The DOI matches its Crossref record; 86 of the 87 references it cites were found in a registry or an index.
Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020
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In the citing paper
“The findings show that performance prediction, resource recommendation, automatic assessment, and improvement of learning experiences are the four main functions of AI applications in online higher education.”
From Artificial intelligence in higher education: the state of the field (2023) · cites this paper as Ouyang et. al. (2022)
In the citing paper
“Extant systematic reviews on HE highlighted the use of AIEd for prediction (viz., Chu etal., 2022; Hinojo-Lucena etal., 2019; Ouyang etal., 2022; Zawacki-Richter etal., 2019).”
From Artificial intelligence in higher education: the state of the field (2023) · cites this paper as Ouyang et al., 2022
In the citing paper
“Based on such emotional signals, AI can adapt the learning environment, such as by using motivational messages or simplifying the task at moments of frustration of the learner, or gamifying when the learner is bored [216].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [216]
In the citing paper
“higher education settings are not only about promoting cognitive skills and knowledge but also play a significant role in developing learners' socio-affective skills, which are crucial for their overall well-being ([35,37]).”
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“…are still in the 1 3 preliminary and exploratory stage, AI is proved to be positive to enhance online instruction and learning quality by offering accurate prediction, assessment and engaging students with online materials and environments (Yang et al., 2020; Zawacki-Richter et al., 2019).”
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Vision, challenges, roles and research issues of Artificial Intelligence in Education
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A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · cited 1×
“For instance, existing review work has summarized the trends of AIEd (Tang et al., 2021; Xie et al., 2019; Zhai et al., 2021), applications (Alyahyan & Düştegör, 2020; Hooshyar et al., 2016; Liz-Domínguez et al., 2019; Shahiri et al., 2015), theoretical paradigms (Ouyang & Jiao, 2021) and AI…”
BackgroundApplication and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003
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Metadata
- Authors
- Ouyang, F., Zheng, L., & Jiao, P.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Ouyang, Fan
- Venue
- Education and Information Technologies
- Published
- 2022-02-26
- Volume
- 27
- Issue
- 6
- Pages
- 7893-7925
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4214643205
- Semantic Scholar
- 811c64131e009de84f0d92da6cc96e824a291bd5
- Cited by
- 831
- References
- 87
- Citations by year
- 2026: 198 · 2025: 263 · 2024: 251 · 2023: 112 · 2022: 7
- FWCI
- 19.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Education and Information Technologies (publishedVersion)
- Funders
- National Natural Science Foundation of China (62177041)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, online higher education, online learning, learning analytics, learning outcome prediction, student satisfaction prediction, resource recommendation, automated assessment, deep learning, genetic algorithm, student engagement, academic performance
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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