Artificial Intelligence in Online Higher Education: A Systematic Review of Empirical Research from 2011 to 2020

Autor/inn/en	Ouyang, Fan; Zheng, Luyi; Jiao, Pengcheng
Titel	Artificial Intelligence in Online Higher Education: A Systematic Review of Empirical Research from 2011 to 2020
Quelle	In: Education and Information Technologies, 27 (2022) 6, S.7893-7925 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ouyang, Fan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-10925-9
Schlagwörter	Artificial Intelligence; Electronic Learning; Higher Education; Literature Reviews; Technology Uses in Education; Predictor Variables + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Hochschulbildung; Hochschulsystem; Hochschulwesen; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Prädiktor
Abstract	As online learning has been widely adopted in higher education in recent years, artificial intelligence (AI) has brought new ways for improving instruction and learning in online higher education. However, there is a lack of literature reviews that focuses on the functions, effects, and implications of applying AI in the online higher education context. In addition, what AI algorithms are commonly used and how they influence online higher education remain unclear. To fill these gaps, this systematic review provides an overview of empirical research on the applications of AI in online higher education. Specifically, this literature review examines the functions of AI in empirical researches, the algorithms used in empirical researches and the effects and implications generated by empirical research. According to the screening criteria, out of the 434 initially identified articles for the period between 2011 and 2020, 32 articles are included for the final synthesis. Results find that: (1) the functions of AI applications in online higher education include prediction of learning status, performance or satisfaction, resource recommendation, automatic assessment, and improvement of learning experience; (2) traditional AI technologies are commonly adopted while more advanced techniques (e.g., genetic algorithm, deep learning) are rarely used yet; and (3) effects generated by AI applications include a high quality of AI-enabled prediction with multiple input variables, a high quality of AI-enabled recommendations based on student characteristics, an improvement of students' academic performance, and an improvement of online engagement and participation. This systematic review proposes the following theoretical, technological, and practical implications: (1) the integration of educational and learning theories into AI-enabled online learning; (2) the adoption of advanced AI technologies to collect and analyze real-time process data; and (3) the implementation of more empirical research to test actual effects of AI applications in online higher education. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01