A dialogic theoretical foundation for integrating generative AI into pedagogical design

Bibliographic Data

ID21297239
AuthorsRupert Wegerif (0000-0003-2278-2245, Faculty of Education University of Cambridge Cambridge UK, corresponding author), Imogen Casebourne (0000-0001-5595-5902, Hughes Hall University of Cambridge Cambridge UK)
Year2026
Volume57
Issue3
Pages639-654
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.70026
OpenAlexW4416304339
LanguageEN
Citations received4
References cited35

Generative AI presents a profound challenge to the existing structures and purposes of education. It forces us to reconsider not only how we teach and learn but also, more fundamentally, what education is for. This conceptual paper argues that, in order to integrate AI into education in a way that can meet the major challenges facing humanity, ranging from ecological crisis to the future of democratic societies, we must reframe education. Drawing on the nature and potential of generative AI in conjunction with educational theory, we propose a double dialogic pedagogy that recognises education as both teaching thinking through dialogue and inducting students into participation in the long‐term powerful dialogues of culture. We relate this double dialogic pedagogy with AI to education for collective intelligence. This pedagogy positions AI not as a replacement for human thinking, but as a partner in expanding the space of dialogue. By articulating this theoretical foundation, we offer a basis for the future design of educational practices and technologies that can support human flourishing in an age of accelerating technological change. Practitioner notes What is already known about this topic The rise of generative AI, particularly large language models, poses both opportunities and challenges for education. While existing research highlights AI's potential to support personalised learning through intelligent tutoring systems, concerns remain about its possible negative impact on students' critical and creative thinking. Previous studies have also noted the need for educational theory to keep pace with technological developments, yet little work has been done to articulate a pedagogical framework that addresses the broader societal and environmental challenges of the AI era. What this paper adds This paper proposes a double dialogic pedagogy as a theoretical foundation for integrating AI into educational design. It situates current developments in AI within a historical and philosophical context, drawing on dialogic theory and the concept of the pharmakon to argue that AI is neither inherently beneficial nor harmful, but depends on pedagogical framing. The paper advances the field by explicitly linking AI‐supported learning to education for collective intelligence, offering concrete illustrations of how AI can support both dialogic learning and induction into long‐term cultural dialogues. Implications for practice and/or policy This paper suggests that educational practice should shift from focusing on individual knowledge acquisition towards cultivating collective intelligence through dialogic pedagogy. Policymakers and educators are encouraged to design AI‐supported learning environments that promote collaborative problem‐solving and critical engagement with real‐world challenges, such as those posed by the Anthropocene. Assessment policies should be re‐evaluated to reward collaborative inquiry and ethical reasoning, rather than individual content reproduction. This reconceptualisation positions education as a key enabler of planetary‐level self‐regulation and human flourishing in the face of technological and environmental disruptions

Citizen journalism · Cognitive reframing · Democracy · Dialogic · Educational technology · Flourishing · Foundation (evidence) · Generative grammar · Sociology of Education · AI in Service Interactions · Intelligent Tutoring Systems and Adaptive Learning · Text Readability and Simplification

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