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Huw Davies, Lecturer in Digital Education (Data and Society), Centre for Research in Digital Education, University of Edinburgh
Yan Shvartshnaider, Assistant Professor, Department of Electrical Engineering and Computer Science, Lassonde School of Engineering at York University
Introduction
The advent of AI, particularly Large Language Models, have brought many celebratory and borderline utopian claims of the technologies’ promise and potential to democratize education and reduce inequalities by offering all learners low-cost, high-quality, personalized educational resources, knowledge and tools at their convenience. However, given that educational technologies have an uneven record of delivering such declared benefits in the past, critical scholars of educational technology are skeptical (e.g., Williamson, Macgilchrist, Potter, 2023; Selwyn, 2024). Scholars have cited the influence of social contexts in impeding educational technology’s translation from proposed benefits to actual practice, while such implementation may introduce unintended consequences and back-fire effects (Tawfik, Reeves, & Stichcite, 2016). Certain features and affordances in educational technology employing AI may emerge to provide certain benefits in specific learning contexts, designs or use cases and for some students. But given a growing corpus of critical education technology studies literature pertaining to AI’s evident risks to teaching, learning and the political economy of educational provision, this technology must be handled with care. The rush to integrate AI into existing formal and informal learning platforms and educational contexts is already gathering pace, without rigorous monitoring, regulation, or governance.
Meanwhile, in many countries, including the UK, USA, Canada, and Australia, higher education and universities are under political and economic pressure. The state is receding from higher education, outsourcing it to a variety of suppliers, and reducing its funding, accountability and oversight (Sarpong & Adelekan, 2023). Public schools are similarly being incrementally privatised and governed more like businesses competing for customers with corporate cultures and management structures (Gutiérrez & Exley, 2025). Commercial and profit driven tech infrastructures and learning platforms are being implemented as default instructional delivery platforms in ways that are compulsory for both instructors and students as a condition of employment and enrollment (Paris, Reynolds, McGowan, 2022). AI is readily being integrated into commercial education technology systems, in ways that often appear to users as unsolicited and without users’ opt-in choice or facilitation of agency (Berendt, Littlejohn, & Blakemore, 2020). LLMs are also becoming de facto and default information search systems for people and learners across the globe, replacing (and augmenting) search engines like Google, as well as library databases hosted at academic institutions and public libraries – for better or worse, from a standpoint of quality in information output (Haque & Li, 2025).
Such rapid proliferation, as well as adjacent marketing and PR on the part of companies making these inroads, and its pickup by the media, are contributing to hearsay and hype about what AI can achieve (Nemorin, Vlachidis, Ayerakwa, & Andriotis, 2022).
Topics of Interest
For this special issue, we invite papers that critically examine the use of AI in EdTech. We invite works that engage this scholarly inquiry through conceptual, theoretical, methodological, empirical research, ethics, and governance and policy lenses.
The following is a selection of exemplars for the types of research questions and topics of interest that are invited:
What research and scholarly paradigms, methods and theories are well-suited to understanding the proliferation of AI in education contexts?
What are the most relevant and impactful ethics theories and approaches relevant to AI in education?
Is AI becoming an instrument for democratizing, and/or, privatising public education? How do such perspectives juxtapose or coincide or contradict each other?
In what ways is AI refusal surfacing in contexts, regions, sectors as a response to AI proliferation?
What are some of the benefits and drawbacks of using AI in EdTech for teaching and learning?
How can research on AI education integration, and teaching and learning processes and outcomes be conducted rigorously, as it pertains to their concurrent risks?
Is the ability of companies to exploit AI in educational technology exacerbating educational inequality? How might this relate to the digital divide, especially as it pertains to whether and how the technology may augment or impede meaningful learning?
How do the environmental costs of AI pose barriers to its use in public education at mass scales?
In what ways can AI undermine trust and social relations within education?
Does AI enable the outsourcing of effortful and mindful learning activities to machines?
Does AI inhibit knowledge acquisition and subsequent skills development and application?
How should we (re)conceptualize and operationalize academic integrity and plagiarism in the AI era?
What will societies lose if they outsource education to AI?
Are concerns about student cheating with AI a rational response to the system proliferation and if not, how should it be otherwise conceived of? If so, how should such a response be incorporated into instructional paradigms?
Submission Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available here:
Author guidelines must be strictly followed. Please see:
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Key Deadlines
Submissions open: 15 October 2025
Submissions close: 15 February 2026
References
Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: learner choice and fundamental rights. Learning, Media and Technology, 45(3), 312–324. https://doi.org/10.1080/17439884.2020.1786399
Gutiérrez, G., & Exley, S. (2025). Beyond narrow definitions: Quantifying school privatisation across countries and over time. European Educational Research Journal, 24(4), 508-529.
Haque, M. A., & Li, S. (2025). Exploring ChatGPT and its impact on society. AI and Ethics, 5(2), 791-803.
Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2022). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38–51. https://doi.org/10.1080/17439884.2022.2095568
Paris, B., Reynolds, R., & McGowan, C. (2022). Sins of omission: Critical informatics perspectives on privacy in e‐learning systems in higher education. Journal of the Association for Information Science and Technology, 73(5), 708-725.
Sarpong, J., & Adelekan, T. (2023). Globalisation and education equity: The impact of neoliberalism on universities’ mission. Policy Futures in Education, 22(6), 1114-1129. https://doi.org/10.1177/14782103231184657
Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk Tidsskrift for Pedagogikk og Kritikk, 10(1), 3-14. https://doi.org/10.23865/ntpk.v10.6062
Tawfik, A.A., Reeves, T.D. & Stich, A. Intended and Unintended Consequences of Educational Technology on Social Inequality. TechTrends 60, 598–605 (2016). https://doi.org/10.1007/s11528-016-0109-5
Williamson, B., Macgilchrist, F., & Potter, J. (2023). Re-examining AI, automation and datafication in education. Learning, media and technology, 48(1), 1-5.
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