AF-KT: An Alternate Autoregressive and Forgetting-Aware Transformer for Knowledge Tracing in Adaptive Examination Preparation

This research proposes AF-KT, a transformer-based knowledge tracing model that explicitly models human memory. Its key contributions are a forgetting-aware attention mechanism with learnable per-head time-decay rates, a Bayesian mastery gate that tracks per-concept knowledge estimates, and a calibrated alternate-sequence architecture. On the EdNet-KT1 benchmark (5,000 students), AF-KT achieves an AUC of 0.8608 and accuracy of 0.8054, outperforming recent state-of-the-art baselines and providing a practical foundation for adaptive examination preparation systems.