About

Mike Yao is a Professor of Digital Media in the Charles H. Sandage Department of Advertising and serves as the Director of the Institute of Communications Research. He holds a Ph.D. in Communication from the University of California, Santa Barbara, and earned a B.A. in Psychology and Film Studies, also from UC Santa Barbara. His academic affiliations include courtesy appointments as a Professor of Business Administration in the Gies College of Business and as a Professor of Communication in the College of Liberal Arts and Sciences. His research focuses on the social and psychological impacts of interactive digital media. He conducts research and writes on topics such as online behavior, digital literacy, and computer-mediated communication. His current interests include how users perceive and manage personal boundaries on social media, examining attitudes, beliefs, and self-protective behaviors related to online privacy from a psychosocial perspective, considering cognitive appraisal, social norms, and individual differences. Additionally, he investigates the psychological impacts of digital media use, including the influence of interactive media like video games and social media on social behavior both online and offline. He is working on developing an integrated theory of digitally mediated human behavior. Mike Yao is actively involved in research related to media, technology, and social behavior, and his work is associated with the Media, Technology, and Social Behavior Lab. He is recognized for his contributions to understanding the social and psychological dimensions of digital media, including discussions on AI dependence and bias, and has presented at prominent conferences such as the International Communication Association.

Research topics

• Computer Science
• Psychology
• Political Science
• Business
• Internet privacy
• Oncology
• Multimedia
• Medicine
• Surgery
• Advertising
• World Wide Web
• Social psychology
• Internal medicine
• Human–computer interaction

Selected publications

• Should the AI Speak First? Evaluating Proactive vs. Reactive Facilitation in Mixed-Reality Medical Training

  2026-04-13

  articleOpen access

  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.

  PublisherDOI

• Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable Interfaces

  2026-04-13

  articleOpen accessSenior author

  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.

  PublisherOA PDFDOI

• Does Sequencing Matter? Evaluating AI and Human Simulations for High-Stakes Communication Training in Law Enforcement

  2026-04-13

  articleOpen access

  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.

  PublisherDOI

• SpatialTutor: Object-Aware Mixed Reality Training for Procedural Medical Skills Training with AI-Driven Support

  2026-01-26 · 1 citations

  article

  Procedural medical skills are difficult to teach consistently due to resource constraints, instructor variability, and limited feedback in traditional manikin-based training. We present SpatialTutor, a mixed reality training system that transforms manikin-based simulations into intelligent, interactive experiences by integrating spatially anchored 3D animations, real-time object and hand tracking, and a large language model (LLM)-powered conversational AI assistant. We conducted a formative study with experts (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{N}=8$</tex>) and an evaluation with experts (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=5$</tex>) and novice learners (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=10$</tex>). Results showed high usability, strong engagement with spatial guidance and feedback, and increased learner confidence. While the AI assistant was valued for offering low-pressure support, it was underutilized due to limited proactivity and social discomfort in shared settings. These findings highlight the need for more adaptive, context-aware AI in MR training.

  PublisherDOI

• Emotionally Vulnerable Subtype of Internet Gaming Disorder: Measuring and Exploring the Pathology of Problematic Generative AI Use

  ArXiv.org · 2025-10-08

  preprintOpen accessSenior author

  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.

  PublisherOA PDFDOI

• Not just a corridor: Hydrodynamic traps and fiber risk in the Kuroshio extension

  Water Research · 2025-10-10

  article
  PublisherDOI

• Revisiting Trust in the Era of Generative AI: Factorial Structure and Latent Profiles

  ArXiv.org · 2025-10-11

  preprintOpen accessSenior author

  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 &amp; 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.

  PublisherOA PDFDOI

• "Pragmatic Tools or Empowering Friends?" Discovering and Co-Designing Personality-Aligned AI Writing Companions

  ArXiv.org · 2025-09-14

  preprintOpen access

  The growing popularity of AI writing assistants presents exciting opportunities to craft tools that cater to diverse user needs. This study explores how personality shapes preferences for AI writing companions and how personalized designs can enhance human-AI teaming. In an exploratory co-design workshop, we worked with 24 writers with different profiles to surface ideas and map the design space for personality-aligned AI writing companions, focusing on functionality, interaction dynamics, and visual representations. Building on these insights, we developed two contrasting prototypes tailored to distinct writer profiles and engaged 8 participants with them as provocations to spark reflection and feedback. The results revealed strong connections between writer profiles and feature preferences, providing proof-of-concept for personality-driven divergence in AI writing support. This research highlights the critical role of team match in human-AI collaboration and underscores the importance of aligning AI systems with individual cognitive needs to improve user engagement and collaboration productivity.

  PublisherOA PDFDOI

• The Routine: Enhancing Special Needs Education Through AI-Personalized Embodied Learning in Virtual Environments

  2025-01-27 · 2 citations

  articleSenior author

  “The Routine” is a virtual learning environment (VLE) designed for children with special needs, primarily those with autism. By integrating embodied learning theory, which combines physical movement with cognitive development it aims to teach life skills and promote independence. Unlike traditional approaches, “The Routine” employs artificial intelligence to personalize learning experiences and dynamically adjust to each learner's needs. Through controlled play, intelligent educational agents enhance behavioral, social, and cognitive abilities in real-time. The system incorporates wearable electronics and sensors to track each child's physical and emotional states, adapting accordingly to provide a tailored experience. Future developments will focus on improving spatial learning through the integration of virtual and mixed-reality technologies, as well as enhancing prototypes with adjustable hardware to increase accessibility. As an ongoing project in educational technology, “The Routine” emphasizes adaptive, interactive, and multimodal learning methodologies, underscoring the revolutionary potential of VLEs in special education.

  PublisherDOI

• The impact of the brand's green marketing strategy on consumers' environmental awareness

  Journal of fintech and business analysis. · 2025-08-19

  articleOpen access1st authorCorresponding

  Against the backdrop of increasingly severe global environmental issues and the rise of green consumption trends, this study focuses on the effect of brand green marketing strategies on enhancing consumers' environmental awareness. Based on Planned Behavior and Consumer Behavior Theory, this study adopts a quantitative research approach to develope a theoretical model with hypotheses to assess the impact of green product, promotion, and channel strategies on environmental awareness. The results show that all three green marketing strategiesgreen product, green promotion, and green channel strategies have significant positive effects on consumers' environmental awareness. Among them, green channel strategies exhibit the strongest influence (= 0.407,p&lt; 0.01), followed by green promotion strategies (= 0.193,p= 0.081,p&lt; 0.05), collectively explaining 34.9% of the variation in consumers' environmental awareness. The study provides empirical support for enterprises to optimize their green marketing mix, effectively enhancing consumers' environmental awareness and promoting the development of the green consumption market.

  PublisherOA PDFDOI

Frequent coauthors

• Mateusz Gola

  Institute of Psychology

  28 shared

• Yanyun Wang

  18 shared

• Qingyang Tang

  Fudan University

  15 shared

• David J. Terris

  Augusta University

  15 shared

• Mithat Durak

  Bolu Abant İzzet Baysal University

  14 shared

• Ziwei Liu

  14 shared

• Monika McNeill

  Glasgow Caledonian University

  14 shared

• Amir H. Pakpour

  Qazvin University of Medical Sciences

  14 shared

Education

• Ph.D., Communication

  University of California, Santa Barbara

• B.A., Psychology & Film Studies

  University of California, Santa Barbara