Artificial Intelligence Reshapes Academic Inequality in Higher Education Through Unequal Access to Learning Enhancement Technologies
Merith Eva Bardales Meneces, Alexandra Rivas Meza, Raymond Portelli Xueres, Karen Roxana Pulido Murillo
Introduction: Artificial intelligence (AI) is expanding opportunities for personalized learning, academic writing assistance, automated feedback, and access to educational resources in higher education. However, students may differ in their ability to benefit from these technologies because of socioeconomic circumstances, unequal technological access, differences in AI literacy, and variations in institutional support. This study aims to examine the relationships between unequal access to AI-enhanced learning technologies and academic inequality among university students. Methodology: The study proposes a quantitative, non-experimental, cross-sectional design with a correlational and explanatory orientation. Data will be collected through a structured questionnaire administered to university students. The analytical framework includes socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, learning engagement, and self-reported academic outcomes. The planned analyses include descriptive statistics, correlation analysis, and multivariable regression. Structural equation modeling may be considered if the sample size, measurement properties, and model specification justify its use. **Results:** Empirical results are not yet available because data collection and statistical analysis have not been completed. The final manuscript will report the participant characteristics, measurement quality, estimated associations, confidence intervals, effect sizes, and hypothesis-testing results based on the observed data. Conclusion: The proposed framework seeks to clarify how access to AI technologies, students' AI-related competencies, socioeconomic conditions, and institutional support may be associated with differences in academic experiences. The study may inform institutional strategies for promoting equitable access to AI-enhanced learning technologies. Any conclusions about educational effects will be based on the empirical findings and interpreted in light of the limitations of a cross-sectional design.