How strong is the evidence in generative AI-related academic misconduct allegations? A mixed-methods analysis

Abstract

Since the public release of ChatGPT in late 2022, higher education institutions have experienced an increase in academic misconduct allegations related to suspected generative AI (GenAI) misuse. A necessary part of these processes is the inclusion of evidence to support and strengthen allegations. To date, no systematic framework exists for understanding trends and patterns in GenAI student academic misconduct and classifying the types and probative value of evidence presented in these allegations. This study addresses that gap through a mixed-methods analysis of 1,162 GenAI-related misconduct case records spanning January 2023 to December 2025 at one regional Australian university. Analysis confirmed an increase in case volumes over the study period and produced an empirically derived 15-code evidence taxonomy, which was applied across the 1,855 evidence items against three probative quality credentials drawn from legal evidence scholarship: relevance, credibility, and inferential force. Results point to patterns across time that differ across evidence types, including an increasing use of fabricated references and student admissions of AI use, and a decrease in allegations being raised without support. These findings highlight a critical gap in current misconduct policies: institutions lack explicit criteria for determining what constitutes reliable evidence in GenAI misconduct cases. To address this, the study offers three contributions: first, an empirically derived taxonomy that categorises the types of evidence used in GenAI misconduct proceedings. Second, a structured framework for assessing the quality and reliability of that evidence, designed for direct application in institutional decision-making. Third, the first empirical analysis at scale of how evidence is currently gathered and evaluated in GenAI misconduct cases.