  Educational technologies, and the systems of schooling in which they are
deployed, enact particular ideologies about what is important to know and how
learners should learn. As artificial intelligence technologies -- in education
and beyond -- may contribute to inequitable outcomes for marginalized
communities, various approaches have been developed to evaluate and mitigate
the harmful impacts of AI. However, we argue in this paper that the dominant
paradigm of evaluating fairness on the basis of performance disparities in AI
models is inadequate for confronting the systemic inequities that educational
AI systems (re)produce. We draw on a lens of structural injustice informed by
critical theory and Black feminist scholarship to critically interrogate
several widely-studied and widely-adopted categories of educational AI and
explore how they are bound up in and reproduce historical legacies of
structural injustice and inequity, regardless of the parity of their models'
performance. We close with alternative visions for a more equitable future for
educational AI.
