{"title":"Codevelop Learning Tasks With Generative AI: Mapping Challenges, Gains, and Professional Development Needs","authors":"Jie Cao;Shuman Wang;Xian Chen;Christian Schunn","doi":"10.1109/TLT.2026.3675467","DOIUrl":null,"url":null,"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.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"215-228"},"PeriodicalIF":7.4000,"publicationDate":"2026-01-01","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/11441993/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/3/18 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
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.