Mathematics Education Researchers (MER) community
AI-driven technologies are becoming increasingly ubiquitous in educational institutions worldwide, while researchers continue to revisit and expand upon decades of critical work in the field of educational technology. GenAI has captured public and scholarly attention, but many other AI technologies are used in education, and there is a need to critically analyse the methods, techniques, and processes that constitute these technologies.
Facer and Selwyn’s (2021) reflections on “non-stupid optimism” remain instructive here, calling us to look beyond the charismatic allure of techno-fixes and instead engage with the materializing and social labor involved in fostering equitable educational practices. These ideas resonate deeply with Macgilchrist’s (2019) use of “cruel optimism” to examine the paradoxes and fantasies embedded in datafied education, where hopes for equity persist even amid market-driven logics.
The criticality reflected in such pieces has been further developed in edtech research addressing issues related to teacher professionalism (Selwyn et al., 2025), teacher autonomy and overload (Selwyn, 2022; Watters, 2021), educational imaginaries (Rahm, 2023), algorithmic surveillance (Cobo & Rivera-Vargas, 2023), and datafication (Jarke & Breiter, 2019). Global analyses further show how major technology corporations are increasingly shaping educational governance (Williamson et al., 2022), while others examine how digital platforms and infrastructural power are reconfiguring the public education and edtech landscape (Bergviken-Rensfeldt & Hillman, 2023).
Inspired by this rich body of critical work, and concerned by the rapid, often indiscriminate uptake of GenAI across society in general and the education sector in particular in recent years, we propose a special issue and accompanying workshop on what it means to be critical in critical studies of AI technologies (including GenAI) in education (cf. Macgilchrist, 2021).
This special issue aims to interrogate discourses about, and inscribed into, AI, identify assumptions about the future of education and the role of AI technologies in it, as well as creatively critique the complex, messy, and evolving socio-material entanglements in which researchers are embedded and contribute to through their participation in a dynamic and expanding educational ecosystem. More specifically, we aim to identify and reflect on the various facets that constitute a critical approach to education and technology (CSET, 2025), particularly in relation to the future of AI in education (Eben & Murphy, 2025). Within this frame of reference, we seek to deepen, further articulate, challenge, or expand the concepts and approaches developed to enable critical insights on the intensifying hype and unnuanced discourse surrounding AI and education, thereby contributing to a more rigorous debate (Cerratto Pargman et al., 2024).
The special issue aims to break the ‘self-reinforcing cycle of topical, methodological, conceptual, and linguistic convergence’ in the AI-focused scientific monoculture of contemporary research (Traberg et al., 2026). We welcome contributions, conceptual and empirical, that interrogate the science behind AI-enabled educational technologies; the knowledge, assumptions, and data science practices encoded in AI systems; and the epistemic and social controversies and dilemmas that surface when AI is used in student learning, teachers’ practices, and institutional decision-making. We encourage contributions that examine the environmental costs and their implications for human creativity.
We invite works addressing the legality of the training material used to create these systems, as well as a normative position on the use of these technologies across education sectors. We are interested in contributions that reflect on what counts as good education and what it means to learn in the age of GenAI. We also welcome work that employs speculative and imaginative methods (e.g., Ross, 2022; Vervoort et al., 2015) to explore alternative ways of thinking and envisioning AI in education, rather than solely diagnosing deficits or debunking prevailing narratives.
Finally, we look forward to submissions that critically examine how researchers, practitioners, developers, and policy-makers may inadvertently contribute to the ongoing hype surrounding e.g., GenAI or agentic AI.
The authors of the accepted manuscripts (abstracts) will be invited to participate in a workshop hosted by Stockholm University during December 10th-11th (lunch-lunch), where we will engage in collective reflection, critique, and world-making on the future of AI in education.
References:
Cobo, C. and Rivera-Vargas, P. (2023). «What Is “Algorithmic Education” and Why Do Education Institutions Need to Consolidate New Capacities? » In The New Digital Education Policy Landscape, edited by Cristóbal Cobo and Axel Rivas, 210-225. Londres, New York: Routledge.
Cerratto Pargman, T., Sporrong, E., Farazouli, A., & McGrath, C. (2024). Beyond the hype: Towards a critical debate about AI chatbots in Swedish higher education. Högre utbildning, 14(1), 74–81.
CSET collective (2025). Critical studies of education and technology … reasons to be hopeful? 9th June, [https://bridges.monash.edu/articles/report/Critical_studies_of_education_and_technology_reasons_to_be_hopeful_/29265038?file=55209389]
Facer, K., & Selwyn, N. (2021). Digital technology and the futures of education: Towards ‘Non-Stupid’optimism. Futures of Education initiative, UNESCO.
Jarke, J., and Breiter, A. “The datafication of education.” Learning, Media and Technology, 44 no. 1 (2019), 1–6.
Macgilchrist, F. (2019). Cruel optimism in edtech: When the digital data practices of educational technology providers inadvertently hinder educational equity. Learning, Media and Technology, 44(1), 77-86.
Macgilchrist, F. (2021). What is “critical” in critical studies of edtech? Three responses. Learning, Media and Technology, 46(2), 230–243.
Rahm, L. (2023). Educational imaginaries: Governance at the intersection of technology and education. Journal of Education Policy, 38(1), 46-68.
Rensfeldt, Bergviken, A., & Hillman, T. (2023). The relational powers of platforms and infrastructures played out in school: Differences and implications for teacher work. In World Yearbook of Education 2024 (pp. 106-121). Routledge.
Ross, J. (2022). Digital futures for learning: Speculative methods and pedagogies. Routledge.
Selwyn, Neil. "The Critique of Digital Education: Time for a (Post)Critical Turn." In A New Repertoire for Critique in Contemporary Education, edited by Rekha Gorur, Paolo Landri and Romuald Normand, Routledge, 2022.
Selwyn, N. (2022). Less work for teacher? The ironies of automated decision-making in schools. In Everyday automation(pp. 73-86). Routledge.
Selwyn, N., Rivera Vargas, P., & Herrera Urízar, G. (2025). Critical studies on education and technology: paths taken and futures imagined. A Dialogue with Neil Selwyn. Revista Izquierdas, 2025, num. 54, p. 1-14.
Traberg, C. S., Roozenbeek, J., & van der Linden, S. (2026). AI is turning research into a scientific monoculture. Communications Psychology, 4(1), 37.
Vervoort, J. M., Bendor, R., Kelliher, A., Strik, O., & Helfgott, A. E. (2015). Scenarios and the art of worldmaking. Futures, 74, 62-70.
Watters, A. (2021) Teaching Machines: The History of Personalized Learning. Cambridge: MIT Press.
Williamson, B., Gulson, K. N., Perrotta, C., & Witzenberger, K. (2022). Amazon and the new global connective architectures of education governance. Harvard Educational Review, 92(2), 231-256.
- Please submit a 500-700-word abstract, title, and author details to Teresa Cerratto Pargman (te...@dsv.su.se).
- Please note that all abstracts must align with the journal’s aims and scope.
- Abstracts should identify how the manuscript will make a robust theoretical, conceptual or empirically grounded contribution to contemporary debates.
- Abstracts should include a clear rationale and proposed methodological/theoretical approach.
- Authors will be notified by 30 September if their abstract has been accepted. All full manuscripts (max 8,000 words, excluding references) will be subject to double-blind peer review.
More:https://think.taylorandfrancis.com/special_issues/towards-a-critical-approach-to-ai-in-education/