{"title":"The Influence of Gen-AI Assisted Learning on Primary School Students' Math Anxiety: An Intervention Study","authors":"Xueshen Wang, Yun Wei","doi":"10.1002/acp.70088","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Math anxiety refers to the emotions of fear, worry, and avoidance that students experience while learning math or participating in math-related activities. How to effectively alleviate students' math anxiety has always been a concern for global education researchers. Generative artificial intelligence (Gen-AI) is a specialized branch of artificial intelligence that focuses on creating new content based on individual needs, such as text, images, audio, and video. This study firstly attempted to integrate Gen-AI assisted learning approach into primary school math classes and explored the influence of this approach on primary school students' math anxiety. This study adopted a mixed quasi-experimental design and was conducted among sixth-grade students from a public primary school in central China. By comparing pre—and post-tests, it was found that the Gen-AI-assisted learning approach could effectively reduce primary school students' math anxiety. The results of semi-structured interviews showed that Gen-AI assisted learning approach could reduce primary school students' math anxiety by enhancing their interest in learning, increasing their math self-efficacy and academic engagement, as well as providing personalized learning support and timely feedback.</p>\n </div>","PeriodicalId":48281,"journal":{"name":"Applied Cognitive Psychology","volume":"39 4","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Cognitive Psychology","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acp.70088","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Math anxiety refers to the emotions of fear, worry, and avoidance that students experience while learning math or participating in math-related activities. How to effectively alleviate students' math anxiety has always been a concern for global education researchers. Generative artificial intelligence (Gen-AI) is a specialized branch of artificial intelligence that focuses on creating new content based on individual needs, such as text, images, audio, and video. This study firstly attempted to integrate Gen-AI assisted learning approach into primary school math classes and explored the influence of this approach on primary school students' math anxiety. This study adopted a mixed quasi-experimental design and was conducted among sixth-grade students from a public primary school in central China. By comparing pre—and post-tests, it was found that the Gen-AI-assisted learning approach could effectively reduce primary school students' math anxiety. The results of semi-structured interviews showed that Gen-AI assisted learning approach could reduce primary school students' math anxiety by enhancing their interest in learning, increasing their math self-efficacy and academic engagement, as well as providing personalized learning support and timely feedback.
期刊介绍:
Applied Cognitive Psychology seeks to publish the best papers dealing with psychological analyses of memory, learning, thinking, problem solving, language, and consciousness as they occur in the real world. Applied Cognitive Psychology will publish papers on a wide variety of issues and from diverse theoretical perspectives. The journal focuses on studies of human performance and basic cognitive skills in everyday environments including, but not restricted to, studies of eyewitness memory, autobiographical memory, spatial cognition, skill training, expertise and skilled behaviour. Articles will normally combine realistic investigations of real world events with appropriate theoretical analyses and proper appraisal of practical implications.