Responsible AI in academia: policies and guidelines in US universities

Abstract

Purpose With the democratization of access to artificial intelligence (AI) via large language models, questions about AI ethics in education are at the forefront of educational policymaking. This paper aims to understand how US universities address AI ethics in education, particularly in coursework, research and academic integrity. Design/methodology/approach This research empirically evaluates emerging AI policies at 170 US universities via a hybrid content analysis approach that applied both inductive and thematic analysis. The deductive analysis is applied to identify the structures of strategies, norms and rules based on established institutional theory. Findings This study identified many diverse strategies, with fewer emergent norms and scant rules, along with a pattern that suggests those universities with both undergraduate teaching and research excellence lead the way in setting boundaries and articulating expectations and consequences for misuse of AI in higher education. Originality/value Using thematic and structured content analysis, this study identifies policy concerns, institutional frameworks and emerging norms related to AI use in US higher education. The study also highlights universities’ concerns and intervention strategies, while providing policy recommendations based on identified trends and best practices.

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