The growing popularity of AI writing assistants creates exciting opportunities to support diverse writers. This study examines how personality shapes expectations for AI writing companions and how personality-informed design can enhance human–AI teaming in writing. Through exploratory co-design workshops with 24 writers representing different personality profiles, we elicited values and design ideas for AI writing companions spanning functionality, interaction dynamics, and visual representation. These insights informed two contrasting prototypes reflecting distinct writing orientations, used as design provocations in review-and-refinement workshops with eight participants to prompt reflection on fit, priorities, and writing practices. Our findings reveal both shared foundational needs across writers and meaningful personality-driven preferences that influence how writers engage with AI. This work underscores the importance of team matching in human-AI collaboration and demonstrates how aligning AI companions with individual cognitive and interpersonal needs can improve engagement and perceived collaboration effectiveness.

Training professionals in high-stakes, trauma-informed communication is critical across domains such as law enforcement, healthcare, and counseling. While live role-play with trained actors remains the gold standard, it is resource-intensive and emotionally demanding. Generative AI offers scalable alternatives, but what is gained or lost when training shifts to AI? We developed an AI-powered sexual assault victim interview training system and conducted a mixed-methods study with 35 police recruits, each completing both an AI-based and a live, actor-based training session. By varying the sequence (AI-first vs. human-first), we examined differences in self-efficacy, perceptions of the AI system, and perceived learning experience. Although both modalities supported learning, the order in which they were experienced significantly shaped learners’ emotional engagement, sense of preparedness, and interpretation of each simulation’s role. Building on these insights, we introduce a conceptual design framework that identifies social–emotional, temporal, and embodied distance as key pedagogical dimensions, and we offer implications for sequencing hybrid simulations to scaffold preparation, performance, and reflection. Our findings position AI not as a replacement for human realism, but as a complementary modality that expands opportunities for safe, scalable practice in sensitive communication training.

As AI support tools become more common in immersive medical training, designers face a critical interaction-design question: When should an AI facilitator take initiative, and when should it wait for the learner? To investigate this design tension, we compared two versions of an AI facilitator in a mixed-reality (XR) lumbar puncture simulator training conditions: one in which the AI proactively initiated guidance and encouragement, and another in which the AI responded only when prompted. Using a mixed-methods approach, we examined how medical students (n=22) engaged with, interpreted, and reacted to these two facilitation styles. We found no significant differences in learning outcomes, interaction frequency, or overall experience ratings. However, interviews and behavioral analyses revealed nuanced differences in how learners perceived AI interventions across distinct task phases. AI-initiated support was seen as helpful in some moments and disruptive in others, depending on task phase, cognitive load, and personal preferences. Based on these findings, we contribute a boundary framework which offers actionable design guidance for calibrating AI proactivity in immersive training systems, and extends HCI research on proactive agents and human–AI collaboration within high-cognitive-load environments.

AI-driven recommender systems are often perceived as personalization black boxes, limiting users' ability to understand how their data shapes content (information asymmetry) or to influence system behavior meaningfully (power asymmetry). This study explores how design can strengthen user agency by integrating transparency with actionable control. We developed a provotype that introduces new interface features for managing data use, discovering varied content, and configuring context-based recommending modes. The walkthroughs and interviews with 19 participants show how these features help users interpret personalization signals, understand how their actions influence outcomes, address concerns from unwanted inference to narrow feeds (e.g., filter bubbles), and build trust in the system. We also identify strategies for promoting adoption and awareness of agency-enhancing features. Overall, our findings reaffirm users' desire for active influence over personalization and contribute concrete interface mechanisms with empirical insights for designing recommender systems that foreground user autonomy and fairness in AI-driven content delivery.

E-commerce platforms increasingly leverage product recommendation features that use interface cues—such as promotions, ratings, and scarcity indicators—to drive consumers' choice behavior. While prior research has examined the individual effects of these cues, their combined effects and relative importance remain under-explored. This study employs Choice-Based Conjoint analysis across three sequential studies to examine how various combinations of common interface cues impact product preferences. Findings reveal that, in general, cues signaling product popularity and special offers exert the strongest influence, followed by scarcity indicators. Specifically, high ratings with numerous reviews, extra price coupons, and limited-time deals were especially persuasive for both search and experience products, while product authenticity played an additional critical role for experience products. Our study can inform e-commerce design strategies, such as prioritizing impactful cues in product listings, to facilitate user decision-making and improve the usability of the product display interface.

Studies in the Proteus Effect have shown that users conform to stereotypes associated with their avatar's appearance. In this study, we used longitudinal behavioral data from 1,040 users in a virtual world to examine the behavioral outcome of conflicting gender cues between user and avatar. We found that virtual gender had a significant effect on in-game behaviors for both healing and player-vs-player activity.

Abstract With the rapid diffusion of the Internet, researchers, policy makers, and users have raised concerns about online privacy, although few studies have integrated aspects of usage with psychological and attitudinal aspects of privacy. This study develops a model involving gender, generalized self‐efficacy, psychological need for privacy, Internet use experience, Internet use fluency, and beliefs in privacy rights as potential influences on online privacy concerns. Survey responses from 413 college students were analyzed by bivariate correlations, hierarchical regression, and structural equation modeling. Regression results showed that beliefs in privacy rights and a psychological need for privacy were the main influences on online privacy concerns. The proposed structural model was not well supported by the data, but a revised model, linking self‐efficacy with psychological need for privacy and indicating indirect influences of Internet experience and fluency on online privacy concerns about privacy through beliefs in privacy rights, was supported by the data.