Making time for what matters? Changing patterns of teachers’ work with and through generative AI

In the current context of teacher recruitment and retention crises, government policies and technology companies are promoting the use of generative AI (gAI) as a solution to unsustainable teacher workloads, promising to “free them up to focus on what matters most” (Gates, 2024), reallocating time spent on lesson planning, marking and administration to direct interaction with students (Koop &amp; Thomsen, 2023) . Substantial reductions in teacher workload with gAI use are yet to be evidenced, and other social, economic and political responses to workload overlooked, with gAI perhaps better understood as a political choice of “automated austerity schooling” (Williamson, 2024). Such choices open up education markets and promise to create lifelong users for as-yet-unprofitable gAI businesses, while potentially reducing teachers’ judgment and agency, leading to further de-professionalisation (Selwyn et al., 2023)<br/><br/>Drawing on theorisations of teachers’ professional work and labour as a dynamic sociomaterial enactment (Fenwick 2016), and reflecting on recent pilot interview (5 participants) and survey (n=53) data from UK secondary teachers, we discuss how teachers are currently negotiating with gAI, and raise critical questions for how teachers’ work may be changing.<br/><br/>Specifically, we focus on three key areas: Firstly, how using gAI is impacting on workload, with modest time-savings balanced against additional tasks of refining, reviewing and negotiating with gAI outputs. Secondly, how teachers professional learning and developing expertise is challenged through use of genAI. Finally, how, in contrast to policy and tech visions, teachers saw lesson planning and feedback as part of the relational, personal and creative elements that make teaching meaningful. These insights raise critical questions about the future of teachers’ labour, work and learning, that have significant implications for our understanding of policy and investment in teaching and teacher education.

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Publication Details

Journal
Bristol Research (University of Bristol)
Published
2025-06-20
Primary Topic
Educational Theory and Curriculum Studies
Type
article
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article

Making time for what matters? Changing patterns of teachers’ work with and through generative AI

Lyndsay Grant, Nicola Warren-Lee
Bristol Research (University of Bristol)
Educational Theory and Curriculum Studies
article

Making time for what matters? Changing patterns of teachers’ work with and through generative AI

Lyndsay Grant, Nicola Warren-Lee
article en

Abstract

In the current context of teacher recruitment and retention crises, government policies and technology companies are promoting the use of generative AI (gAI) as a solution to unsustainable teacher workloads, promising to “free them up to focus on what matters most” (Gates, 2024), reallocating time spent on lesson planning, marking and administration to direct interaction with students (Koop &amp; Thomsen, 2023) . Substantial reductions in teacher workload with gAI use are yet to be evidenced, and other social, economic and political responses to workload overlooked, with gAI perhaps better understood as a political choice of “automated austerity schooling” (Williamson, 2024). Such choices open up education markets and promise to create lifelong users for as-yet-unprofitable gAI businesses, while potentially reducing teachers’ judgment and agency, leading to further de-professionalisation (Selwyn et al., 2023)<br/><br/>Drawing on theorisations of teachers’ professional work and labour as a dynamic sociomaterial enactment (Fenwick 2016), and reflecting on recent pilot interview (5 participants) and survey (n=53) data from UK secondary teachers, we discuss how teachers are currently negotiating with gAI, and raise critical questions for how teachers’ work may be changing.<br/><br/>Specifically, we focus on three key areas: Firstly, how using gAI is impacting on workload, with modest time-savings balanced against additional tasks of refining, reviewing and negotiating with gAI outputs. Secondly, how teachers professional learning and developing expertise is challenged through use of genAI. Finally, how, in contrast to policy and tech visions, teachers saw lesson planning and feedback as part of the relational, personal and creative elements that make teaching meaningful. These insights raise critical questions about the future of teachers’ labour, work and learning, that have significant implications for our understanding of policy and investment in teaching and teacher education.

Bristol Research (University of Bristol)
Openalex Percentile: Top 11%
Educational Theory and Curriculum Studies
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Making time for what matters? Changing patterns of teachers’ work with and through generative AI — Lyndsay Grant, Nicola Warren-Lee · Bristol Research (University of Bristol) (2025) | TGRS Research Map | TGRS