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

IF 4 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational Computing Research Pub Date : 2024-05-15 DOI:10.1177/07356331241240459

Xiaoman Wang, Rui “Tammy” Huang, Max Sommer, Bo Pei, Poorya Shidfar, Muhammad Shahroze Rehman, Albert D. Ritzhaupt, Florence Martin

{"title":"The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis","authors":"Xiaoman Wang, Rui “Tammy” Huang, Max Sommer, Bo Pei, Poorya Shidfar, Muhammad Shahroze Rehman, Albert D. Ritzhaupt, Florence Martin","doi":"10.1177/07356331241240459","DOIUrl":null,"url":null,"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.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Computing Research","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1177/07356331241240459","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

2010 至 2022 年人工智能自适应学习系统对学习者成果的影响：元分析

本研究的目的是考察利用人工智能技术部署的自适应学习系统在一系列相关变量（如持续时间、学生水平等）中的总体效果。本荟萃分析采用系统化程序，审查了 18 个学术数据库中的文献，确定了 N = 45 项利用人工智能自适应学习的独立研究。这项荟萃分析考察了人工智能自适应学习系统与非自适应学习干预相比对学生认知学习成果的总体影响，发现它们具有中到大的正效应大小（g = 0.70）。该效应受出版物类型、研究来源、学生分类水平、学生学科、持续时间和研究设计的影响。此外，所有三个适应性来源（认知、情感和行为）和适应性目标（导航和评估）都是重要的调节因素。自适应引擎中使用的人工智能类型没有调节作用。本研究为实践和研究提供了启示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Educational Computing Research EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

11.90

自引率

6.20%

发文量

期刊介绍： The goal of this Journal is to provide an international scholarly publication forum for peer-reviewed interdisciplinary research into the applications, effects, and implications of computer-based education. The Journal features articles useful for practitioners and theorists alike. The terms "education" and "computing" are viewed broadly. “Education” refers to the use of computer-based technologies at all levels of the formal education system, business and industry, home-schooling, lifelong learning, and unintentional learning environments. “Computing” refers to all forms of computer applications and innovations - both hardware and software. For example, this could range from mobile and ubiquitous computing to immersive 3D simulations and games to computing-enhanced virtual learning environments.