Task-Centered Analysis of Higher Education Students’ Uses of Generative Artificial Intelligence

This study examines how higher education students use generative artificial intelligence (GenAI) for academic tasks and identifies the types of tasks in which these tools are employed. Our research was conducted in a large multidisciplinary university in Israel. Data from 825 eligible responses to an open-ended item underwent a conventional content analysis using a bottom-up coding approach; data were coded inductively in an iterative process, achieving substantial inter-rater reliability. The findings produced the SOI-MARSMeLLAW framework, which maps eight themes of GenAI use (by frequency, from higher to lowest)—Writing, Learning, Reading, Searching, Meta-learning, Multimedia, Analysis, and Learning Aids—across three information channels: Self, Output, and Input. Overall, we find that students rely on GenAI primarily to support rather than replace their learning, suggesting a flexible and strategic approach that balances efficiency with agency. Two follow-up analyses using the broad data looked at domain-specificity and Task-Centered GenAI Literacy. We found that STEM students emphasizing Learning tasks and non-STEM students highlighting Reading, Meta-learning, and Searching; and that literacy was higher for Writing-related tasks than for Learning-related tasks. These findings have implications for GenAI policy in higher education institutions, and for the redesign of pedagogy and assessment in higher education.