{"title":"生成式人工智能能成为好的教学助手吗？——基于生成式ai辅助教学的实证分析","authors":"Qianwen Tang, Wenbo Deng, Yidan Huang, Shuaijie Wang, Hao Zhang","doi":"10.1111/jcal.70027","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Generative Artificial Intelligence (AI) shows promise in enhancing personalised learning and improving educational efficiency. However, its integration into education raises concerns about misinformation and over-reliance, particularly among adolescents. Teacher supervision plays a critical role in mitigating these risks and ensuring the effective use of Generative AI in classrooms. Despite the growing interest in Generative AI, there is limited empirical research on its actual impact and the role of teacher oversight.</p>\n </section>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>The purpose of this study is to systematically assess the role of Generative AI in classroom teaching, with a specific focus on how teacher supervision shapes its effectiveness.</p>\n </section>\n \n <section>\n \n <h3> Method</h3>\n \n <p>This study employed a quasi-experimental design to examine differences in learning outcomes among students under three instructional methods: traditional computer-assisted teaching, Generative AI-assisted teaching without teacher supervision and Generative AI-assisted teaching with teacher supervision. The study was implemented in the context of a two-week Information Science and Technology course in a middle school, involving three classes with 45, 41 and 45 students, respectively. To ensure consistency in teaching styles, all classes were taught by the same experienced teacher. Data collection included a knowledge test to assess knowledge mastery, as well as questionnaires to measure learning satisfaction and engagement. The collected data were analysed using one-way ANOVA to compare the effectiveness of the three teaching methods.</p>\n </section>\n \n <section>\n \n <h3> Results and Conclusion</h3>\n \n <p>Compared with traditional computer-assisted teaching, Generative AI-assisted teaching can significantly enhance students' learning satisfaction, but can not improve their learning engagement and knowledge mastery level. Furthermore, in the process of Generative AI-assisted teaching, teacher supervision can significantly increase students' learning engagement and knowledge mastery compared with situations without teacher supervision. This study indicated Generative AI's potential as an educational tool and underscored the essential role of teacher supervision.</p>\n </section>\n \n <section>\n \n <h3> Implications</h3>\n \n <p>This study fills a critical gap by providing empirical evidence on how Generative AI and teacher supervision interact to improve classroom learning outcomes. It shows that Generative AI's potential to enhance learning outcomes is significantly amplified with teacher oversight.</p>\n </section>\n </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 3","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Can Generative Artificial Intelligence be a Good Teaching Assistant?—An Empirical Analysis Based on Generative AI-Assisted Teaching\",\"authors\":\"Qianwen Tang, Wenbo Deng, Yidan Huang, Shuaijie Wang, Hao Zhang\",\"doi\":\"10.1111/jcal.70027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Generative Artificial Intelligence (AI) shows promise in enhancing personalised learning and improving educational efficiency. However, its integration into education raises concerns about misinformation and over-reliance, particularly among adolescents. Teacher supervision plays a critical role in mitigating these risks and ensuring the effective use of Generative AI in classrooms. Despite the growing interest in Generative AI, there is limited empirical research on its actual impact and the role of teacher oversight.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>The purpose of this study is to systematically assess the role of Generative AI in classroom teaching, with a specific focus on how teacher supervision shapes its effectiveness.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Method</h3>\\n \\n <p>This study employed a quasi-experimental design to examine differences in learning outcomes among students under three instructional methods: traditional computer-assisted teaching, Generative AI-assisted teaching without teacher supervision and Generative AI-assisted teaching with teacher supervision. The study was implemented in the context of a two-week Information Science and Technology course in a middle school, involving three classes with 45, 41 and 45 students, respectively. To ensure consistency in teaching styles, all classes were taught by the same experienced teacher. Data collection included a knowledge test to assess knowledge mastery, as well as questionnaires to measure learning satisfaction and engagement. The collected data were analysed using one-way ANOVA to compare the effectiveness of the three teaching methods.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results and Conclusion</h3>\\n \\n <p>Compared with traditional computer-assisted teaching, Generative AI-assisted teaching can significantly enhance students' learning satisfaction, but can not improve their learning engagement and knowledge mastery level. Furthermore, in the process of Generative AI-assisted teaching, teacher supervision can significantly increase students' learning engagement and knowledge mastery compared with situations without teacher supervision. This study indicated Generative AI's potential as an educational tool and underscored the essential role of teacher supervision.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Implications</h3>\\n \\n <p>This study fills a critical gap by providing empirical evidence on how Generative AI and teacher supervision interact to improve classroom learning outcomes. It shows that Generative AI's potential to enhance learning outcomes is significantly amplified with teacher oversight.</p>\\n </section>\\n </div>\",\"PeriodicalId\":48071,\"journal\":{\"name\":\"Journal of Computer Assisted Learning\",\"volume\":\"41 3\",\"pages\":\"\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Assisted Learning\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jcal.70027\",\"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 Computer Assisted Learning","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jcal.70027","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}
Can Generative Artificial Intelligence be a Good Teaching Assistant?—An Empirical Analysis Based on Generative AI-Assisted Teaching
Background
Generative Artificial Intelligence (AI) shows promise in enhancing personalised learning and improving educational efficiency. However, its integration into education raises concerns about misinformation and over-reliance, particularly among adolescents. Teacher supervision plays a critical role in mitigating these risks and ensuring the effective use of Generative AI in classrooms. Despite the growing interest in Generative AI, there is limited empirical research on its actual impact and the role of teacher oversight.
Objective
The purpose of this study is to systematically assess the role of Generative AI in classroom teaching, with a specific focus on how teacher supervision shapes its effectiveness.
Method
This study employed a quasi-experimental design to examine differences in learning outcomes among students under three instructional methods: traditional computer-assisted teaching, Generative AI-assisted teaching without teacher supervision and Generative AI-assisted teaching with teacher supervision. The study was implemented in the context of a two-week Information Science and Technology course in a middle school, involving three classes with 45, 41 and 45 students, respectively. To ensure consistency in teaching styles, all classes were taught by the same experienced teacher. Data collection included a knowledge test to assess knowledge mastery, as well as questionnaires to measure learning satisfaction and engagement. The collected data were analysed using one-way ANOVA to compare the effectiveness of the three teaching methods.
Results and Conclusion
Compared with traditional computer-assisted teaching, Generative AI-assisted teaching can significantly enhance students' learning satisfaction, but can not improve their learning engagement and knowledge mastery level. Furthermore, in the process of Generative AI-assisted teaching, teacher supervision can significantly increase students' learning engagement and knowledge mastery compared with situations without teacher supervision. This study indicated Generative AI's potential as an educational tool and underscored the essential role of teacher supervision.
Implications
This study fills a critical gap by providing empirical evidence on how Generative AI and teacher supervision interact to improve classroom learning outcomes. It shows that Generative AI's potential to enhance learning outcomes is significantly amplified with teacher oversight.
期刊介绍：
The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
