Effective patient-provider communication is often hindered by disparities in medical knowledge and the use of technical jargon. Although analogies and metaphors can help bridge this gap, physicians struggle to generate them under clinical time pressure, highlighting a need for supportive design. Through formative interviews with patients and physicians, we identified requirements for explanatory tools that are clear, accurate, and context-sensitive. In response, we designed MediMate, an interactive system that allows physicians to rehearse and iteratively refine patient-friendly explanations using LLM-generated analogies in a low-stakes setting. The interface of MediMate is designed to scaffold the creative process, helping physicians balance clarity with medical accuracy. In a user study involving both physicians and patients, we found that explanations developed with MediMate enhanced communication efficiency by providing such scaffolding. Physicians reported increased self-efficacy and perceived value in using the system as a practice tool for developing their communication skills. Our work demonstrates how interactive AI-powered tools can support clinical communication rehearsal and offers insights for the design of future clinical decision-support and educational tools.

Zero-Shot Anomaly Detection (ZSAD) aims to identify and localize anomalous regions in images of unseen object classes. While recent methods based on vision-language models like CLIP show promise, their performance is constrained by existing prompt engineering strategies. Current approaches, whether relying on single fixed, learnable, or dense dynamic prompts, suffer from a representational bottleneck and are prone to overfitting on auxiliary data, failing to generalize to the complexity and diversity of unseen anomalies. To overcome these limitations, we propose PromptMoE. Our core insight is that robust ZSAD requires a compositional approach to prompt learning. Instead of learning monolithic prompts, PromptMoE learns a pool of expert prompts, which serve as a basis set of composable semantic primitives, and a visually-guided Mixture-of-Experts (MoE) mechanism to dynamically combine them for each instance. Our framework materializes this concept through a Visually-Guided Mixture of Prompt (VGMoP) that employs an image-gated sparse MoE to aggregate diverse normal and abnormal expert state prompts, generating semantically rich textual representations with strong generalization. Extensive experiments across 15 datasets in industrial and medical domains demonstrate the effectiveness and state-of-the-art performance of PromptMoE.

Online comments significantly influence users’ judgments, yet their presentation, often determined by platform algorithms, can introduce biases, such as anchoring effects, which distort reasoning. While existing research emphasizes mitigating individual cognitive biases, the evolution of user judgments during comment engagement remains overlooked. This study investigates how presentation cues impact reasoning and explores interface design strategies to mitigate bias. Through a preliminary experiment (N=18) and a co-design workshop, we identified key challenges users face across a four-stage process and distilled four design requirements: pre-engagement framing, interactive organization, reflective prompts, and synthesis support. Based on these insights, we developed CommSense, an on-the-fly plugin that enhances user engagement with online comments by providing visual overviews and lightweight prompts to guide reasoning. A between-subject evaluation (N=24) demonstrates that CommSense improves bias awareness and reflective thinking, helping users produce more comprehensive, evidence-based rationales while maintaining high usability.

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.

Effective collaboration between designers and users is important for fashion design, which can increase the user acceptance of fashion products and thereby create value. However, it remains an enduring challenge, as traditional designer-centric approaches restrict meaningful user participation, while user-driven methods demand design proficiency, often marginalizing professional creative judgment. Current co-design practices, including workshops and AI-assisted frameworks, struggle with low user engagement, inefficient preference collection, and difficulties in balancing user feedback with design considerations. To address these challenges, we conducted a formative study with designers and users experienced in co-design (N=7), identifying critical challenges for current collaboration between designers and users in the co-design process, and their requirements. Informed by these insights, we introduce DesignBridge, a multi-platform AI-enhanced interactive system that bridges designer expertise and user preferences through three stages: (1) Initial Design Framing, where designers define initial concepts. (2) Preference Expression Collection, where users intuitively articulate preferences via interactive tools. (3) Preference-Integrated Design, where designers use AI-assisted analytics to integrate feedback into cohesive designs. A user study demonstrates that DesignBridge significantly enhances user preference collection and analysis, enabling designers to integrate diverse preferences with professional expertise.

Image anomaly detection identifies images that deviate from normal patterns. Knowledge Distillation has been extensively studied in unsupervised anomaly detection, where it is assumed that the student model learns and reconstructs the multi-scale normal representations of the pre-trained teacher model, with the representation discrepancies between the student and teacher identified as anomalies. However, the over-generalization of the student model causes it to reconstruct anomalies, leading to the failure of anomaly detection. To address this issue, we propose a novel Diffusion Distillation method for Anomaly Detection (D2AD), which utilizes a lightweight diffusion pipeline to prevent student from imitating abnormal behavior. Specifically, we use the diffusion process to confuse the abnormal student representation with noisy normal representations and restore them to normal, effectively avoiding the over-generalization issue and enabling the student to efficiently transfer the teacher’s refined knowledge of normality. Moreover, we propose the Anomaly-Guided Diffusion Process (AGDP), which dynamically adjusts the noise intensity based on the likelihood of anomalies to balance anomaly confusion and representation recovery. Extensive experiments on the MVTec AD, VisA, and Real-IAD datasets show significant improvements in anomaly detection and localization, while maintaining high efficiency.