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Accepted Contribution:

Delivering the good(s) in education? Teachers, AI, and speculative ethics of care  
Martin Oliver (UCL Institute of Education) Jade Henry (University College London)

Long abstract:

The UK’s government recently committed £2m as “the first step towards providing every teacher with a personalised AI lesson-planning assistant”. Following a national consultation about AI’s educational risks, ethics and possibilities, this was selected as “a perfect example of the revolutionary benefits this technology can bring”. However, many teachers and parents disagreed.

Education, like healthcare, is often treated as inherently ethical (e.g. as integral to the Sustainable Development Goals and the Universal Declaration of Human Rights). Mainstream discussions position AI as a means to achieve these forms of “social good”, downplaying ethical risks as manageable technical issues (Boenig-Liptsin, 2022). Yet normative assumptions about technology’s benefits, and what may be championed as “social” or “good”, remain contestable (Arora & Sarkar, 2023). Education scholars claim that industry has influenced policy-making, favouring market economics and foreclosing debates about other societal values (Eynon & Young, 2021). This has led to critiques of the principle-based, deontological ethics pervading the field, and calls for the “(re)politicization of data-driven education” (Knox, 2023).

In this presentation, we will analyse ethico-political debates around this investment in AI, building on studies of educational technology and care (Prinsloo & Slade, 2017; Zakharova & Jarke, 2022; Henry & Oliver, 2022). We situate this analysis as a form of speculative ethics, putting theories of care into play with constructivist accounts in STS (Mol, 2008; Pol, 2015; Puig de la Bellacasa, 2017). We describe how speculative ethics enacts a “common ground” of research practices, connecting deontological ethics with relational and applied approaches in technoscience.

Combined Format Open Panel P164
STS & ethics: encounters on common ground
  Session 1