Graduate Student Research Focus: Qiling Wu, Ph.D. Candidate

What inspired you to pick your topic of research?

My current research focuses on health misinformation correction, AI-based correction tools, perceived interactivity and social norms. I am currently developing an experimental study on how people respond to AI-based corrections of raw milk misinformation. I do not yet have a published work directly related to this project, as it is still in development.

My research is motivated by a question that feels increasingly urgent: when people encounter health misinformation online, what kinds of correction actually help them reconsider what they believe, trust and share? I became interested in this topic because misinformation is not only a problem of false claims; it is also a problem of social context, platform design and trust. Health claims about topics such as raw milk, vaccines and nutrition often circulate through communities where people receive social cues about what “people like them” believe or approve of. At the same time, AI tools are becoming more visible in people’s everyday information environments, which raises an important question: can AI-based correction be designed in a way that is not only accurate, but also credible, responsive and socially meaningful?

What contribution do you hope your research makes to the field?

I hope my research contributes to the field by helping scholars better understand how emerging AI-based communication tools may shape people’s responses to health misinformation. Much of the existing correction literature has examined human fact-checkers, expert sources or platform labels, but AI correction introduces new theoretical and practical questions. An AI chatbot can feel interactive, responsive and conversational, yet people may also question its motives, credibility or reliability. My work aims to examine these tensions rather than assuming that AI correction is automatically helpful or harmful.

The project also contributes by bringing together several areas of communication research: misinformation correction, perceived interactivity, social norms, and source credibility. By testing how interactivity and different types of norm cues affect responses to raw milk misinformation, I hope to identify when AI-based correction is more persuasive, more trusted, or more likely to reduce intentions to share misinformation. More broadly, I hope this research can inform the design of correction strategies that are both theoretically grounded and practically useful. As AI tools become more integrated into search engines, social platforms and health information environments, communication scholars have an opportunity to shape how these tools are studied and evaluated. 

What support did you receive from the School to be able to do this research?

The School has played an important role in making this research possible. Financially, I received support through the Kriss Grant, which helped me move forward with an experimental study that requires participant recruitment and data collection. This kind of support is especially meaningful for an early-stage doctoral student because it allows me to develop a project from an initial idea into a feasible empirical study.

Beyond financial support, I have benefited greatly from the intellectual community in the School. As I developed this project, I received detailed feedback from faculty members on the research question, experimental design, stimuli, measurement and broader theoretical contribution. These conversations helped me sharpen the project. I am especially grateful for the guidance of my advisor, Dr. Emily Vraga, whose feedback has been both rigorous and generous. Her mentorship has helped me think more clearly about misinformation correction and about how to build a study that can eventually become a strong paper.

The School’s support has also given me space to learn how research ideas develop through conversation and revision. The feedback I received from faculty and cohorts did not simply improve one project; it helped me understand how to ask better questions, make stronger theoretical arguments, and design more careful communication research.