Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Knowledge Tracing

• URL: http://arxiv.org/abs/2507.00032v1
• Date: Wed, 18 Jun 2025 00:06:28 GMT
• Title: Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Knowledge Tracing
• Abstract summary: KUL-KT is a biologically inspired architecture for knowledge tracing (KT)<n>It combines Hebbian memory encoding with gradient-based consolidation in a scalable, input-agnostic framework.<n>We introduce two key innovations: (i) a time-decaying Hebbian memory update that enables graceful forgetting, and (ii) a novel Loss-aligned Internal Target (LIT) method to compute an ideal internal state.
• Score: 11.25951282067287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce KUL-KT, a biologically inspired architecture for knowledge tracing (KT), combining Hebbian memory encoding with gradient-based consolidation in a scalable, input-agnostic framework. KUL-KT adapts the principle of memory consolidation in neural systems, to student modeling by introducing two key innovations: (i) a time-decaying Hebbian memory update that enables graceful forgetting, and (ii) a novel Loss-aligned Internal Target (LIT) method to compute an ideal internal state, allowing continual learning without backpropagation through time. The architecture consists of a fast Hebbian memory that captures each learner interaction via a single associative update, and a slower linear network that consolidates recalled samples through gradient descent. This design enables few-shot personalization and natural forgetting without storing raw data or relying on large cohort training. Operating entirely in embedding space, KUL-KT supports both structured (tabular) and unstructured (short-answer) inputs. Empirically, KUL-KT outperforms strong baselines on ten public KT benchmarks in rank-sensitive metrics such as nDCG and Recall@10. In a classroom deployment, KUL-KT personalized quizzes from short-answer data, leading to improved learner-perceived helpfulness and reduced difficulty (p < 0.05). Ablation studies confirm that Hebbian decay and LIT are critical for continual adaptation. Compared to a strong graph-based KT model, KUL-KT trains 1.75x faster and uses 99.01\% less memory. These results position KUL-KT as a biologically grounded, memory-efficient, and input-flexible framework for personalized learning at scale.

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