Codevelop Learning Tasks With Generative AI: Mapping Challenges, Gains, and Professional Development Needs

IF 7.4 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
IEEE Transactions on Learning Technologies Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI:10.1109/TLT.2026.3675467
Jie Cao;Shuman Wang;Xian Chen;Christian Schunn
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Abstract

The design of learning tasks represents a fundamental pedagogical process that directly shapes educational effectiveness, but this process is time-consuming and knowledge-intensive for teachers. The rapid emergence of generative artificial intelligence (GenAI) technologies has created unprecedented opportunities for supporting teachers’ teaching practice. However, despite growing interest in teacher–GenAI collaboration, current research primarily focuses on preservice teachers and overall lesson plan design, with limited attention to challenges inherent in teacher–GenAI codesign of learning tasks or what teachers learn from such collaboration. To address this gap, we employed a mixed-methods approach involving 28 in-service teachers who collaborated with GenAI tools to codevelop learning tasks across three weeks. Qualitative analyses identified four overarching interaction challenges: inefficient interactions, low-quality tasks, lack of distributed cognition, and negative human–GenAI feedback loops. These overarching challenges emerged from bilateral deficiencies—three teacher-related shortcomings (providing too little context information, providing too little pedagogical guidance, and lacking GenAI skills) and six GenAI-related shortcomings (accuracy issues, limited prompt understanding, weak knowledge of content sequences, limited context knowledge, incomplete instructional solutions, and usability issues). Quantitative analyses of changes from pre to post showed significant improvements in teachers’ technical knowledge and artificial intelligence (AI) literacy, with no changes in self-efficacy and pedagogical knowledge. Finally, teachers expressed professional development needs primarily in two areas: AI foundations and applications and AI pedagogy. This study provides empirical evidence of bidirectional challenges in teacher–GenAI collaboration in designing learning tasks and offers a framework for understanding teacher professional growth through AI collaboration and further needs.
与生成式人工智能共同开发学习任务:映射挑战,收益和专业发展需求
学习任务的设计代表了一个基本的教学过程,它直接影响了教育的有效性,但这个过程对教师来说是耗时和知识密集型的。生成式人工智能(GenAI)技术的迅速出现为支持教师的教学实践创造了前所未有的机会。然而,尽管人们对教师与人工智能的合作越来越感兴趣,但目前的研究主要集中在职前教师和整体教案设计上,很少关注教师与人工智能共同设计学习任务所固有的挑战,也很少关注教师从这种合作中学到了什么。为了解决这一差距,我们采用了一种混合方法,涉及28名在职教师,他们与GenAI工具合作,在三周内共同开发学习任务。定性分析确定了四个主要的交互挑战:低效的交互、低质量的任务、缺乏分布式认知和负的人类-基因反馈循环。这些总体挑战来自双边缺陷——三个与教师相关的缺点(提供的上下文信息太少,提供的教学指导太少,缺乏GenAI技能)和六个与GenAI相关的缺点(准确性问题,有限的快速理解,对内容序列的认识薄弱,有限的上下文知识,不完整的教学解决方案和可用性问题)。定量分析显示,教师的技术知识和人工智能素养显著提高,自我效能感和教学知识没有变化。最后,教师主要在两个方面表达了专业发展需求:人工智能基础与应用和人工智能教学法。本研究提供了教师与人工智能合作在设计学习任务时所面临的双向挑战的实证证据,并为理解通过人工智能合作实现教师专业成长和进一步需求提供了一个框架。
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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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