Pub Date : 2025-05-27DOI: 10.1177/00224871251336875
Kennedy Kam Ho Chan, Jianyun She, Jan van Driel, Xiang Hu
Although teacher noticing is regarded as a critical component of teacher expertise that matters for student learning, empirical evidence verifying the relationship between teacher noticing and student learning outcomes is scare. Using a relatively large-scale sample involving 189 middle school biology teachers from 156 schools and their students ( n = 7,086), this quantitative study investigated the association between teachers’ level of noticing, measured by a standardized video-based instrument, and student achievement. An analysis utilizing two-level hierarchical linear modeling revealed a significant and positive association between teachers’ level of noticing and student learning outcomes. Specifically, the proportion of variance explained was 0.383. Our study offers robust and timely evidence that verifies the core assumption underpinning teacher noticing research as well as professional development efforts focusing on teacher noticing. Implications for teacher noticing research and teacher professional development are discussed.
{"title":"Does Teacher Noticing Matter for Students’ Science Content Learning Outcomes? Evidence From a Large-Scale Empirical Study","authors":"Kennedy Kam Ho Chan, Jianyun She, Jan van Driel, Xiang Hu","doi":"10.1177/00224871251336875","DOIUrl":"https://doi.org/10.1177/00224871251336875","url":null,"abstract":"Although teacher noticing is regarded as a critical component of teacher expertise that matters for student learning, empirical evidence verifying the relationship between teacher noticing and student learning outcomes is scare. Using a relatively large-scale sample involving 189 middle school biology teachers from 156 schools and their students ( <jats:italic>n</jats:italic> = 7,086), this quantitative study investigated the association between teachers’ level of noticing, measured by a standardized video-based instrument, and student achievement. An analysis utilizing two-level hierarchical linear modeling revealed a significant and positive association between teachers’ level of noticing and student learning outcomes. Specifically, the proportion of variance explained was 0.383. Our study offers robust and timely evidence that verifies the core assumption underpinning teacher noticing research as well as professional development efforts focusing on teacher noticing. Implications for teacher noticing research and teacher professional development are discussed.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"5 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144153937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1177/00224871251324713
Punya Mishra, Jon Margerum-Leys, Guy Trainin, Valerie Hill-Jackson, Laurie Bobley, Peña L. Bedesem, John A. Williams, Cheryl J. Craig
{"title":"Teacher Education in the Age of Generative Artificial Intelligence: Introducing the Special Issue","authors":"Punya Mishra, Jon Margerum-Leys, Guy Trainin, Valerie Hill-Jackson, Laurie Bobley, Peña L. Bedesem, John A. Williams, Cheryl J. Craig","doi":"10.1177/00224871251324713","DOIUrl":"https://doi.org/10.1177/00224871251324713","url":null,"abstract":"","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"6 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-30DOI: 10.1177/00224871251325079
Eunhye Flavin, Sunghwan Hwang, Melita Morales
Generative artificial intelligence (AI)-powered conversation agents such as ChatGPT are increasingly being used in teacher education. Although ChatGPT can provide ample resources for lesson planning, little attention has been paid to how teacher candidates construct prompts and evaluate AI-generated outputs in real time to develop lesson plans. Taking up Bisconti et al.’s conceptualization of generative AI as a social agent in a sociotechnical system, this article investigates how knowledge construction is dynamic and negotiated through interaction when using ChatGPT to develop lesson plans. We applied conversation analysis through Heritage’s gradient model of epistemic stance––which captures moment-to-moment expressions of social relationships––to analyze how teacher candidates position their knowledge in relation to that acquired from ChatGPT, as managed through their prompts and questions. Our findings aim to offer insights to help teacher educators develop scaffolds for teacher candidates to critically curate content from AI-powered agents and identify areas needing further instructional support.
