GAI与教师评分：哪个更适合评估学生表现？

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2025-03-21 DOI:10.1109/TLT.2025.3572518

Xuefan Li;Marco Zappatore;Tingsong Li;Weiwei Zhang;Sining Tao;Xiaoqing Wei;Xiaoxu Zhou;Naiqing Guan;Anny Chan

{"title":"GAI与教师评分：哪个更适合评估学生表现？","authors":"Xuefan Li;Marco Zappatore;Tingsong Li;Weiwei Zhang;Sining Tao;Xiaoqing Wei;Xiaoxu Zhou;Naiqing Guan;Anny Chan","doi":"10.1109/TLT.2025.3572518","DOIUrl":null,"url":null,"abstract":"The integration of generative artificial intelligence (GAI) into educational settings offers unprecedented opportunities to enhance the efficiency of teaching and the effectiveness of learning, particularly within online platforms. This study evaluates the development and application of a customized GAI-powered teaching assistant, trained specifically to enhance teaching efficiency for educators and improve learning outcomes for students in online education. Using four Grade 12 courses (i.e., English, Mathematics, Financial Accounting, and Simplified Chinese), we assessed the performance of generative pretrained transformer (GPT)-4, GPT-4o, and the Trained-GPT model. Results demonstrate that the Trained-GPT achieved grading accuracy and consistency comparable to human teachers, with strong correlations observed in Mathematics (0.996) and English (0.874). While GPT-4o performed well in specific cases, its variability highlights areas for improvement. These findings underscore the potential of AI-powered teaching assistants to streamline grading, deliver timely feedback, and support scalable, high-quality online education.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"569-580"},"PeriodicalIF":2.9000,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GAI Versus Teacher Scoring: Which is Better for Assessing Student Performance?\",\"authors\":\"Xuefan Li;Marco Zappatore;Tingsong Li;Weiwei Zhang;Sining Tao;Xiaoqing Wei;Xiaoxu Zhou;Naiqing Guan;Anny Chan\",\"doi\":\"10.1109/TLT.2025.3572518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The integration of generative artificial intelligence (GAI) into educational settings offers unprecedented opportunities to enhance the efficiency of teaching and the effectiveness of learning, particularly within online platforms. This study evaluates the development and application of a customized GAI-powered teaching assistant, trained specifically to enhance teaching efficiency for educators and improve learning outcomes for students in online education. Using four Grade 12 courses (i.e., English, Mathematics, Financial Accounting, and Simplified Chinese), we assessed the performance of generative pretrained transformer (GPT)-4, GPT-4o, and the Trained-GPT model. Results demonstrate that the Trained-GPT achieved grading accuracy and consistency comparable to human teachers, with strong correlations observed in Mathematics (0.996) and English (0.874). While GPT-4o performed well in specific cases, its variability highlights areas for improvement. These findings underscore the potential of AI-powered teaching assistants to streamline grading, deliver timely feedback, and support scalable, high-quality online education.\",\"PeriodicalId\":49191,\"journal\":{\"name\":\"IEEE Transactions on Learning Technologies\",\"volume\":\"18 \",\"pages\":\"569-580\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Learning Technologies\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11008832/\",\"RegionNum\":3,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/11008832/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

将生成式人工智能（GAI）整合到教育环境中，为提高教学效率和学习效果提供了前所未有的机会，特别是在在线平台中。本研究评估了一种定制的基于人工智能的教学助手的开发和应用，专门用于提高教育工作者的教学效率，改善在线教育中学生的学习成果。使用四门12年级课程（即英语、数学、财务会计和简体中文），我们评估了生成预训练变压器(GPT)-4、GPT- 40和训练后的GPT模型的性能。结果表明，training - gpt的评分准确性和一致性与真人教师相当，数学（0.996）和英语（0.874）具有很强的相关性。虽然gpt - 40在特定情况下表现良好，但其可变性突出了需要改进的领域。这些发现强调了人工智能教学助理在简化评分、提供及时反馈和支持可扩展的高质量在线教育方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

GAI Versus Teacher Scoring: Which is Better for Assessing Student Performance?

The integration of generative artificial intelligence (GAI) into educational settings offers unprecedented opportunities to enhance the efficiency of teaching and the effectiveness of learning, particularly within online platforms. This study evaluates the development and application of a customized GAI-powered teaching assistant, trained specifically to enhance teaching efficiency for educators and improve learning outcomes for students in online education. Using four Grade 12 courses (i.e., English, Mathematics, Financial Accounting, and Simplified Chinese), we assessed the performance of generative pretrained transformer (GPT)-4, GPT-4o, and the Trained-GPT model. Results demonstrate that the Trained-GPT achieved grading accuracy and consistency comparable to human teachers, with strong correlations observed in Mathematics (0.996) and English (0.874). While GPT-4o performed well in specific cases, its variability highlights areas for improvement. These findings underscore the potential of AI-powered teaching assistants to streamline grading, deliver timely feedback, and support scalable, high-quality online education.

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

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.