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.

翻译：自适应习题推荐旨在选择与学习者动态发展的最近发展区相匹配的下一个学习活动。本文提出KUL-Rec——一种受生物学启发的习题推荐系统，该系统通过耦合快速赫布记忆与基于慢速回放的巩固机制，实现了在稀疏交互条件下的持续、少样本个性化推荐。该模型在嵌入空间中运行，使得单一架构能够同时处理表格化知识追踪日志和开放式简答题文本。我们采用双向排序和排序敏感指标（nDCG、Recall@K）使评估与辅导需求保持一致。在十个公开数据集上的实验表明，KUL-Rec在宏观nDCG（0.316对比最强基线0.265）和Recall@10（0.305对比0.211）指标上均取得提升，同时实现了较低的推理延迟，且相较于竞争性图模型峰值GPU内存降低约99%。在一门为期13周的研究生课程中，KUL-Rec对检索增强流程生成的每周简答题测验进行个性化推荐，个性化测验显示出更低的感知难度和更高的帮助性（p < 0.05）。嵌入鲁棒性审计表明编码器选择会影响语义对齐，这提示在部署开放式回答评估时需要常规审计机制。综合结果表明，具有有限巩固机制的赫布回放为跨数据模态和课堂场景的可扩展、实时可解释习题推荐提供了实用路径。