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.

Most AI systems today are designed to manage tasks and execute predefined steps. This makes them effective for process coordination but limited in their ability to engage in joint problem-solving with humans or contribute new ideas. We introduce MultiColleagues, a multi-agent conversational system that shows how AI agents can act as colleagues by conversing with each other, sharing new ideas, and actively involving users in collaborative ideation processes. In a within-subjects study with 20 participants, we compared MultiColleagues to a single-agent baseline. Results show that MultiColleagues fostered stronger perceived social presence, and participants rated their outcomes as higher in quality and novelty, with more elaboration during ideation. These findings demonstrate the potential of AI agents to move beyond process partners toward colleagues that share intent, strengthen group dynamics, and collaborate with humans to advance ideas.

The integration of generative AI with Extended Reality (XR) technologies has unlocked unprecedented capabilities, empowering users with enhanced cognitive, sensory, and environmental control – effectively enabling "superpowers" in immersive digital spaces. This paper explores both the benefits and potential risks. We make two contributions: (i) a synthesized taxonomy of LLM-enabled XR superpowers and their associated risks, and (ii) a set of design guidelines and a forward research agenda derived from that synthesis. We conduct a multi-phase analysis of 135 recent advancements and studies in the field to examine the superpowers granted by these technologies, alongside their associated risks. We categorize the superpowers into internal (cognitive and sensory enhancements) and external (environmental and social manipulations), illustrating how they amplify human abilities in domains such as healthcare, education, and professional training. We then analyze the risks specific to each superpower, revealing critical vulnerabilities in user autonomy, data security, and ethical transparency. This research aims to guide stakeholders in harnessing the potential of XR while mitigating the socio-technical risks of this emerging landscape.

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.