  In the realm of intelligent education, cognitive diagnosis plays a crucial
role in subsequent recommendation tasks attributed to the revealed students'
proficiency in knowledge concepts. Although neural network-based neural
cognitive diagnosis models (CDMs) have exhibited significantly better
performance than traditional models, neural cognitive diagnosis is criticized
for the poor model interpretability due to the multi-layer perception (MLP)
employed, even with the monotonicity assumption. Therefore, this paper proposes
to empower the interpretability of neural cognitive diagnosis models through
efficient kolmogorov-arnold networks (KANs), named KAN2CD, where KANs are
designed to enhance interpretability in two manners. Specifically, in the first
manner, KANs are directly used to replace the used MLPs in existing neural
CDMs; while in the second manner, the student embedding, exercise embedding,
and concept embedding are directly processed by several KANs, and then their
outputs are further combined and learned in a unified KAN to get final
predictions. To overcome the problem of training KANs slowly, we modify the
implementation of original KANs to accelerate the training. Experiments on four
real-world datasets show that the proposed KA2NCD exhibits better performance
than traditional CDMs, and the proposed KA2NCD still has a bit of performance
leading even over the existing neural CDMs. More importantly, the learned
structures of KANs enable the proposed KA2NCD to hold as good interpretability
as traditional CDMs, which is superior to existing neural CDMs. Besides, the
training cost of the proposed KA2NCD is competitive to existing models.
