Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Exercise Recommendation

TL;DR

KUL-Rec is a biologically inspired adaptive exercise recommendation system that uses Hebbian memory and replay mechanisms to personalize learning experiences efficiently across various data types and classroom settings.

Contribution

The paper introduces KUL-Rec, a novel ER system combining Hebbian memory with replay-based consolidation for continual, few-shot personalization in educational contexts.

Findings

Improves ranking metrics (nDCG, Recall@K) over baselines.

Reduces GPU memory usage by approximately 99%.

Enhances student engagement and perceived helpfulness in real courses.

Abstract

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.