Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Exercise Recommendation
This addresses the problem of matching exercises to learners' evolving abilities in educational settings, offering a practical and scalable solution with incremental improvements over existing methods.
The paper tackled adaptive exercise recommendation by proposing KUL-Rec, a system using Hebbian memory and replay for continual personalization, which improved macro nDCG from 0.265 to 0.316 and Recall@10 from 0.211 to 0.305 across datasets, and in a course trial, personalized quizzes led to lower perceived difficulty and higher helpfulness.
Adaptive exercise recommendation (ER) aims to choose the next activity that matches a learner's evolving Zone of Proximal Development (ZPD). We present KUL-Rec, a biologically inspired ER system that couples a fast Hebbian memory with slow replay-based consolidation to enable continual, few-shot personalization from sparse interactions. The model operates in an embedding space, allowing a single architecture to handle both tabular knowledge-tracing logs and open-ended short-answer text. We align evaluation with tutoring needs using bidirectional ranking and rank-sensitive metrics (nDCG, Recall@K). Across ten public datasets, KUL-Rec improves macro nDCG (0.316 vs. 0.265 for the strongest baseline) and Recall@10 (0.305 vs. 0.211), while achieving low inference latency and an $\approx99$\% reduction in peak GPU memory relative to a competitive graph-based model. In a 13-week graduate course, KUL-Rec personalized weekly short-answer quizzes generated by a retrieval-augmented pipeline and the personalized quizzes were associated with lower perceived difficulty and higher helpfulness (p < .05). An embedding robustness audit highlights that encoder choice affects semantic alignment, motivating routine audits when deploying open-response assessment. Together, these results indicate that Hebbian replay with bounded consolidation offers a practical path to real-time, interpretable ER that scales across data modalities and classroom settings.