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Intelligent tutoring systems and learning outcomes: A meta-analysis
Abstract
Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.
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“Every learner has unique learning contexts based on their prior knowledge about the topic, social background, economic well-being and emotional state [41].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [41]
In the citing paper
“Despite being limited in terms of the domain that a particular intelligent tutoring system addresses, they have proven to be effective in providing relevant content to students, interacting with students [6] and improving students’ academic performance [18, 41].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [41]
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Argotte, L., Figueroa, G., & Noguez, J. (2011) · Studies in computational intelligence · doi:10.1007/978-3-642-21332-8_5 · Cited by 3
Bradley, D. A. (2010) · Journal for Nurses in Staff Development · doi:10.1097/nnd.0b013e3181de15ea · Cited by 3
DISCOVER: AN INTELLIGENT DISCOVERY PROGRAMMING SYSTEM
Ramadhan, H. A. (2000) · Cybernetics & Systems · doi:10.1080/019697200124937 · Cited by 3
Flagged first, then by citations
Cited by
Sorted by citations
Vision, challenges, roles and research issues of Artificial Intelligence in Education
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100001 · Cited by 1,435
“Several meta-analytic studies have demonstrated the effectiveness of intelligent tutoring systems to promote learning outcomes (Ma, Adesope, Nesbit, & Liu, 2014; Steenbergen-Hu & Cooper, 2014; VanLehn, 2011).”
BackgroundAI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
Gerlich, M. (2025) · Societies · doi:10.3390/soc15010006 · Cited by 1,248
State of the art and practice in AI in education
Holmes, W., & Tuomi, I. (2022) · European Journal of Education · doi:10.1111/ejed.12533 · Cited by 1,093
Artificial Intelligence trends in education: a narrative overview
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.08.233 · Cited by 956
Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review
Kulik, J. A., & Fletcher, J. D. (2016) · Review of Educational Research · doi:10.3102/0034654315581420 · Cited by 930
“Finally, the meta-analysis by Ma et al. (2014) analyzed 107 findings from 73 separate reports.”
Method
Metadata
- Authors
- Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q.
- Affiliations
- Simon Fraser University (CA)
- Washington State University (US)
- Venue
- Journal of Educational Psychology
- Published
- 2014-11
- Volume
- 106
- Issue
- 4
- Pages
- 901-918
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2117655204
- Cited by
- 840
- References
- 127
- Citations by year
- 2026: 179 · 2025: 132 · 2024: 99 · 2023: 65 · 2022: 67 · 2021: 69 · 2020: 70 · 2019: 41 · 2018: 43 · 2017: 36 · 2016: 22 · 2015: 17
- FWCI
- 24.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Psychology (publishedVersion)
- Funders
- Social Sciences and Humanities Research Council of Canada
- Washington State University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- intelligent tutoring systems, learning outcomes, personalized learning, computer-assisted instruction, teacher-centered instruction, small group instruction, homework help, metacognitive knowledge, feedback, misconceptions, publication bias, effect size
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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