Weizi Liu

Mengke Wu, Kexin Quan, Weizi Liu et al.

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.

Mengke Wu, Weizi Liu, Yanyun Wang et al.

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.

Weizi Liu

With the rise of human-machine communication, machines are increasingly designed with humanlike characteristics, such as gender, which can inadvertently trigger cognitive biases. Many conversational agents (CAs), such as voice assistants and chatbots, default to female personas, leading to concerns about perpetuating gender stereotypes and inequality. Critiques have emerged regarding the potential objectification of females and reinforcement of gender stereotypes by these technologies. This research, situated in conversational AI design, aims to delve deeper into the impacts of gender biases in human-CA interactions. From a behavioral and communication research standpoint, this program focuses not only on perceptions but also the linguistic styles of users when interacting with CAs, as previous research has rarely explored. It aims to understand how pre-existing gender biases might be triggered by CAs' gender designs. It further investigates how CAs' gender designs may reinforce gender biases and extend them to human-human communication. The findings aim to inform ethical design of conversational agents, addressing whether gender assignment in CAs is appropriate and how to promote gender equality in design.

Haocan Sun, Weizi Liu, Di Wu et al.

Trust is one of the most important factors shaping whether and how people adopt and rely on artificial intelligence (AI). Yet most existing studies measure trust in terms of functionality, focusing on whether a system is reliable, accurate, or easy to use, while giving less attention to the social and emotional dimensions that are increasingly relevant for today's generative AI (GenAI) systems. These systems do not just process information; they converse, respond, and collaborate with users, blurring the line between tool and partner. In this study, we introduce and validate the Human-AI Trust Scale (HAITS), a new measure designed to capture both the rational and relational aspects of trust in GenAI. Drawing on prior trust theories, qualitative interviews, and two waves of large-scale surveys in China and the United States, we used exploratory (n = 1,546) and confirmatory (n = 1,426) factor analyses to identify four key dimensions of trust: Affective Trust, Competence Trust, Benevolence & Integrity, and Perceived Risk. We then applied latent profile analysis to classify users into six distinct trust profiles, revealing meaningful differences in how affective-competence trust and trust-distrust frameworks coexist across individuals and cultures. Our findings offer a validated, culturally sensitive tool for measuring trust in GenAI and provide new insight into how trust evolves in human-AI interaction. By integrating instrumental and relational perspectives of trust, this work lays the foundation for more nuanced research and design of trustworthy AI systems.

Haocan Sun, Di Wu, Weizi Liu et al.

Concerns over the potential over-pathologization of generative AI (GenAI) use and the lack of conceptual clarity surrounding GenAI addiction call for empirical tools and theoretical refinement. This study developed and validated the PUGenAIS-9 (Problematic Use of Generative Artificial Intelligence Scale-9 items) and examined whether PUGenAIS reflects addiction-like patterns under the Internet Gaming Disorder (IGD) framework. Using samples from China and the United States (N = 1,508), we conducted confirmatory factor analysis and identified a robust 31-item structure across nine IGD-based dimensions. We then derived the PUGenAIS-9 by selecting the highest-loading items from each dimension and validated its structure in an independent sample (N = 1,426). Measurement invariance tests confirmed its stability across nationality and gender. Person-centered (latent profile analysis) and variable-centered (network analysis) approaches revealed a 5-10% prevalence rate, a symptom network structure similar to IGD, and predictive factors related to psychological distress and functional impairment. These findings indicate that PUGenAI shares features of the emotionally vulnerable subtype of IGD rather than the competence-based type. These results support using PUGenAIS-9 to identify problematic GenAI use and show the need to rethink digital addiction with an ICD (infrastructures, content, and device) model. This keeps addiction research responsive to new media while avoiding over-pathologizing.

Qingxiao Zheng, Yiliu Tang, Yiren Liu et al.

Early conversational agents (CAs) focused on dyadic human-AI interaction between humans and the CAs, followed by the increasing popularity of polyadic human-AI interaction, in which CAs are designed to mediate human-human interactions. CAs for polyadic interactions are unique because they encompass hybrid social interactions, i.e., human-CA, human-to-human, and human-to-group behaviors. However, research on polyadic CAs is scattered across different fields, making it challenging to identify, compare, and accumulate existing knowledge. To promote the future design of CA systems, we conducted a literature review of ACM publications and identified a set of works that conducted UX (user experience) research. We qualitatively synthesized the effects of polyadic CAs into four aspects of human-human interactions, i.e., communication, engagement, connection, and relationship maintenance. Through a mixed-method analysis of the selected polyadic and dyadic CA studies, we developed a suite of evaluation measurements on the effects. Our findings show that designing with social boundaries, such as privacy, disclosure, and identification, is crucial for ethical polyadic CAs. Future research should also advance usability testing methods and trust-building guidelines for conversational AI.