Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands
Generative AI (genAI) tools have involved rapid and broad uptake since their wide release in late 2022, including among teachers. We investigated several factors that play a role in teachers’ motivation and engagement to harness genAI in teaching and learning. We examined contextual factors (in-scho...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Computers and Education: Artificial Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X2400136X |
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| author | Rebecca J. Collie Andrew J. Martin Dragan Gasevic |
| author_facet | Rebecca J. Collie Andrew J. Martin Dragan Gasevic |
| author_sort | Rebecca J. Collie |
| collection | DOAJ |
| description | Generative AI (genAI) tools have involved rapid and broad uptake since their wide release in late 2022, including among teachers. We investigated several factors that play a role in teachers’ motivation and engagement to harness genAI in teaching and learning. We examined contextual factors (in-school support to apply genAI, time pressure, disruptive student behavior) as predictors of motivation (genAI self-efficacy and genAI valuing) and, in turn, engagement (i.e., genAI integration in teaching-related work and student learning activities) over the course of one school term. Among 368 Australian primary and secondary school teachers, our findings revealed that genAI support was associated with greater genAI self-efficacy and genAI valuing. Time pressure was also linked with greater genAI valuing, whereas disruptive student behavior was not linked with the genAI motivation or engagement variables. In turn, genAI self-efficacy was linked with greater levels of both types of genAI integration. GenAI valuing was associated with greater genAI integration in teaching-related work only. Our results provide knowledge about factors relevant for supporting genAI and its application among teachers in Australia—and also hold relevance to teachers in other countries. |
| format | Article |
| id | doaj-art-72e19873926f427ea8de3f82e3ebd10c |
| institution | DOAJ |
| issn | 2666-920X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj-art-72e19873926f427ea8de3f82e3ebd10c2025-08-20T02:52:28ZengElsevierComputers and Education: Artificial Intelligence2666-920X2024-12-01710033310.1016/j.caeai.2024.100333Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demandsRebecca J. Collie0Andrew J. Martin1Dragan Gasevic2School of Education, University of New South Wales, Australia; Corresponding author. School of Education, University of New South Wales, NSW, 2052, Australia.School of Education, University of New South Wales, AustraliaFaculty of Information Technology, Monash University, AustraliaGenerative AI (genAI) tools have involved rapid and broad uptake since their wide release in late 2022, including among teachers. We investigated several factors that play a role in teachers’ motivation and engagement to harness genAI in teaching and learning. We examined contextual factors (in-school support to apply genAI, time pressure, disruptive student behavior) as predictors of motivation (genAI self-efficacy and genAI valuing) and, in turn, engagement (i.e., genAI integration in teaching-related work and student learning activities) over the course of one school term. Among 368 Australian primary and secondary school teachers, our findings revealed that genAI support was associated with greater genAI self-efficacy and genAI valuing. Time pressure was also linked with greater genAI valuing, whereas disruptive student behavior was not linked with the genAI motivation or engagement variables. In turn, genAI self-efficacy was linked with greater levels of both types of genAI integration. GenAI valuing was associated with greater genAI integration in teaching-related work only. Our results provide knowledge about factors relevant for supporting genAI and its application among teachers in Australia—and also hold relevance to teachers in other countries.http://www.sciencedirect.com/science/article/pii/S2666920X2400136XGenerative AITeachersMotivationEngagementIntegrationJob demands |
| spellingShingle | Rebecca J. Collie Andrew J. Martin Dragan Gasevic Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands Computers and Education: Artificial Intelligence Generative AI Teachers Motivation Engagement Integration Job demands |
| title | Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands |
| title_full | Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands |
| title_fullStr | Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands |
| title_full_unstemmed | Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands |
| title_short | Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands |
| title_sort | teachers generative ai self efficacy valuing and integration at work examining job resources and demands |
| topic | Generative AI Teachers Motivation Engagement Integration Job demands |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X2400136X |
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