly outperforming the control group. Furthermore, a linear regression model identified a positive correlation between the time of interaction with the system and the rate of progress in fundamental concepts. Student perceptions were also highly positive, with 80% appreciating the usefulness of adaptive feedback. These findings identify the system’s potential to transform STEM teaching, address the lack of personalization, and improve learning in various educational settings.
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Intelligent Tutoring Systems offering personalised automated hints and explanations have been shown to improve learning outcomes and receive positive evaluation from students.
“Meanwhile, ITS's offering personalised automated hints and explanations have been shown to improve learning outcomes and receive positive evaluation from students [76, 130].”
From AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities (2026) · cites this paper as [130]
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- Authors
- Villegas-Ch, W., Buenano-Fernandez, D., Navarro, A. M., & Mera-Navarrete, A.
- Affiliations
- Universidad de Las Américas (EC)
- Universidad Internacional del Ecuador (EC)
- Corresponding author
- Villegas-Ch, William
- Venue
- Smart Learning Environments
- Published
- 2025-06-30
- Volume
- 12
- Issue
- 1
- ISSN
- 2196-7091 (ISSN-L)
- OpenAlex
- W4411842899
- Cited by
- 80
- References
- 38
- Citations by year
- 2026: 64 · 2025: 16
- FWCI
- 32.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Smart Learning Environments (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- intelligent tutoring systems, adaptive learning, personalized feedback, STEM education, programming education, mathematics learning, physics education, deep learning, natural language processing, student perceptions, learning progress
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