  The development of educational AI (AIEd) systems has often been motivated by
their potential to promote educational equity and reduce achievement gaps
across different groups of learners -- for example, by scaling up the benefits
of one-on-one human tutoring to a broader audience, or by filling gaps in
existing educational services. Given these noble intentions, why might AIEd
systems have inequitable impacts in practice? In this chapter, we discuss four
lenses that can be used to examine how and why AIEd systems risk amplifying
existing inequities. Building from these lenses, we then outline possible paths
towards more equitable futures for AIEd, while highlighting debates surrounding
each proposal. In doing so, we hope to provoke new conversations around the
design of equitable AIEd, and to push ongoing conversations in the field
forward.
