  We consider the equity and fairness of curricula derived from Knowledge
Tracing models. We begin by defining a unifying notion of an equitable tutoring
system as a system that achieves maximum possible knowledge in minimal time for
each student interacting with it. Realizing perfect equity requires tutoring
systems that can provide individualized curricula per student. In particular,
we investigate the design of equitable tutoring systems that derive their
curricula from Knowledge Tracing models. We first show that many existing
models, including classical Bayesian Knowledge Tracing (BKT) and Deep Knowledge
Tracing (DKT), and their derived curricula can fall short of achieving
equitable tutoring. To overcome this issue, we then propose a novel model,
Bayesian-Bayesian Knowledge Tracing (BBKT), that naturally enables online
individualization and, thereby, more equitable tutoring. We demonstrate that
curricula derived from our model are more effective and equitable than those
derived from classical BKT models. Furthermore, we highlight that improving
models with a focus on the fairness of next-step predictions might be
insufficient to develop equitable tutoring systems.