{"title":"“Let’s Ask the Robot!”: Epistemic Stance Between Teacher Candidates Toward AI in Mathematics Lesson Planning","authors":"Eunhye Flavin, Sunghwan Hwang, Melita Morales","doi":"10.1177/00224871251325079","DOIUrl":"https://doi.org/10.1177/00224871251325079","url":null,"abstract":"Generative artificial intelligence (AI)-powered conversation agents such as ChatGPT are increasingly being used in teacher education. Although ChatGPT can provide ample resources for lesson planning, little attention has been paid to how teacher candidates construct prompts and evaluate AI-generated outputs in real time to develop lesson plans. Taking up Bisconti et al.’s conceptualization of generative AI as a social agent in a sociotechnical system, this article investigates how knowledge construction is dynamic and negotiated through interaction when using ChatGPT to develop lesson plans. We applied conversation analysis through Heritage’s gradient model of epistemic stance––which captures moment-to-moment expressions of social relationships––to analyze how teacher candidates position their knowledge in relation to that acquired from ChatGPT, as managed through their prompts and questions. Our findings aim to offer insights to help teacher educators develop scaffolds for teacher candidates to critically curate content from AI-powered agents and identify areas needing further instructional support.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"28 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-30DOI: 10.1177/00224871251325065
Stefanie Panke
The autoethnographic study investigates the transformative impact of generative AI on educational research, instructional design, and teaching practices over a 5-month period (May–October 2024). By integrating AI tools into every phase of the research process, the study examines AI’s role as both a research partner and a subject of inquiry. Field notes, queries, and AI-generated outputs were systematically collected, creating a corpus for analysis. Grounded in activity theory, this research offers a reflective narrative on the evolving work routines of instructional designers and educators, emphasizing the orchestration of technology rather than prescriptive best practices. The study contributes to educational technology research by documenting the use of AI at a specific point in time, providing a foundation for future inquiry into the practical implications of AI in education.
{"title":"How Can (A)I Research This? An Autoethnographic Exploration of Generative AI in Research, Teaching and Instructional Design","authors":"Stefanie Panke","doi":"10.1177/00224871251325065","DOIUrl":"https://doi.org/10.1177/00224871251325065","url":null,"abstract":"The autoethnographic study investigates the transformative impact of generative AI on educational research, instructional design, and teaching practices over a 5-month period (May–October 2024). By integrating AI tools into every phase of the research process, the study examines AI’s role as both a research partner and a subject of inquiry. Field notes, queries, and AI-generated outputs were systematically collected, creating a corpus for analysis. Grounded in activity theory, this research offers a reflective narrative on the evolving work routines of instructional designers and educators, emphasizing the orchestration of technology rather than prescriptive best practices. The study contributes to educational technology research by documenting the use of AI at a specific point in time, providing a foundation for future inquiry into the practical implications of AI in education.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"50 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-24DOI: 10.1177/00224871251325073
Melissa Warr, Marie K. Heath
In this article, we explore the concept of a “hidden curriculum” within generative AI, specifically Large Language Models (LLMs), and its intersection with the hidden curriculum in education. We highlight how AI, trained on biased human data, can perpetuate societal inequities and discriminatory practices despite appearing objective. We present a technology audit that examines how LLMs score and provide feedback on student writing samples paired with student descriptions. Findings reveal that LLMs exhibit implicit biases, such as assigning lower scores when students are said to attend an “inner-city school” or prefer rap music. In addition, the feedback text given to passages said to be written by Black and Hispanic students displayed higher levels of clout or authority, mirroring and legitimizing power dynamics of schooling. We conclude by discussing implications of these findings for teacher education, policy, and research, emphasizing the need to address AI’s hidden curriculum to avoid perpetuating educational inequality.
