TDNet Discover

The purpose of this qualitative study was to discern emerging themes and trends about the ethical use of GenAI for teaching and learning at Big Ten Universities. For the utility of this study, the ethical use of GenAI for teaching and learning at Big Ten Universities was generally defined as publicly available policies and guidelines for students and faculty regarding the use or prohibition of GenAI in coursework. The theory guiding this study was Siemen’s connective learning theory as it stresses the role of technology in education and offers a valuable lens to examine trends of how universities are adapting to and implementing GenAI into their curricula. The study employed a content analysis approach to discover trends in policies on the ethical use of GenAI among U.S. universities. The study examined two research questions: RQ1: How is the ethical use of generative AI in teaching and learning addressed in instructional policies and guidelines of universities in the Big Ten Academic Alliance? RQ2: What are trends or patterns in current policies and teaching guidelines on the use of generative AI in instructional contexts in the universities of the Big Ten Academic Alliance? In this study, the setting was the Big Ten Academic Alliance, an organizational consortium of research-intensive universities. A systematic collection and analysis of publicly available materials, including academic integrity policies, faculty guides, technology usage guidelines, and GenAI-specific directives, provided a comprehensive view of institutional practices. The collected university policy documents were imported into Quirkos to facilitate systematic inductive content analysis. The analysis revealed four themes: (1) universities support the use of GenAI by faculty and students, (2) universities view transparency as ethical use of GenAI, (3) universities want to ensure the safe use of GenAI, and (4) universities support faculty autonomy with the use of GenAI in courses.