Can Generative Artificial Intelligence be a Good Teaching Assistant?—An Empirical Analysis Based on Generative AI-Assisted Teaching

IF 5.1 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2025-05-05 DOI:10.1111/jcal.70027

Qianwen Tang, Wenbo Deng, Yidan Huang, Shuaijie Wang, Hao Zhang

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引用次数: 0

Abstract

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.

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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

生成式人工智能（AI）在增强个性化学习和提高教育效率方面显示出前景。然而，它与教育的结合引起了人们对错误信息和过度依赖的担忧，尤其是在青少年中。教师监督在减轻这些风险和确保在课堂上有效使用生成式人工智能方面发挥着关键作用。尽管对生成式人工智能的兴趣日益浓厚，但对其实际影响和教师监督作用的实证研究有限。本研究的目的是系统地评估生成式人工智能在课堂教学中的作用，特别关注教师监督如何影响其有效性。方法采用准实验设计，考察传统计算机辅助教学、无教师监督的生成式人工智能辅助教学和有教师监督的生成式人工智能辅助教学三种教学方式下学生学习成果的差异。该研究在一所中学为期两周的信息科学与技术课程中实施，涉及三个班级，分别有45名，41名和45名学生。为确保教学风格的一致性，所有课程均由同一位经验丰富的教师授课。数据收集包括评估知识掌握程度的知识测试，以及衡量学习满意度和参与度的问卷调查。收集的数据采用单因素方差分析比较三种教学方法的有效性。结果与结论与传统计算机辅助教学相比，生成式人工智能辅助教学可以显著提高学生的学习满意度，但不能提高学生的学习参与度和知识掌握水平。此外，在生成式人工智能辅助教学的过程中，与没有教师监督的情况相比，教师监督可以显著提高学生的学习参与度和知识掌握程度。这项研究表明了生成式人工智能作为一种教育工具的潜力，并强调了教师监督的重要作用。本研究通过提供关于生成式人工智能和教师监督如何相互作用以改善课堂学习成果的经验证据，填补了一个关键的空白。研究表明，在教师的监督下，生成式人工智能提高学习成果的潜力被大大放大。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

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