{"title":"Uncovering the Hidden Curriculum in Generative AI: A Reflective Technology Audit for Teacher Educators","authors":"Melissa Warr, Marie K. Heath","doi":"10.1177/00224871251325073","DOIUrl":"https://doi.org/10.1177/00224871251325073","url":null,"abstract":"In this article, we explore the concept of a “hidden curriculum” within generative AI, specifically Large Language Models (LLMs), and its intersection with the hidden curriculum in education. We highlight how AI, trained on biased human data, can perpetuate societal inequities and discriminatory practices despite appearing objective. We present a technology audit that examines how LLMs score and provide feedback on student writing samples paired with student descriptions. Findings reveal that LLMs exhibit implicit biases, such as assigning lower scores when students are said to attend an “inner-city school” or prefer rap music. In addition, the feedback text given to passages said to be written by Black and Hispanic students displayed higher levels of clout or authority, mirroring and legitimizing power dynamics of schooling. We conclude by discussing implications of these findings for teacher education, policy, and research, emphasizing the need to address AI’s hidden curriculum to avoid perpetuating educational inequality.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"8 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-19DOI: 10.1177/00224871251325109
Ling Zhang, Zijun Yao, Arya Hadizadeh Moghaddam
Educator preparation, personalized learning (PL) implementation, and applications of Generative AI converge as three interrelated systems that, when carefully designed, can help achieve the long-sought goal of providing inclusive education for all learners. However, realizing this potential comes with challenges resulting from theoretical complexities and technological constraints. This article provides a theoretical analysis of the complex interconnectedness among these systems guided by the Cultural-Historical Activity Theory (CHAT). Building on the analysis, we introduce CoPL, a multi-agent system consisting of multiple agents with distinct functions that facilitate the complex PL design and engage pre-service teachers (PSTs) in dynamic conversations while prompting them to reflect on the inclusivity of agent-generated instructional suggestions. We describe the affordances and limitations of the system as a professional learning tool for PSTs to develop competencies for designing inclusive PL to meet diverse learning needs of all learners. Finally, we discuss future research on refining CoPL and its practical applications.
{"title":"Designing GenAI Tools for Personalized Learning Implementation: Theoretical Analysis and Prototype of a Multi-Agent System","authors":"Ling Zhang, Zijun Yao, Arya Hadizadeh Moghaddam","doi":"10.1177/00224871251325109","DOIUrl":"https://doi.org/10.1177/00224871251325109","url":null,"abstract":"Educator preparation, personalized learning (PL) implementation, and applications of Generative AI converge as three interrelated systems that, when carefully designed, can help achieve the long-sought goal of providing inclusive education for all learners. However, realizing this potential comes with challenges resulting from theoretical complexities and technological constraints. This article provides a theoretical analysis of the complex interconnectedness among these systems guided by the Cultural-Historical Activity Theory (CHAT). Building on the analysis, we introduce CoPL, a multi-agent system consisting of multiple agents with distinct functions that facilitate the complex PL design and engage pre-service teachers (PSTs) in dynamic conversations while prompting them to reflect on the inclusivity of agent-generated instructional suggestions. We describe the affordances and limitations of the system as a professional learning tool for PSTs to develop competencies for designing inclusive PL to meet diverse learning needs of all learners. Finally, we discuss future research on refining CoPL and its practical applications.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"34 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1177/00224871251325058
Danah Henriksen, Edwin Creely, Natalie Gruber, Sean Leahy
This article provides a critical thematic literature review that explores the intersection of generative artificial intelligence (GenAI) and social-emotional learning (SEL), analyzing its implications for teacher education. GenAI offers promising applications for enhancing SEL competencies such as self-awareness, empathy, and social skills through tools like real-time emotional feedback and personalized learning experiences. However, the integration of GenAI into SEL also presents significant challenges, including risks of depersonalization, algorithmic bias, and privacy concerns. This paper introduces a conceptual framework designed to prepare both pre-service and in-service teachers to navigate these complexities, emphasizing ethical considerations, human oversight, and cultural sensitivity. The framework highlights strategies to operationalize cultural sensitivity within AI systems, recognizing the limitations of current technologies in accounting for diverse social and emotional norms. By addressing both opportunities and risks, we aim to provide a balanced analysis of GenAI’s potential in SEL as well as guidance for teacher education programs.
