Leveraging artificial intelligence to explore gendered patterns in financial literacy among teachers in academia

IntroductionFinancial literacy is essential for long-term economic stability, yet persistent gender disparities in financial knowledge continue to be observed across professions, including academia. This study explores how Artificial Intelligence (AI) can be applied to identify and analyze gender-based patterns in financial literacy among higher education faculty.MethodsA mixed-methods design was employed, combining traditional survey instruments with AI-driven analytics. Survey data were collected from 300 academic professionals across diverse institutions, capturing financial knowledge, attitudes, behaviors, and socioeconomic characteristics such as marital status, number of dependents, and family income. Natural language processing (NLP) and machine learning (ML) techniques were used to detect linguistic and behavioral differences between male and female participants.ResultsFindings revealed statistically significant gender gaps in financial literacy. Male participants scored higher in investing knowledge (Δ=1.9 points, p<0.001) and expressed greater confidence (+0.42 sentiment vs. -0.15 for women). Intersectional analysis showed that women in STEM disciplines demonstrate