AI In Research Series

AI in Research Series

New this academic year, the AI in Research series explores how generative AI is studied and used across VCU. The series is designed for faculty and advanced researchers.

VCU librarians will discuss how generative AI is affecting the research landscape broadly as it relates to information discovery, publication, dissemination and use of published materials. Researchers will discuss how they have gone about studying generative AI or have incorporated generative AI in their research process. 

The presentations will be brief to allow ample discussion and questions and time to think about how particular examples relate to other areas of research. Each session is a time to think about the issues involved and to learn from each other.  

Upcoming Sessions

Discussion with Dr. Hyundam (Dami) Gu: AI Implementation on EHR-based Healthcare Observational Study
Online
April 1, 2026, 3 p.m. 

• In this talk, Dr. Hyundam (Dami) Gu will explore how generative AI, particularly natural language processing (NLP) and large language models (LLMs), is reshaping research in medicine and public health. Drawing from her team’s work at the Liver Institute, she will share real-world challenges faced in studying alcohol-associated liver disease and substance use disorders, and how AI tools have helped overcome them. The session will also highlight broader applications of AI in clinical practice, translational research, and real-world evidence generation, offering a practical and inspiring perspective for researchers, students, and health professionals. Register now.

Prior Sessions 

Speculative Fashion: Education Futures
James Branch Cabell Library, Room 203
Nov. 19, 2025, 4 p.m. 

• This presentation examines how generative AI and multimodal technologies are reshaping fashion design and merchandising education within the evolving Metaverse. Positioning the university as a model for broader cultural and technological change, the research explores how students engage with intelligent systems to develop AI literacy, creativity, and ethical awareness. Drawing on qualitative data and interdisciplinary insights, the study highlights the pedagogical opportunities and challenges of integrating human–AI collaboration in design education. 

Can AI Read and Understand the Scientific Literature?
Online
Dec. 3, 2025, 3 p.m. 

• People ask ChatGPT & their fellow LLMs questions every day, but how much does a chatbot (LLM) really know? This presentation will address this question by drilling into some details of organismal biology, asking four different chatbots, for example, what do they know about snakes? While this sounds very specific, it addresses the general problem of how much information an LLM covers, its accuracy, sources, hallucinations, and other problems that chatbots promise to solve, but … don’t. Peter Uetz will present some results he found with a group of students and VCU collaborators. 

Generative AI in Advertising Design
James Branch Cabell Library, Room 205
Feb. 11, 2026, 1 p.m. 

• This presentation discusses how generative AI is used to develop advertisements, and the business, marketing, and ethical implications of synthetic ads. Multiple research findings and techniques will be discussed, illustrating the growing role of AI in research and industry, the effective deployment of ethical AI guardrails and concrete examples of how accessible, low-cost AI technologies can be used to generate AI advertisements that preserve the human element.

GenAI in Academia: Urging Caution and Resistance
Online
March 4, 2026, 1 p.m. 

• This session urges more critical approaches to applying GenAI to research and learning. From environmental impacts to "cognitive offloading", this talk will discuss the costs of GenAI; “GenAI refusal” and critical AI literacy; the AI Disclosure Framework; and the Ethical AI Assessment Tool. 

Discussion with Dr. Shruti Syal: Climbing Ladders and Avoiding the Snakes
Online
March 20, 2026, 3 p.m. 

• GenAI has several ethics issues, even as a research tool. Dr. Shruti Syal will discuss how she uses Perplexity and Prezi AIs in a way that addresses a few of them: knowledge and data privacy, bias, IP, hallucinations, copyright, explainability, workforce reshaping vs training, AI use transparency and detractive learning. Dr. Syal will also discuss how, despite cautionary steps, concerns about training bias, user data security and scalability are not as clearly resolved. 

image credit: Roman Shashko from Noun Project (CC BY-NC-ND 2.0)