Transforming pedagogy with GenAI-supported formative assessment: Challenges for teacher education

IF 13 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
British Journal of Educational Technology Pub Date : 2026-04-19 Epub Date: 2026-02-12 DOI:10.1111/bjet.70051
Mary Webb, Arthur Galamba
{"title":"Transforming pedagogy with GenAI-supported formative assessment: Challenges for teacher education","authors":"Mary Webb,&nbsp;Arthur Galamba","doi":"10.1111/bjet.70051","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <p>This article examines the challenges primary and secondary teachers face in implementing formative assessment, with a particular focus on the use of digital technologies and the emerging potential of artificial intelligence (AI), including generative AI (GenAI) and agentic AI. Drawing on empirical research and theoretical perspectives, we explore how formative assessment—an established pedagogical practice with significant impact on student learning—has evolved alongside technological developments. We revisit a well-established framework of five key formative assessment strategies, analysing how it has been extended to integrate digital technologies and how this influences the roles of teachers, learners and tools in classroom decision-making. Our central research question asks: <i>How can GenAI be integrated into formative assessment practices to enhance student learning while supporting teacher agency and professional judgement?</i> We argue that GenAI, when critically and thoughtfully deployed, can create new opportunities for personalised feedback, dynamic learning pathways and the co-construction of knowledge between teachers and students. Moreover, GenAI's ability to support ‘moments of contingency’ enables teachers to respond more effectively to emerging learning needs, thus fostering self-regulation and deeper engagement. However, we stress that AI agency is agency without intelligence. The value of these technologies depends on how they are interpreted and implemented by educators, which requires ongoing reflection, collaboration and theoretical understanding. With deep implications for teacher education programmes, our analysis suggests that teacher quality in this evolving pedagogical landscape should be understood as adaptive, multifaceted and grounded in both technological fluency and sound formative assessment principles, moving beyond the narrow and prescriptive definitions that dominate recent educational policy in England.</p>\n </section>\n \n <section>\n \n <div>\n \n <div>\n \n <h3>Practitioner notes</h3>\n <p>What is already known about this topic\n\n </p><ul>\n \n <li>Formative assessment is well established in education, with strong empirical support showing high impact on learning.</li>\n \n <li>Digital technologies have shown more variable and generally moderate impact on learning, despite long-standing interest.</li>\n \n <li>Teachers often face challenges implementing formative assessment effectively due to limited training and systemic constraints.</li>\n \n <li>Very little is known about how generative AI (GenAI) will influence formative assessment practices, given its novelty and potential to disrupt traditional teaching roles.</li>\n </ul>\n <p>What this paper adds\n\n </p><ul>\n \n <li>This paper examines how formative assessment can be enhanced—and complicated—by digital technologies, especially new forms of AI.</li>\n \n <li>A formative assessment framework is reviewed, outlining how GenAI and <i>agentic AI</i> could support each of its five core strategies.</li>\n \n <li>The paper offers a timely analysis of AI's pedagogical potential, portraying it as a dynamic but fallible agent in learning.</li>\n </ul>\n <p>Implications for practice and/or policy\n\n </p><ul>\n \n <li>Teacher education must now include AI literacy alongside formative assessment pedagogy, enabling ethical and informed use of GenAI.</li>\n \n <li>Educators should be equipped to evaluate and adapt emerging technologies while protecting student agency and pedagogical intent.</li>\n \n <li>Schools and policymakers must foster conditions for early-career teachers to explore and refine AI-supported approaches.</li>\n \n <li>Ethical safeguards are needed to address concerns around feedback quality, learner autonomy and dependency on automation.</li>\n \n <li>Policy should promote collaborative design of pedagogies that integrate AI responsibly, centring on deep learning and teacher–student relationships.</li>\n </ul>\n </div>\n </div>\n </section>\n </div>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"57 3","pages":"690-706"},"PeriodicalIF":13.0000,"publicationDate":"2026-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.70051","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Educational Technology","FirstCategoryId":"95","ListUrlMain":"https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.70051","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0

Abstract

This article examines the challenges primary and secondary teachers face in implementing formative assessment, with a particular focus on the use of digital technologies and the emerging potential of artificial intelligence (AI), including generative AI (GenAI) and agentic AI. Drawing on empirical research and theoretical perspectives, we explore how formative assessment—an established pedagogical practice with significant impact on student learning—has evolved alongside technological developments. We revisit a well-established framework of five key formative assessment strategies, analysing how it has been extended to integrate digital technologies and how this influences the roles of teachers, learners and tools in classroom decision-making. Our central research question asks: How can GenAI be integrated into formative assessment practices to enhance student learning while supporting teacher agency and professional judgement? We argue that GenAI, when critically and thoughtfully deployed, can create new opportunities for personalised feedback, dynamic learning pathways and the co-construction of knowledge between teachers and students. Moreover, GenAI's ability to support ‘moments of contingency’ enables teachers to respond more effectively to emerging learning needs, thus fostering self-regulation and deeper engagement. However, we stress that AI agency is agency without intelligence. The value of these technologies depends on how they are interpreted and implemented by educators, which requires ongoing reflection, collaboration and theoretical understanding. With deep implications for teacher education programmes, our analysis suggests that teacher quality in this evolving pedagogical landscape should be understood as adaptive, multifaceted and grounded in both technological fluency and sound formative assessment principles, moving beyond the narrow and prescriptive definitions that dominate recent educational policy in England.

