Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view
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Intelligent tutoring systems have been developed and effectively integrated into learning environments to provide enriched personalized learning experiences, for instance, by offering tailored feedback and guidance.
“Since several years, intelligent tutoring systems (ITS) have been developed and effectively integrated into learning environments to provide enriched personalized learning experiences, for instance, by offering tailored feedback and guidance60,61.”
From On opportunities and challenges of large multimodal foundation models in education (2025) · cites this paper as [60]
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Self-citation
- Authors
- Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., & Zhou, R.
- Affiliations
- University of Science and Technology of China (CN)
- Anhui University (CN)
- Corresponding author
- Wang, Dong
- Zhou, Rongting
- Venue
- Asia Pacific Education Review
- Published
- 2021-05-04
- Volume
- 22
- Issue
- 3
- Pages
- 441-461
- ISSN
- 1598-1037 (ISSN-L)
- 1876-407X
- PMC
- PMC8095475
- OpenAlex
- W3157169558
- Cited by
- 124
- References
- 132
- Citations by year
- 2026: 28 · 2025: 32 · 2024: 40 · 2023: 11 · 2022: 11 · 2021: 2
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Asia Pacific Education Review (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- Anhui Provincial Quality Constructing Teaching Research Project (No: 2018jwxm0899)
- 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, research trends, research evolution, multidisciplinary research, interactive learning environment, student modeling, teaching and learning strategies, machine learning in education, Coh-Metrix, STEM education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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