Transforming pedagogy with GenAI ‐supported formative assessment

Challenges for teacher education

Bibliographic Data

ID21297660
AuthorsMary Webb (0000-0002-4409-5940, School of Education Communication and Society King's College London London UK, corresponding author), Arthur Galamba (0000-0003-2208-7015, School of Education Communication and Society King's College London London UK)
Year2026
Volume57
Issue3
Pages690-706
Publication date2026-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBritish Journal of Educational Technology (JOURNAL)
Journal identifiersISSN: 0007-1013 • E-ISSN: 1467-8535
PublisherWiley (PUBLISHER • GB)
DOI10.1111/bjet.70051
OpenAlexW7128728050
LanguageEN
Citations received2
References cited38

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.

Agency (philosophy) · Educational technology · Fluency · Formative assessment · Professional development · Quality (philosophy) · Teacher education · Teaching method · Literacy, Media, and Education · Reflective Practices in Education · Student Assessment and Feedback

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