Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020
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The four main functions of AI applications in online higher education are performance prediction, resource recommendation, automatic assessment, and improvement of learning experiences.
“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)
Extant systematic reviews on higher education highlighted the use of AIEd for prediction.
“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
Based on 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.
“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]
Higher education settings play a significant role in developing learners' socio-affective skills, which are crucial for their overall well-being.
“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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“Interestingly, none mentioned future applications that could predict whether they are at risk of failing a course, a function well-researched in the literature on AI educational technologies (Ouyang et al., 2022).”
- 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
- Physical Sciences › Computer Science › Information Systems › 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
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