We describe a modern deep learning system that automatically identifies informative contextual examples (\qu{contexts}) for first language vocabulary instruction for high school student. Our paper compares three modeling approaches: (i) an unsupervised similarity-based strategy using MPNet's uniformly contextualized embeddings, (ii) a supervised framework built on instruction-aware, fine-tuned Qwen3 embeddings with a nonlinear regression head and (iii) model (ii) plus handcrafted context features. We introduce a novel metric called the Retention Competency Curve to visualize trade-offs between the discarded proportion of good contexts and the \qu{good-to-bad} contexts ratio providing a compact, unified lens on model performance. Model (iii) delivers the most dramatic gains with performance of a good-to-bad ratio of 440 all while only throwing out 70\% of the good contexts. In summary, we demonstrate that a modern embedding model on neural network architecture, when guided by human supervision, results in a low-cost large supply of near-perfect contexts for teaching vocabulary for a variety of target words.

翻译：本文描述了一种现代深度学习系统，该系统能自动识别适用于高中生第一语言词汇教学的信息性情境示例（即“语境”）。我们比较了三种建模方法：（i）基于MPNet统一情境化嵌入的无监督相似性策略；（ii）建立在指令感知、微调Qwen3嵌入基础上的监督框架，并配备非线性回归头；（iii）模型（ii）与人工构建语境特征的结合。我们引入了一种称为“保持能力曲线”的新颖度量指标，用以可视化优质语境丢弃比例与“优劣语境比”之间的权衡关系，为模型性能提供了紧凑统一的评估视角。模型（iii）实现了最显著的性能提升，其优劣语境比达到440，同时仅舍弃70%的优质语境。总之，我们证明：在人工监督指导下，基于神经网络架构的现代嵌入模型能够以低成本大规模生成近乎完美的词汇教学语境，适用于多种目标词汇的教学场景。