Practitioner notes

What is already known about this topic

  • Formative assessment is well established in education, with strong empirical support showing high impact on learning.
  • Digital technologies have shown more variable and generally moderate impact on learning, despite long-standing interest.
  • Teachers often face challenges implementing formative assessment effectively due to limited training and systemic constraints.
  • Very little is known about how generative AI (GenAI) will influence formative assessment practices, given its novelty and potential to disrupt traditional teaching roles.

What this paper adds

  • This paper examines how formative assessment can be enhanced—and complicated—by digital technologies, especially new forms of AI.
  • A formative assessment framework is reviewed, outlining how GenAI and agentic AI could support each of its five core strategies.
  • The paper offers a timely analysis of AI's pedagogical potential, portraying it as a dynamic but fallible agent in learning.

Implications for practice and/or policy

  • Teacher education must now include AI literacy alongside formative assessment pedagogy, enabling ethical and informed use of GenAI.
  • Educators should be equipped to evaluate and adapt emerging technologies while protecting student agency and pedagogical intent.
  • Schools and policymakers must foster conditions for early-career teachers to explore and refine AI-supported approaches.
  • Ethical safeguards are needed to address concerns around feedback quality, learner autonomy and dependency on automation.
  • Policy should promote collaborative design of pedagogies that integrate AI responsibly, centring on deep learning and teacher–student relationships.

Abstract Image

用genai支持的形成性评估转变教学法:教师教育面临的挑战
本文探讨了中小学教师在实施形成性评估时面临的挑战,特别关注数字技术的使用和人工智能(AI)的新兴潜力,包括生成式人工智能(GenAI)和代理式人工智能。借鉴实证研究和理论观点,我们探讨了形成性评估——一种对学生学习有重大影响的既定教学实践——是如何随着技术发展而演变的。我们重新审视了五种关键形成性评估策略的完善框架,分析了如何将其扩展到集成数字技术,以及这如何影响教师、学习者和课堂决策工具的角色。我们的中心研究问题是:如何将GenAI整合到形成性评估实践中,以增强学生的学习,同时支持教师代理和专业判断?我们认为,GenAI经过批判性和深思熟虑的部署,可以为个性化反馈、动态学习途径和教师与学生之间的知识共同构建创造新的机会。此外,GenAI支持“突发事件时刻”的能力使教师能够更有效地应对新出现的学习需求,从而促进自我调节和更深入的参与。然而,我们强调,人工智能机构是没有智能的机构。这些技术的价值取决于教育者如何解释和实施它们,这需要持续的反思、合作和理论理解。我们的分析对教师教育计划有着深远的影响,我们的分析表明,在这种不断发展的教学环境中,教师质量应该被理解为适应性强、多方面的,并以技术流畅性和健全的形成性评估原则为基础。超越了主导英国近期教育政策的狭隘和规范的定义。关于这个话题我们已经知道的是,形成性评估在教育中已经建立起来,有强有力的实证支持,显示出对学习的高影响。尽管人们对数字技术的兴趣由来已久,但数字技术对学习的影响更为多变,总体上也较为温和。由于培训有限和系统限制,教师经常面临有效实施形成性评估的挑战。鉴于其新颖性和颠覆传统教学角色的潜力,我们对生成式人工智能(GenAI)将如何影响形成性评估实践知之甚少。本文探讨了如何通过数字技术,特别是新形式的人工智能来加强形成性评估,并使其复杂化。审查了形成性评估框架,概述了基因人工智能和代理人工智能如何支持其五项核心战略中的每一项。这篇论文及时分析了人工智能的教学潜力,将其描述为一个动态但易出错的学习代理。教师教育现在必须包括人工智能素养和形成性评估教学法,使人工智能的使用合乎道德和知情。教育工作者应该具备评估和适应新兴技术的能力,同时保护学生的积极性和教学意图。学校和政策制定者必须为早期职业教师创造条件,以探索和完善人工智能支持的方法。需要道德保障来解决反馈质量、学习者自主权和对自动化的依赖等问题。政策应促进以深度学习和师生关系为中心,负责任地整合人工智能的教学方法的协作设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
British Journal of Educational Technology
British Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.60
自引率
4.50%
发文量
111
期刊介绍: BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书