GenAI Use in Computer Science: Insider Perspectives to Inform Practice

• In Event: AERA Poster Session Friday 9:45am
  In Poster Session: STEM Education and Computational Learning Innovations

Abstract

This mixed-methods study investigates how undergraduate and graduate computer science (CS) students engage with Generative Artificial Intelligence (GenAI) tools in academic contexts. Drawing on survey responses from approximately 300 students and follow-up interviews, the study explores patterns of GenAI use, prompt design strategies, trust calibration, perceived impacts on learning, and ethical decision-making. Grounded in sociocultural learning theory and trust in automation, the research aims to generate student-centered insights to inform university policies and instructional practices. By centering student experiences, the study contributes to evolving discussions on academic integrity, digital literacy, and the future of learning with AI. Findings support institutions in designing responsible, inclusive, and pedagogically sound responses to GenAI integration in higher education.

Authors

• Farhad Roodi, Purdue University

• Nursah Yakut, Purdue University

• William R. Watson, Purdue University