[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-42834-en":3,"doc-seo-42834-105":29,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},42834,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Does Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis","Generative artificial intelligence (Gen-AI) has become widely used to support college students, yet its impact on academic achievement lacks consensus. This meta-analysis evaluates the effectiveness of Gen-AI interventions and examines how moderator variables shape outcomes across studies. Twenty-eight articles comprising 65 independent studies and 1,909 participants were included. Results indicate a statistically significant improvement in academic achievement with a medium effect size (Hedges’s g = 0.533). Moderator effects vary by generated content type, sample size, and learning style.","Original Research Article  \nDoes Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis  \nLihui Sun 1 􀀁 and Liang Zhou 1  \nJournal of Educational Computing Research  \n2024, Vol. 0(0) 1–38  \n© The Author(s) 2024  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)  \n[DOI: 10.1177/07356331241277937](DOI: 10.1177/07356331241277937)[ ](DOI: 10.1177/07356331241277937)[journals.sagepub.com/home/jec](journals.sagepub.com/home/jec)  \nAbstract  \nThe 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 metaanalytic 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 \u003C .05) . There were withingroup differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text (g = 0.554, p \u003C .05), and sample size of 21–40 (g = 0.776, p \u003C .05), the use of independent learning styles (g = 0.600, p \u003C .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.  \n1 School of Education, Minzu University of China, Beijing, China  \nCorresponding Author:  \nLihui Sun, School of Education, Minzu University of China, No. 27 Zhongguancun South Avenue Beijing, Beijing 100081, China.  \nEmail: [slhphd@yeah.net](slhphd@yeah.net)  \nKeywords  \ncollege students, generative artificial intelligence (Gen-AI), academic achievement, instruction design, meta-analysis  \nIntroduction  \nGenerative Artificial Intelligence (Gen-AI) has recently gained attention for its potential to mimic human thought and create content (Bommasani et al., 2021) . It has been used to automate writing assistance, personalize learning path design, and simulate complex problem-solving processes, applications that are particularly prominent in higher education (Biswas, 2023; Kitamura, 2023; Xiao et al., 2023) . Several countries, including the United Kingdom, the United States, China, and Canada, have called for rapid reform of university teaching, emphasizing the critical role of GenAI in university education (Saheb & Saheb, 2023) . However, there is no academic consensus on whether these technologies enhance the academic achievement of college students. A large number of empirical studies on GenAI provide conflicting evidence (Essel et al., 2022; Hobert et al., 2023) . Several studies have pointed out that Gen-AI can significantly improve the academic achievement of college students (Koç, 2024; Kuhail et al., 2022; Qureshi, 2023) by developing their writing skills (Banihashem et al., 2024), critical thinking (Essien et al., 2024), problem-solving skills (Liu et al., 2024), and can also be effective in improving college students ’ motivation and self-efficacy (Pellas, 2023). For example, Yilmaz et al.’s (2023) study of 45 undergraduate students found that undergraduates using ChatGPT had significantly higher computational thinking skills. Shi et al. (2024) also noted that Gen-AI can significantly improve students’ cognitive, skill, and affecti","cbCaigsaBWytG12C","https://ap.wps.com/l/cbCaigsaBWytG12C","pdf",1766262,1,38,"English","en",105,"# Abstract\n## Research Aim and Data Source\n## Main Findings and Effect Size\n## Moderator Variables and Differential Effects\n# Introduction\n## Background and Use Cases\n## Evidence Conflict in Prior Studies\n## Study Rationale and Contributions\n# Literature Review\n## Generative Artificial Intelligence","[{\"question\":\"What does the meta-analysis investigate about Gen-AI and college students?\",\"answer\":\"It assesses whether generative artificial intelligence improves college students’ academic achievement and analyzes how different moderator variables influence the effect.\"},{\"question\":\"How many studies and participants were included?\",\"answer\":\"The analysis included 28 articles, 65 independent studies, and 1,909 participants.\"},{\"question\":\"What is the overall impact of Gen-AI on academic achievement?\",\"answer\":\"Gen-AI significantly improves academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408, 0.659], p \\u003c .05).\"},{\"question\":\"Which factors showed stronger improvement effects among moderators?\",\"answer\":\"When generated content is text (g = 0.554), sample size is 21–40 (g = 0.776), and independent learning style is used (g = 0.600), the improvement is most significant. Other moderators showed moderate positive impact without significant within-group differences.\"}]",1783371815,96,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":27},"does-generative-artificial-intelligence-improve-the-academic-achievement-of-college-students-a-meta-analysis","",{"@graph":35,"@context":89},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/does-generative-artificial-intelligence-improve-the-academic-achievement-of-college-students-a-meta-analysis/42834/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-21","2026-07-06",true,{"@type":65,"interactionType":66,"userInteractionCount":11},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What does the meta-analysis investigate about Gen-AI and college students?","Question",{"text":75,"@type":76},"It assesses whether generative artificial intelligence improves college students’ academic achievement and analyzes how different moderator variables influence the effect.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many studies and participants were included?",{"text":80,"@type":76},"The analysis included 28 articles, 65 independent studies, and 1,909 participants.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the overall impact of Gen-AI on academic achievement?",{"text":84,"@type":76},"Gen-AI significantly improves academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408, 0.659], p \u003C .05).",{"name":86,"@type":73,"acceptedAnswer":87},"Which factors showed stronger improvement effects among moderators?",{"text":88,"@type":76},"When generated content is text (g = 0.554), sample size is 21–40 (g = 0.776), and independent learning style is used (g = 0.600), the improvement is most significant. 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