Proposals are invited for papers for a special issue of the journal on the theme of AI and Education: critical perspectives and alternative futures. The special issue will be edited by Ben Williamson (University of Stirling, UK) and Rebecca Eynon (University of Oxford, UK).
Autonomous cars, personal digital assistants, the eradication of disease and poverty, robot armies, deskilling, mass unemployment and perhaps even the end of humanity itself are becoming increasingly familiar topics in public discourse. There has been cyclical interest in the societal implications of Artificial Intelligence (AI) since the 1960s, and once again, AI is making a very strong comeback. Indeed, this time, it is proposed that the proliferation and availability of big data, alongside advances in computational techniques and neuroscience knowledge means that much of the hype and hope of past interest cycles are coming partially into being and thus deserve academic attention (Russell et al, 2015; Yuste et al, 2017). Yet, the resurgence of interest and excitement around the role that AI could play in society also reflects a complex array of economic, cultural, social and political factors that need to be explored and accounted for alongside technical change (Johnson and Verdicchio 2017). AI is a highly diverse field of study (Russell and Norvig, 2009), and - like its predecessor ‘Big Data’ - AI is also a “myth and rhetorical move” (Crawford et al, 2014: 1665).
In the academic sphere, the vast majority of AI developments are taking place within the fields of computer science, neuroscience and philosophy. Nuanced and critical debate from the social sciences have also emerged, with attention drawn to the ethical and social implications of AI for a number of different spheres in society from health to political engagement (Crawford and Calo, 2016; Cath et al, 2017). However, few of these recent efforts are based within the field of Education. Indeed the role of AI in Education is a topic of popular debate, but is being informed by experts in computer science, engineering and related technical fields; commercial companies and others are seeking a new ‘technical fix’ for Education (Robins and Webster, 1989). This instrumental focus can also be seen in the advances being made in AI and learning that are primarily acquisition based (Sfard, 1998), prioritising the support of individual cognition based on insights from neuroscience and cognitive science, which are potentially reducing and narrowing the experience of learning and the purpose of education (Eynon and Salveson, forthcoming).
What is much needed is a ‘responsible response’ (Biesta, 2013) by academics and practitioners engaged in issues of Education to explore the role of AI in Education now and in the future, employing a more critical approach that takes account of the wider social, economic, cultural and political contexts. In this special issue, we seek to achieve this goal and would welcome papers taking a varied approach to this topic encompassing a wide range of theoretical and empirical approaches. Examples of topics include, but are not limited to, the following:
- ‘On the ground’ realities of the use of AI in Education
- Historical analyses of the philosophical, ethical and social debates about AI and Education
- AI, Education and issues of privacy and surveillance
- AI and alternative Educational futures
- The political economy of AI and Education
- AI infrastructures within Education
- AI, Education and equity
- Ethics of emerging AI applications in Education
- The scientific and technical basis of AI in Education
- Discourses surrounding AI in Education
- Implications of AI for educational policymaking and governance
We are currently soliciting abstracts for proposed papers for the special issue. Abstracts should be no longer than 300 words and be accompanied by up to six keywords.
- Deadline for submission of abstract: 15th July 2018
- Successful authors informed: 1st September 2018
- Deadline for submission of full papers: 1st March 2019
Full papers are expected to be between 6,000 and 8,000 words (please refer to the journal website for full ‘instructions for authors’). All papers will be subject to the usual blind reviewing and refereeing processes.
Please send abstracts and keywords to the guest editors by 15th July 2018. Email: email@example.com & firstname.lastname@example.org. Please put ‘abstract Learning, Media and Technology’ in the subject.
Biesta, G. (2013). Responsive or responsible? Democratic education for the global networked society. Policy Futures in Education, 11(6), 733-744.
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M. and Floridi, L. (2017) Artificial Intelligence and the 'Good Society': the US, EU, and UK approach. Science and engineering ethics. https://doi.org/10.1007/s11948-017-9901-7
Crawford, K.; Miltner, K. and Gray, M.L. (2014). Critiquing Big Data: Politics, Ethics, Epistemology. International Journal of Communication 8, 1663–72.
Crawford, K. and Calo, R. (2016) There is a blind spot in AI research. Nature, 538(7625): 311-313.
Johnson, D. and Verdicchio, M. (2017). Reframing AI Discourse. Minds and Society. https://doi.org/10.1007/s11023-017-9417-6
Robins, K., & Webster, F. (1989). The technical fix: Education, computers and industry. London: Macmillan
Russell, S. and Norvig, P. (2009). Artificial intelligence: A modern approach. Prentice hall
Russell, S., Dewey, D. and Tegmark, M., 2015. Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105-114.
Sfard, A. (1998). On two metaphors for learning and the dangers of choosing just one. Educational Researcher, 27(2), 4-13.
Yuste, R. et al (2017). Four ethical priorities for neurotechnologies and AI. Nature, 551(7679), 159-163.