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

Financial 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.

A 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.

Findings 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 demonstrated narrower gaps (Δ=0.7) compared to women in the humanities (Δ=1.2), with disparities shaped by wage differentials and caregiving responsibilities. Socioeconomic factors-including marital status, family size, and income-were also associated with variations in financial literacy and investment confidence. While the findings are correlational, AI-powered sentiment and cluster analyses provided deeper insights into behavioral segments, illustrating the compounded influence of gender, discipline, and socioeconomic context.

By integrating AI techniques with traditional survey methods, this research advances the study of gender and financial literacy in academia. The combined approach enhances interpretability and highlights the value of context-sensitive interventions. Recommended strategies include gender-responsive financial training, AI-enabled coaching tools, and institutional and policy-level reforms supported by universities, government agencies, and funding bodies.