Generative artificial intelligence (GenAI) tools such as ChatGPT are increasingly used by students in higher education, offering opportunities to support learning, creativity, and efficiency. At the same time, their rapid adoption raises significant concerns related to academic integrity, ethical engagement, data privacy, and uncritical reliance on AI-generated outputs. While institutional policies and educator-focused frameworks are emerging, practical, student-facing guidance remains limited. This article presents development of Using GenAI Tools – Practical Guide for Students, a 16-point practical guide designed to support students in the responsible and reflective use of text-based generative AI tools in academic contexts. The guide was developed through a design-informed, practical-based and iterative process involving literature, policy and practice inputs, classroom use, student feedback, conference-based feedback, academic review, and successive refinement across multiple teaching trimesters. The Technology Acceptance Model and Constructivist Learning Theory informed the guide’s design logic, shaping elements that support perceived usefulness, ease of use, ethical decision-making, critical evaluation, and active engagement with learning. Rather than evaluating learning outcomes or performance gains, the article focuses on the development and rationale of a student-centred guidance artefact that complements institutional academic integrity policies. The resulting guide offers accessible and actionable support for students navigating ethical boundaries of GenAI use, while positioning AI as a supportive tool rather than a substitute for independent thinking or academic responsibility. Finally, it contributes a practice-oriented resource to ongoing discussions on academic integrity, student agency, and responsible GenAI use in AI-enabled higher education.
Academic misconduct is an emotionally charged issue for students and educators. Educators dealing with student academic misconduct perform emotional labor to execute their expected workplace roles which impacts their emotional and physical health. Hochschild’s emotional labor theory is applied to tertiary educators facing academic misconduct, as their work with students and colleagues is managed by their employer organization in order to shape another’s emotions. Tertiary educators’ responses to two open-ended survey questions on academic misconduct and integrity gave voice to their emotional labor driven experiences and concerns. Via qualitative case study analysis, responses showing tertiary educators’ emotional labor and its ramifications were presented within three theoretical themes: institutional regulation of emotions, emotion reactions, and organization cohesion maintenance. Respondents shared how academic misconduct impacted their emotions, professional emotional reactions, reporting likelihood, and job longevity. They shared concerns for their employer and student relationships. Positive, practical steps to address educators’ emotional labor related to academic misconduct included rectifying structural organizational inadequacies, providing academic integrity training, and bolstering collegial support. Clinical trial number: Not applicable.
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.
Abstract Since 2023, universities worldwide have adopted policies restricting or prohibiting the use of generative artificial intelligence (GenAI) in assessed work. In the United Kingdom, the Quality Assurance Agency (QAA) and the Russell Group have issued guidance, and many institutions now operate tiered assessment classification systems. Internationally, An et al. (2025) report that ninety-four per cent of the top fifty US universities (by the ranking used in that study) had issued faculty-facing GenAI guidelines, and Luo (2024) documents broadly similar structures across the world’ s top twenty institutions by QS ranking. This paper argues that such policies frequently lack the technical precision necessary to distinguish between genuinely generative AI use—where a machine produces the assessed intellectual content—and non-generative uses of AI-powered tools, such as optical character recognition (OCR), voice-to-text transcription, and handwriting recognition, where the tool performs a clerical format-conversion function on content the student has already authored. The paper is a conceptual and policy analysis; it advances interpretive arguments and a proposed evidential framework, not empirical findings. Its principal limitations are stated in Section 7.4.
This study investigates contract cheating (CC) in higher education institutions in the United Arab Emirates (UAE) in relation to staff turnover intentions. Grounded in the theories of reasoned action and social responsibility, it aims to investigate the relationship between students’ contract cheating—outsourcing assignments to other parties in exchange for money—and its effects on faculty members’ inclinations to quit their jobs. A survey was distributed in six public and private universities in the UAE and completed by 259 participants. A structural equation model was used to examine the relationships between faculty members’ turnover intentions and their recognition of CC, their perceptions of its long-term effects, and the current CC-related policies of their institutions. The results showed that recognition and long-term effects were positively related to CC, while institutional policy was negatively related. These components of CC are, in turn, positively related to faculty turnover intention. The paper concluded with relevant theoretical and practical recommendations for faculty, and higher education administrations and institutions.
