The AKT method consists of four components: two self-attentive encoders, one for questions and one for knowledge acquisition, a single attention-based knowledge retriever, and a feed-forward response prediction model.

The Rasch model-based embeddings are used as raw embeddings for questions and responses. The question and knowledge encoders compute the context-aware representations of questions and responses pairs. The knowledge retriever uses these representations as input and computes the knowledge state of the learner.