Generative AI, Teacher Knowledge and Educational Research: Bridging Short- and Long-Term Perspectives.

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Title: Generative AI, Teacher Knowledge and Educational Research: Bridging Short- and Long-Term Perspectives.
Authors: Mishra, Punya1 (AUTHOR) punya.mishra@asu.edu, Oster, Nicole1 (AUTHOR) nicole.oster@asu.edu, Henriksen, Danah1 (AUTHOR) danah.henriksen@asu.edu
Source: TechTrends: Linking Research & Practice to Improve Learning. Mar2024, Vol. 68 Issue 2, p205-210. 6p.
Subject Terms: *Generative artificial intelligence, *Education research, *Teachers, *Teacher education, *Educational technology, Futures
Abstract: This article reflects on the transformative nature of generative AI (GenAI) tools for teaching and teacher education, both reflecting on current innovation and consider future potentials and challenges. In that sense, we aim to position the field of education going forward with the implications of new technologies like GenAI for education and educational research. We argue the need for a dual-lens approach. First and foremost, practice and research should focus on the here-and-now, i.e. how to design powerful learning experiences for pre-service and in-service teachers for them to be productive, creative, critical, and ethical users. But there is also a need for a deeper, longer view—based on sociological and historical trends and patterns that will influence the socio-techno-cultural matrix within which education functions in the long term. We begin with a brief introduction to GenAI technologies. This is followed by an in-depth discussion of the fundamental nature of GenAI tools—their similarities and differences to prior technologies, and the implications for teacher education and research. [ABSTRACT FROM AUTHOR]
Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Generative AI, Teacher Knowledge and Educational Research: Bridging Short- and Long-Term Perspectives.
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  Data: <searchLink fieldCode="JN" term="%22TechTrends%3A+Linking+Research+%26+Practice+to+Improve+Learning%22">TechTrends: Linking Research & Practice to Improve Learning</searchLink>. Mar2024, Vol. 68 Issue 2, p205-210. 6p.
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  Data: This article reflects on the transformative nature of generative AI (GenAI) tools for teaching and teacher education, both reflecting on current innovation and consider future potentials and challenges. In that sense, we aim to position the field of education going forward with the implications of new technologies like GenAI for education and educational research. We argue the need for a dual-lens approach. First and foremost, practice and research should focus on the here-and-now, i.e. how to design powerful learning experiences for pre-service and in-service teachers for them to be productive, creative, critical, and ethical users. But there is also a need for a deeper, longer view—based on sociological and historical trends and patterns that will influence the socio-techno-cultural matrix within which education functions in the long term. We begin with a brief introduction to GenAI technologies. This is followed by an in-depth discussion of the fundamental nature of GenAI tools—their similarities and differences to prior technologies, and the implications for teacher education and research. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Teachers
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              Text: Mar2024
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