ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Finance
Volume 8 - 2025 | doi: 10.3389/frai.2025.1634640
This article is part of the Research TopicAdvancing Knowledge-Based Economies and Societies through AI and Optimization: Innovations, Challenges, and ImplicationsView all 5 articles
Leveraging Artificial Intelligence to Explore Gendered Patterns in Financial Literacy Among Teachers in Academia
Provisionally accepted
Ruban Christopher.*
A.R Nithya- Hindustan Institute of Technology and Science, Padur, India
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Financial literacy is a critical skill for long-term economic stability, yet gendered disparities in financial knowledge persist across professions, including academia. This study investigates the role of Artificial Intelligence (AI) in identifying and analyzing gendered patterns in financial literacy among teachers in higher education. Using a mixed-methods approach, we collected survey data from 300 academic professionals across diverse institutions, capturing financial knowledge, attitudes, behaviors, and socioeconomic characteristics (including marital status, number of children, and family income). AI-driven natural language processing (NLP) and machine learning (ML) techniques detected linguistic and behavioral differences between male and female participants. Findings indicate statistically significant gender gaps: men scored higher in investing knowledge (Δ=1.9 points, p<0.001) and displayed more confident sentiment (+0.42 vs. -0.15 for women). Intersectional analysis revealed that Science, Technology, Engineering, and Mathematics (STEM) women exhibited narrower gaps (Δ=0.7) compared to humanities women (Δ=1.2), with disparities linked to disciplinary wage differentials and caregiving responsibilities. Socioeconomic variables further explained variation in financial behaviors, with marital status, number of dependents, and income level associated with both financial literacy scores and investment confidence. The results are correlational. AI-powered sentiment and cluster analyses uncovered nuanced behavioral segments, highlighting the compounded effect of gender, discipline, and socioeconomic context on financial literacy. This research contributes to academic discourse by integrating AI analytics with traditional survey methods, enhancing interpretability, and offering actionable, institution-specific recommendations. Proposed interventions include targeted gender-sensitive training, AI-enabled financial coaching tools, and policy reforms supported by educational bodies, government agencies, and dedicated funding streams.
Keywords: artificial intelligence, financial literacy, gender disparities, Academia, machine learning, Natural Language Processing, Socioeconomic context
Received: 24 May 2025; Accepted: 31 Aug 2025.
Copyright: © 2025 Christopher. and Nithya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ruban Christopher., Hindustan Institute of Technology and Science, Padur, India
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