This paper reviews academic integrity policies from over 60 UK universities, focusing on how they address the use of generative AI (GenAI) in assessment. It finds wide variation in clarity, accessibility, and categories of academic misconduct where GenAI use is addressed. Key challenges include inconsistent terminology, blurred lines between policy and guidance, and limited operational clarity. The paper highlights good practice examples and recommends clearer communication, simple and structured frameworks (e.g., traffic light or two-lane models), and more agile policy development. As GenAI use grows, it would be valuable for universities to review and refresh their academic integrity frameworks to ensure fairness, transparency, and educational validity in a rapidly changing assessment landscape.
Academic Integrity in a GenAI World
Silvia Dal Bianco;
2025-01-01
Abstract
This paper reviews academic integrity policies from over 60 UK universities, focusing on how they address the use of generative AI (GenAI) in assessment. It finds wide variation in clarity, accessibility, and categories of academic misconduct where GenAI use is addressed. Key challenges include inconsistent terminology, blurred lines between policy and guidance, and limited operational clarity. The paper highlights good practice examples and recommends clearer communication, simple and structured frameworks (e.g., traffic light or two-lane models), and more agile policy development. As GenAI use grows, it would be valuable for universities to review and refresh their academic integrity frameworks to ensure fairness, transparency, and educational validity in a rapidly changing assessment landscape.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
