AI in Higher Education

This exploratory mixed-methods study examines how undergraduate students interpret and engage with generative artificial intelligence (GenAI) in higher education, with particular attention to social stratification and generational status. Drawing on sociological concerns about inequality, access, and the institutional shaping of technology use, the project investigates how students’ baseline orientations toward GenAI shape, and are reshaped by, structured engagement with an AI-supported learning tool.

Across two interconnected phases, the research links students’ initial attitudes, confidence, trust, and prior exposure to GenAI with their experiences learning alongside a researcher-designed AI Skills Trainer that functions as a virtual coach for social science research practices. Rather than treating GenAI as a neutral or purely technical resource, the study conceptualizes AI as a sociotechnical actor embedded within existing educational hierarchies. Particular attention is given to how first-generation and continuing-generation students negotiate trust, authority, and legitimacy when learning with AI.

Survey data illuminate students’ baseline orientations toward GenAI, while AI interaction transcripts and semi-structured interviews capture the meanings students attach to AI-mediated learning and expertise. By centering students’ lived experiences and interpretive frameworks, this research contributes to sociological debates on technology and inequality, offering insight into how generative AI may reproduce, reconfigure, or potentially mitigate stratified educational outcomes in higher education.