生成式人工智能能提高大学生的学习成绩吗?元分析

IF 4 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Lihui Sun, Liang Zhou
{"title":"生成式人工智能能提高大学生的学习成绩吗?元分析","authors":"Lihui Sun, Liang Zhou","doi":"10.1177/07356331241277937","DOIUrl":null,"url":null,"abstract":"The use of generative artificial intelligence (Gen-AI) to assist college students in their studies has become a trend. However, there is no academic consensus on whether Gen-AI can enhance the academic achievement of college students. Using a meta-analytic approach, this study aims to investigate the effectiveness of Gen-AI in improving the academic achievement of college students and to explore the effects of different moderating variables. A total of 28 articles (65 independent studies, 1909 participants) met the inclusion criteria for this study. The results showed that Gen-AI significantly improved college students’ academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408,0.659], p < .05). There were within-group differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text ( g = 0.554, p < .05), and sample size of 21–40 ( g = 0.776, p < .05), the use of independent learning styles ( g = 0.600, p < .05) had the most significant improvement in college student’s academic achievement. The intervention duration, the discipline types, and the assessment tools also had a moderate positive impact on college students’ academic achievement, but there were no significant within-group differences in any of the moderating variables. This study provides a theoretical basis and empirical evidence for the scientific application of Gen-AI and the development of educational technology policy.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Does Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis\",\"authors\":\"Lihui Sun, Liang Zhou\",\"doi\":\"10.1177/07356331241277937\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of generative artificial intelligence (Gen-AI) to assist college students in their studies has become a trend. However, there is no academic consensus on whether Gen-AI can enhance the academic achievement of college students. Using a meta-analytic approach, this study aims to investigate the effectiveness of Gen-AI in improving the academic achievement of college students and to explore the effects of different moderating variables. A total of 28 articles (65 independent studies, 1909 participants) met the inclusion criteria for this study. The results showed that Gen-AI significantly improved college students’ academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408,0.659], p < .05). There were within-group differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text ( g = 0.554, p < .05), and sample size of 21–40 ( g = 0.776, p < .05), the use of independent learning styles ( g = 0.600, p < .05) had the most significant improvement in college student’s academic achievement. The intervention duration, the discipline types, and the assessment tools also had a moderate positive impact on college students’ academic achievement, but there were no significant within-group differences in any of the moderating variables. This study provides a theoretical basis and empirical evidence for the scientific application of Gen-AI and the development of educational technology policy.\",\"PeriodicalId\":47865,\"journal\":{\"name\":\"Journal of Educational Computing Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-08-28\",\"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/10.1177/07356331241277937\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Computing Research","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1177/07356331241277937","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

摘要

使用生成式人工智能(Gen-AI)帮助大学生学习已成为一种趋势。然而,对于 Gen-AI 能否提高大学生的学业成绩,学术界尚未达成共识。本研究采用元分析方法,旨在研究 Gen-AI 在提高大学生学业成绩方面的有效性,并探讨不同调节变量的影响。共有 28 篇文章(65 项独立研究,1909 名参与者)符合本研究的纳入标准。结果表明,Gen-AI 显著提高了大学生的学业成绩,且效果中等(Hedges's g = 0.533, 95% CI [0.408,0.659], p <.05)。活动类别、样本量和生成内容这三个调节变量存在组内差异,当生成内容为文本时(g = 0.554,p <.05),样本量为 21-40 时(g = 0.776,p <.05),自主学习方式的使用(g = 0.600,p <.05)对大学生学业成绩的提高最为显著。干预时间、学科类型和评估工具也对大学生的学业成绩产生了适度的积极影响,但任何调节变量在组内都没有显著差异。本研究为 Gen-AI 的科学应用和教育技术政策的制定提供了理论基础和实证依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Does Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis
The use of generative artificial intelligence (Gen-AI) to assist college students in their studies has become a trend. However, there is no academic consensus on whether Gen-AI can enhance the academic achievement of college students. Using a meta-analytic approach, this study aims to investigate the effectiveness of Gen-AI in improving the academic achievement of college students and to explore the effects of different moderating variables. A total of 28 articles (65 independent studies, 1909 participants) met the inclusion criteria for this study. The results showed that Gen-AI significantly improved college students’ academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408,0.659], p < .05). There were within-group differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text ( g = 0.554, p < .05), and sample size of 21–40 ( g = 0.776, p < .05), the use of independent learning styles ( g = 0.600, p < .05) had the most significant improvement in college student’s academic achievement. The intervention duration, the discipline types, and the assessment tools also had a moderate positive impact on college students’ academic achievement, but there were no significant within-group differences in any of the moderating variables. This study provides a theoretical basis and empirical evidence for the scientific application of Gen-AI and the development of educational technology policy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Educational Computing Research
Journal of Educational Computing Research EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
11.90
自引率
6.20%
发文量
69
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信