The recent hyperbole around artificial intelligence (AI) has impacted on our ability to properly consider the lasting educational implications of this technology. This paper outlines a number critical issues and concerns that need to feature more prominently in future educational discussions around AI. These include:

1. the limited ways in which educational processes and practices can be statistically modelled and calculated;
2. the ways in which AI technologies risk perpetuating social harms for minoritized students;
3. the losses incurred through reorganising education to be more ‘machine readable’; and
4. the ecological and environmental costs of data-intensive and deviceintensive forms of AI.

The paper concludes with a call for slowing down and recalibrating current discussions around AI and education – paying more attention to issues of power, resistance and the possibility of re-imagining education AI along more equitable and educationally beneficial lines.

This paper calls for more reasoned responses to the educational possibilities of AI. While educators should not completely ignore recent developments around machine learning, large-language models and the like, there is certainly a need to resist the more extreme hopes and fears that the idea of AI technology continues to provoke. At the same time, there is also a need to better engage with complex issues and concerns that have so far tended to remain sidelined in educational discussions around AI. This requires sustained, ongoing and open dialogue that brings in perspectives not usually given space in conversations around digital innovation and education futures. In particular, this requires paying closer attention to the experiences and standpoints of those groups likely to gain least (and likely to lose most) from the unfettered implementation of AI technology in education. As a start, this brief paper sets out some pertinent starting-points from which such discussions can progress in earnest.