培养学生教师的专业发展:指导生成式人工智能(AI)学习者解决数学问题

IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xiuling He;Ruijie Zhou;Qiong Fan;Xiong Xiao;Ying Yu;Zhonghua Yan
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引用次数: 0

摘要

快速的技术进步正在重塑教学专业知识的发展,为教育工作者提供了21世纪专业能力的新途径。本研究提出一种创新的人工智能(AI)驱动的专业发展方法,并探讨其对学生教师能力发展的影响。总共有28名三年级学生教师参与了指导人工智能学习者的任务,并应用了导师获得的知识和技能。任务绩效和任务过程分别用来描述教师的知识和教学实践,同时对专业发展调查的数据进行了深入分析,以深入了解教师的观点。研究发现,人工智能教学实践显著促进了参与者的知识获取。值得注意的是,高绩效团队展示了强调过程性指导的复杂指导模式。相反,表现较差的一组更喜欢指导性和事实性的方法,他们的行为模式显得不那么重要。此外,人工智能教学实践对学生教师的专业知识观和人工智能素养也有积极的影响。本研究的结果有助于对人工智能学习活动融入教师教育的理论和实践理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preparing Student Teachers for Professional Development: Mentoring Generative Artificial Intelligence (AI) Learners in Mathematical Problem Solving
Rapid technological advancements are reshaping pedagogical expertise development, offering novel pathways to equip educators with 21st-century professional competencies. This study proposes an innovative artificial intelligence (AI)-driven professional development approach and investigates its impact on student teachers’ competence development. In total, 28 third-year student teachers participated in tasks to mentor AI learners, applying mentor-acquired knowledge and skills. Task performance and task processes were used to delineate teacher knowledge and teaching practices, respectively, while data from professional development surveys were thoroughly analyzed to gain in-depth insights into teacher perspectives. Findings reveal that AI teaching practice significantly enhanced participants’ knowledge acquisition. Notably, high-performance groups demonstrated complex mentoring patterns emphasizing procedural mentoring. Conversely, the low-performance group preferred a more directive and factual approach, whose behavioral patterns appeared less significant. Furthermore, AI teaching practice also had a positive effect on student teachers’ perspectives toward professional knowledge and AI literacy. The findings of this study contribute to the theoretical and practical understanding of integrating AI-based learning activities into teacher education.
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来源期刊
IEEE Transactions on Learning Technologies
IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
7.50
自引率
5.40%
发文量
82
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.
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