Artificial Intelligence in Higher Education: Bridging or Widening the Gap for Diverse Student Populations?

This study addresses a critical gap in understanding the differential effects of AI-based tools in higher education on diverse student populations, focusing on first-generation and minority students. Conducted as a case study in an introductory psychology course at a peripheral college, this research employed a mixed-methods approach, combining surveys (n = 110), in-depth semi-structured interviews (n = 20 selected to reflect class diversity), and the lecturer’s reflective journal. Data were analyzed using descriptive and inferential statistics (t-tests, Chi-square) and thematic analysis, with triangulation across data sources to examine how AI-based simulations influenced learning experiences and outcomes. The findings reveal that while AI enhanced content understanding and engagement across groups, it also highlighted and potentially widened educational gaps through an emerging “AI literacy divide.” This divide manifested in varying AI engagement patterns and differences in applying AI knowledge beyond the course, which was significantly more pronounced among majority and non-first-generation students compared to minority and first-generation peers. Qualitative data linked these disparities to prior technological exposure, cultural background, and academic self-efficacy. This study proposes an integrative framework highlighting AI literacy, AI engagement, and AI-enhanced cognitive flexibility as mediators between cultural/technological capital and AI adoption. The conclusions underscore the need for inclusive pedagogical strategies and institutional support to foster equitable AI adoption.