Applying the keyword method for vocabulary memorization remains a significant challenge for L1 Chinese–L2 English learners. They frequently struggle to generate phonologically appropriate keywords, construct coherent associations, and create vivid mental imagery to aid long-term retention. Existing approaches, including fully automated keyword generation and outcome-oriented mnemonic aids, either compromise learner engagement or lack adequate process-oriented guidance. To address these limitations, we conducted a formative study with L1 Chinese-L2 English learners and educators (N=18), which revealed key difficulties and requirements in applying the keyword method to vocabulary learning. Building on these insights, we introduce WordCraft, a learner-centered interactive tool powered by Multimodal Large Language Models (MLLMs). WordCraft scaffolds the keyword method by guiding learners through keyword selection, association construction, and image formation, thereby enhancing the effectiveness of vocabulary memorization. Two user studies demonstrate that WordCraft not only preserves the generation effect but also achieves high levels of effectiveness and usability.

Automated control of personalized multiple anesthetics in clinical Total Intravenous Anesthesia (TIVA) is crucial yet challenging. Current systems, including target-controlled infusion (TCI) and closed-loop systems, either rely on relatively static pharmacokinetic/pharmacodynamic (PK/PD) models or focus on single anesthetic control. So they limit both personalization and collaborative control. To address these issues, we propose a novel Value Decomposition Multi-Agent Deep Reinforcement Learning (VD-MADRL) framework based on Markov Game (MG) for Personalized Multiple Anesthetics Control in a Closed-Loop system (PMAC-CL). VD-MADRL optimizes the collaboration between two anesthetics propofol (Agent I) and remifentanil (Agent II) by leveraging a MG to identify optimal actions among heterogeneous agents. We employ various value function decomposition methods to resolve the credit allocation problem and enhance collaborative control. We also introduce a multivariate environment model based on random forest (RF) for anesthesia state simulation. To ensure data validity, we design a data resampling and alignment technique to synchronize trajectory data from different devices, avoiding gradient explosion and maintaining conformity to Markov property. Extensive experiments on general and thoracic surgery datasets demonstrate that VD-MADRL provides more refined dose adjustments and maintains multiple anesthesia state indicators more stably at target levels compared to human experience. Especially, the best-performing algorithm, VDN in general surgery with online training, achieved a 16.4% increase in cumulative reward (CR) and a 58.0% reduction in mean MDPE compared to human experience. This demonstrates its great clinical value.