{"title":"Social-Emotional Learning and Generative AI: A Critical Literature Review and Framework for Teacher Education","authors":"Danah Henriksen, Edwin Creely, Natalie Gruber, Sean Leahy","doi":"10.1177/00224871251325058","DOIUrl":"https://doi.org/10.1177/00224871251325058","url":null,"abstract":"This article provides a critical thematic literature review that explores the intersection of generative artificial intelligence (GenAI) and social-emotional learning (SEL), analyzing its implications for teacher education. GenAI offers promising applications for enhancing SEL competencies such as self-awareness, empathy, and social skills through tools like real-time emotional feedback and personalized learning experiences. However, the integration of GenAI into SEL also presents significant challenges, including risks of depersonalization, algorithmic bias, and privacy concerns. This paper introduces a conceptual framework designed to prepare both pre-service and in-service teachers to navigate these complexities, emphasizing ethical considerations, human oversight, and cultural sensitivity. The framework highlights strategies to operationalize cultural sensitivity within AI systems, recognizing the limitations of current technologies in accounting for diverse social and emotional norms. By addressing both opportunities and risks, we aim to provide a balanced analysis of GenAI’s potential in SEL as well as guidance for teacher education programs.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"25 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1177/00224871251325083
Melis Dilek, Evrim Baran, Ezequiel Aleman
Teacher education increasingly requires educators to engage with generative AI technologies, yet critical and reflective engagement opportunities remain scarce. While AI is often framed as a tool for automation, its broader pedagogical and ethical implications receive less attention. To address this gap, we implemented a critical co-discovery approach within an online AI in Education (AIEd) course to enhance educators’ AI literacy. This illustrative case study examines which AI literacy components can be developed through critical co-discovery and how this approach fosters educators’ reflective, critical, and participatory engagement with AI. Findings revealed that through co-discovery activities, educators co-constructed an understanding of AI concepts, ethical considerations, and context-specific applications. The study highlights the need for prolonged engagement with AI literacy by integrating it into teacher education program to ensure educators can critically navigate and assert their agency in AI’s complex role in education.
{"title":"AI Literacy in Teacher Education: Empowering Educators Through Critical Co-Discovery","authors":"Melis Dilek, Evrim Baran, Ezequiel Aleman","doi":"10.1177/00224871251325083","DOIUrl":"https://doi.org/10.1177/00224871251325083","url":null,"abstract":"Teacher education increasingly requires educators to engage with generative AI technologies, yet critical and reflective engagement opportunities remain scarce. While AI is often framed as a tool for automation, its broader pedagogical and ethical implications receive less attention. To address this gap, we implemented a critical co-discovery approach within an online AI in Education (AIEd) course to enhance educators’ AI literacy. This illustrative case study examines which AI literacy components can be developed through critical co-discovery and how this approach fosters educators’ reflective, critical, and participatory engagement with AI. Findings revealed that through co-discovery activities, educators co-constructed an understanding of AI concepts, ethical considerations, and context-specific applications. The study highlights the need for prolonged engagement with AI literacy by integrating it into teacher education program to ensure educators can critically navigate and assert their agency in AI’s complex role in education.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"70 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1177/00224871251321898
Michael S. Putman, Drew Polly, Miranda S. Fitzgerald
This research reports on a 3-year project involving the implementation of an intentionally designed year-long clinical experience delivered within a school–university partnership. Research questions focused on the extent the organizational facets of the experience contributed to the development of a community of practice (CoP) and how candidates’ perceptions of their preparation were associated with components of the CoP. Findings demonstrated the importance of candidates building foundational relationships with school personnel and peers and the impact of supported, authentic practice. Implications are discussed as related considerations to deepen partnerships and provide clinical experiences with structured opportunities for relationship building and examinations of practice.
{"title":"Moving From the Periphery to Practitioner: Investigating the Development of a Community of Practice Within a Year-Long Clinical Experience","authors":"Michael S. Putman, Drew Polly, Miranda S. Fitzgerald","doi":"10.1177/00224871251321898","DOIUrl":"https://doi.org/10.1177/00224871251321898","url":null,"abstract":"This research reports on a 3-year project involving the implementation of an intentionally designed year-long clinical experience delivered within a school–university partnership. Research questions focused on the extent the organizational facets of the experience contributed to the development of a community of practice (CoP) and how candidates’ perceptions of their preparation were associated with components of the CoP. Findings demonstrated the importance of candidates building foundational relationships with school personnel and peers and the impact of supported, authentic practice. Implications are discussed as related considerations to deepen partnerships and provide clinical experiences with structured opportunities for relationship building and examinations of practice.","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"36 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-18DOI: 10.1177/00224871251314921
Cheryl J. Craig, John A. Williams, Valerie Hill-Jackson
{"title":"Dead Spaces in Teaching and Teacher Education: What Are They? How Can They Be Overcome?","authors":"Cheryl J. Craig, John A. Williams, Valerie Hill-Jackson","doi":"10.1177/00224871251314921","DOIUrl":"https://doi.org/10.1177/00224871251314921","url":null,"abstract":"","PeriodicalId":17162,"journal":{"name":"Journal of Teacher Education","volume":"36 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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