1. KQN performs better than all the previous models.

2. KQN enables to query student knowledge state with respect to different skills.

3. KQN architecture leads to the concept of probabilistic skill similarity which is helpful for the domain modeling and clustering.

4. KQN is resistant towards changes in the variability of the dimensionality of the vector space for knowledge state and skill vectors.

KQN has a lot more parameters than neural networks and it is computationally heavier than the DKT. As it was mentioned in the paper, skill distances are "useful features for domain modeling to explore the latent structure of the skill domain, which can be a future direction of the KT research"