Artificial Integrity: Concerning Patterns of AI Usage Among Undergraduate Students

Abstract

The introduction of large language models has intensified concerns around breaches of academic integrity in higher education. The current narrative on AI-enabled cheating among students has been largely shaped by survey-based measures in academic outlets and anecdotal evidence in media outlets, and personal experiences. We present a multi-pronged method for detecting Concerning AI Usage (CAI) in a preregistered study with 81 undergraduate students at an R1 university in the US. We find that—depending on which signals we rely upon to detect AI usage—41% to 70% of students engaged in CAI. We also investigate the relationships between CAI and demographic features, self-reported intellectual virtue, and learning outcomes, and show the limitations of relying on self-reports for AI usage and intellectual virtue.

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