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Seminar Series: Dr. Carolyn Lin
November 11, 2024 @ 2:00 pm – 3:00 pm
Dr. Carolyn Lin, Department of Communication – University of Connecticut
Carolyn A. Lin’s research integrates specific multimedia narratives—tailored to target different audience segments— with digital informatics and data science tools to achieve effective communication outcomes. Through her work studying the effects of human-computer interaction, she has developed or co-developed a medical informatics system, two health videogames, and two mobile apps that focus on health risk management and healthy living education, as well as storm risk preparedness, mitigation, and recovery. Her recent research on human-computer interaction addresses the ways in which augmented reality influences marketing outcomes and how human-AI interaction impacts the perceived role of AI in the workplace.
Delivering Effective Risk Communication Using Interactive Informatic Tools to Facilitate Environmental Literacy, Health and Equities
The major barriers of developing and delivering effective risk communication messages reflect the reality that our audiences have diverse demographic backgrounds, sociocultural values and political ideologies, in addition to low health and environmental literacy. Other barriers can be attributed to the widespread misinformation, disinformation and conspiracy theories about public health and environmental issues that are commonly seen and believed by selected segments of the public. Data-science-driven informatic tools can be developed to facilitate the desired risk communication objectives. For instance, these tools can be designed to allow users—to 1) process information with ease, 2) learn the health and environmental protection techniques in a real-world scenario, and 3) apply these techniques in an immersive environment—to help improve environmental literacy, health and equity.
The challenges of environmental health and equity issues will continue to burden our economy, security, health, safety, and quality of life. For this reason, we need to remain vigilant about designing data tools such as machine-learning algorithms and generative AI to present science-based facts to counter the spread of pseudo-science misinformation through data manipulation.