Using the information inequity framework to study GenAI equity: analysis of educational perspectives

Introduction. Generative AI presents opportunities and challenges for higher education, particularly concerning equity. Understanding stakeholders' perceptions of equity is crucial as AI increasingly influences teaching, learning, and administrative practices. Method. The study was conducted in a large, research-intensive institution in the US. Participants (n=206) from diverse university roles responded to an open-ended question about how Generative AI affects educational equity. The responses were analyzed based on the information and equity dimensions (Lievrouw & Farb, 2003). Analysis. Data were analyzed using a combination of deductive and inductive coding to identify key themes. The framework of information inequity underscores how disparities in access, skills, and ethical considerations create uneven opportunities for stakeholders to benefit from Generative AI, making these dimensions essential for understanding educational equity. Results. Findings revealed differing focal points among the groups: faculty and staff concentrated on issues of physical and financial access to AI tools, while students placed greater emphasis on the ethical implications and value-based considerations of AI in education. Conclusion. The study suggests that addressing AI equity in higher education requires a comprehensive approach that goes beyond improving access. AI literacy education should include skills development and address ethical considerations, ensuring that all stakeholders' concerns are met.