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The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems from 2010 to 2022 on Learner Outcomes: A Meta-Analysis

Authors :
Xiaoman Wang
Rui Huang
Max Sommer
Bo Pei
Poorya Shidfar
Muhammad Shahroze Rehman
Albert D. Ritzhaupt
Florence Martin
Source :
Journal of Educational Computing Research. 2024 62(6):1568-1603.
Publication Year :
2024

Abstract

The purpose of this research study was to examine the overall effect of adaptive learning systems deployed using artificial intelligence technology across a range of relevant variables (e.g., duration, student level, etc.). Following a systematic procedure, this meta-analysis examined literature from 18 academic databases and identified N = 45 independent studies utilizing AI-enabled adaptive learning. This meta-analysis examined the overall effect of AI-enabled adaptive learning systems on students' cognitive learning outcomes when compared with non-adaptive learning interventions and found that they have a medium to large positive effect size (g = 0.70). The effect was significantly moderated by publication type, origin of study, student classification level, student discipline, duration, and research design. In addition, all three adaptive sources (cognitive, affective, and behavioral) and adaptive targets (navigation and assessment) were significant moderators. The type of AI used in the adaptive engine did not moderate the effects. Implications for both practice and research are provided.

Details

Language :
English
ISSN :
0735-6331 and 1541-4140
Volume :
62
Issue :
6
Database :
ERIC
Journal :
Journal of Educational Computing Research
Publication Type :
Academic Journal
Accession number :
EJ1441048
Document Type :
Journal Articles<br />Reports - Research<br />Information Analyses
Full Text :
https://doi.org/10.1177/07356331241240459