Fair When Sparse, Accurate When Rich: RL-Gated Conversational Fusion for Temporally Equitable Knowledge Tracing

Proposes an RL-gated conversational fusion model for knowledge tracing that maintains fairness when interaction data is sparse and improves accuracy when data is rich.