Research Symposium

BIO

Arwa Gulzar is a third-year senior at Florida State University pursuing a Bachelor of Science degree. She is actively engaged in undergraduate research and has presented her work at the Florida Undergraduate Research Conference at the University of North Florida as well as the Undergraduate Research Symposium at Florida State University.

Arwa has contributed to several research projects focused on health disparities and mental health. Her work includes “Examining the Associations of Positive Affect with Depression and Suicidality among Sexual and Gender Minority Older Adults,” as well as “Enhancing Inclusion in Clinical Trials: A Scoping Review of Strategies for LGBTQIA+ People and People Living with HIV.” Through these projects, she has gained experience in data analysis, research design, and communicating scientific findings.

In addition to her research involvement, Arwa serves as a UROP Leader in the Undergraduate Research Opportunity Program (UROP), where she teaches sections of the UROP class and supports first- and second-year students as they begin their research experiences. She also helps teach the UROP Leader Training course, helping prepare and guide new undergraduate research leaders.

After graduating in the summer, Arwa plans to pursue a Master’s degree in Data Analysis and Artificial Intelligence to further develop her skills in data-driven research and analysis. She ultimately plans to attend medical school with the goal of becoming a neurosurgeon

Demographic Differences in Artificial Intelligence Trust Scores in Novel Scale

Abstract

Artificial intelligence (AI) is increasingly integrated into healthcare and research settings, yet trust in AI systems may vary across demographic groups. Understanding the structure of AI trust is critical for equitable implementation. This study examined the latent dimensions of AI trust and their associations with demographic characteristics.

Online survey data were collected from 304 respondents; after removing duplicate cases and participants with missing demographic information, the final analytic sample included 271 individuals. The AI Trust questionnaire contained 18 Likert-type items, five dichotomous items, and open-ended responses. Due to highly unbalanced distributions, dichotomous items were excluded from exploratory factor analysis (EFA). Polychoric correlations were used to account for the ordinal nature of Likert responses. Sampling adequacy was excellent (KMO = 0.92), and Bartlett’s test of sphericity was significant (p < .001).

Parallel analysis and scree inspection supported a two-factor solution using principal axis factoring with promax rotation. The two correlated factors explained 53.6% of common variance and demonstrated strong internal consistency (α = 0.94 and α = 0.84). Regression analyses revealed significant differences in primary factor scores across sexual identity, gender identity, and age. Non-binary, transgender/other gender identity, queer, and “another” sexual identity participants reported lower trust compared to reference groups, while older adults reported higher trust.

These findings indicate that AI trust is multidimensional and demographically patterned, highlighting the need for inclusive and equity-informed AI deployment strategies.

Keywords: Artificial Intelligence (AI), Demographic Differences, Factor Analysis, Survey analysis