The DOI matches its Crossref record; 125 of the 126 references it cites were found in a registry or an index.
Artificial intelligence in education: A systematic literature review
Open accessAbstract
Artificial intelligence (AI) in education (AIED) has evolved into a substantial body of literature with diverse perspectives. In this review paper, we seek insights into three critical questions: (1) What are the primary categories of AI applications explored in the education field? (2) What are the predominant research topics and their key findings? (3) What is the status of major research design elements, including guiding theories, methodologies, and research contexts? A bibliometric analysis of 2,223 research articles followed by a content analysis of selected 125 papers reveals a comprehensive conceptual structure of the existing literature. The extant AIED research spans a wide spectrum of applications, encompassing those for adaptive learning and personalized tutoring, intelligent assessment and management, profiling and prediction, and emerging products. Research topics delve into both the technical design of education systems and the examination of the adoption, impacts, and challenges associated with AIED. Furthermore, this review highlights the diverse range of theories applied in the AIED literature, the multidisciplinary nature of publication venues, and underexplored research areas. In sum, this research offers valuable insights for interested scholars to comprehend the current state of AIED research and identify future research opportunities in this dynamic field.
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“The rapid evolution and widespread availability of generative artificial intelligence (Generative AI) tools have significantly reshaped the landscape of higher education (Samala et al., 2025; S. Wang et al., 2024).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as S. Wang et al., 2024
In the citing paper
“This diversity suggests that the topic transcends traditional disciplinary boundaries, attracting attention from both educational technology specialists and researchers in broader pedagogical and computational domains [37].”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [37]
In the citing paper
“Compared to earlier studies [19,37], this analysis offers a deeper categorization and highlights the tension between AI's potential and its limitations.”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [37]
In the citing paper
“These findings align with research by Younas et al. [59] and Wang et al. [37], which have also found that intelligent tutoring systems and gamified AI applications promote student interest and self-directed learning.”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [37]
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Metadata
- Authors
- Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z.
- Affiliations
- University of Saskatchewan (CA)
- Wilfrid Laurier University (CA)
- China University of Geosciences (CN)
- Beijing Sport University (CN)
- Corresponding author
- Wang, Fang
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 252
- Pages
- 124167
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4396766974
- Cited by
- 977
- References
- 126
- Citations by year
- 2026: 512 · 2025: 418 · 2024: 44
- FWCI
- 57.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Expert Systems with Applications (publishedVersion, cc-by-nc-nd)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- artificial intelligence in education, adaptive learning, personalized tutoring, intelligent assessment, student profiling, educational technology adoption, research gaps
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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