Category: Research and Creative Endeavor

Mark Burbridge’s primary research areas center around film criticism, media studies, and digital media studies, with specific research focus on the evolution of film criticism, histories of film industry and media markets, and how emerging technologies affect popular film discourse.

Boram Cho’s research interests include AI creativity, human-AI interaction and collaboration, persuasion knowledge, emerging technologies and media in children and older adults, and ethical AI use.

Matthew Huh’s research focuses on cultural and audience shifts found in contemporary pop culture, animation, and nostalgia, along with the impact of blockbuster media in film culture.

Jiayi (Joy) Wang’s research focuses on artificial intelligence in advertising and consumer behavior, with particular interest in how AI-generated advertising and persuasion knowledge shape consumer responses across digital platforms.

Chenqi Zhu’s research interests lie in the intersection of media and communication study, history of science, and science technology studies. She is interested in how communicative, scientific, and technological practices condition and transform each other across historical trajectories, and to their constitutive role in structuring human nature and broader social formations.

Describe your areas of research interest.

I’m especially interested in three questions. First, how do people make sense of AI in the first place—what do they believe a machine can genuinely experience, claim, do, or create? Second, what changes when AI moves from simply providing information to recommending, interpreting, and eventually acting on a consumer’s behalf? And third, what happens when AI becomes emotional—when it recognizes how we feel, expresses emotions of its own, or begins to function more like a companion than a tool?

How do you approach your research?

I study these questions in advertising and consumer decision-making across contexts such as virtual influencers, generative AI advertising, conversational AI, social robots, AI shopping agents, and emotionally responsive AI. I also try to study these technologies in settings that resemble how people actually encounter them. Rather than relying only on written scenarios, many of my studies use live AI conversations, interactive advertising interfaces, and simulated shopping environments where participants actually interact with or delegate tasks to an AI. That allows me to study not just what people say they would do with AI, but how they respond when the technology is actively participating in the decision.

Why does your research matter?

Even if you never think of yourself as an “AI person,” AI is increasingly involved in ordinary decisions around you. It can influence which products you see, explain why an advertised option supposedly fits you, recommend what to buy, create the advertising itself, or even complete parts of a purchase on your behalf. At the same time, companies are designing AI to sound warmer, more understanding, and more human.

Each of those changes raises a different question that matters beyond AI itself. When AI creates or endorses something, we have to decide what kinds of claims we consider credible coming from a machine. When AI recommends something, we have to decide how much weight to give its judgment. When it acts for us, we have to decide how much control we are willing to hand over. And when it becomes emotionally responsive, we have to decide whether we are still interacting with a tool or beginning to treat it as a social partner.

Those are ultimately questions about persuasion, autonomy, responsibility, and relationships. They matter because AI is increasingly being inserted into moments where consumers are vulnerable to influence—when they are uncertain about what to choose, when they are delegating a consequential decision, or even when they are looking for emotional support. Understanding when these systems help people and when they become uncomfortable, misleading, or overly influential can inform not only advertising practice, but also how companies design AI and how consumers are protected as these technologies become more deeply embedded in everyday life.

How did ICR help you develop and hone in on your research area?

ICR gave me the freedom to follow a research question across disciplinary boundaries. My work is grounded in advertising and consumer psychology, but it also draws from communication, social psychology, human–computer interaction, and research on emotion and emerging technology. Having faculty with expertise spanning AI, computational approaches, and emotional and physiological processes made it possible for me to think about consumer–AI interaction from more than one perspective.

ICR also broadened my methodological training. In addition to experimental and survey methods, coursework in analytics and computational research introduced me to R-based data analysis (a free, open-source software environment designed for statistical computing and graphics) and encouraged me to think more broadly about the kinds of data and methods I can use. That combination of theoretical breadth and methodological flexibility helped me move from studying individual AI phenomena to developing a more connected research program.

Just as importantly, ICR taught me to keep asking, “What is the theoretical question underneath the new technology?” AI changes incredibly quickly. A platform or feature that feels novel today may look completely different a few years from now, so I learned not to build my research around a particular technology. Instead, I focus on the more enduring human questions underneath it—how people infer minds and intentions, respond to persuasion, give up control, assign responsibility, and form relationships.

What led you to be interested in your research area?

I became interested in AI because I kept noticing that the most interesting part was not whether the technology worked—it was how quickly people began treating it as something more than technology.

A conversational AI can produce a technically correct answer, but people still ask whether it really understands them. A virtual influencer can present a product perfectly, but consumers may still wonder whether it could actually have experienced what it is describing. And an AI can make a very good recommendation, yet that recommendation feels different once the system moves from saying, “Here is what I think you should buy,” to saying, “I can buy it for you.” That shift toward agentic AI, which can search, choose, and execute decisions on the consumer’s behalf, made me especially interested in what happens when people stop simply taking advice from AI and begin giving it authority.

What other aspects of your research do you find interesting?

AI systems are increasingly designed not only to be capable, but to be emotionally responsive—to recognize distress, offer reassurance, express emotion, and maintain ongoing conversations. That creates a very different set of questions. What does it mean when a machine says, “I understand,” or even, “I feel the same way”? When does that make the interaction feel supportive, and when does it feel artificial or manipulative? And what happens when repeated emotional interaction develops into something that consumers experience as a relationship?

Those moments are what continue to draw me to this area. As AI moves from communicating with consumers to influencing their decisions, to acting for them, and even relating to them, it keeps entering roles that were once occupied by people. I want to understand how consumers decide what role a machine is allowed to play in each of those roles—and what happens when it crosses the line.