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.
The rapid expansion of generative artificial intelligence (GenAI) has increased concerns about academic integrity in higher education, reshaping the way it was previously conceptualized. As GenAI becomes more prevalent, an in-depth understanding of students’ perspectives appears to be crucial to navigate the ethical use of the innovation. The objective of this study is, therefore, to explore students’ perspectives of academic integrity issues regarding the use of GenAI, focusing on plagiarism, originality, and the fairness of academic assessment results. This study utilized a qualitative research approach, based on a reflexive thematic method. The participants of this study are undergraduate students from Ambo University. The result of the study is unexpectedly paradoxical. GenAI tools have emerged as an essential revolution to improve student academic engagement. They offer students several perceived benefits, such as overcoming language barriers, access to up-to-date resources, and improved academic support. However, a serious concern arises as a substantial number of students are positive about using GenAI and relying on it for coursework as a minor violation. It is also noted that GenAI creates uneven playing fields between students who genuinely work on their own efforts and those who submit work generated by GenAI. Finally, it can be concluded that the students’ view of GenAI can shape their ethical use of these tools, which requires prioritizing awareness raising, developing policy frameworks, and monitoring policy reinforcement. The study may contribute to the quality of education by addressing issues related to academic integrity in the post-digital world.
Remote proctoring has become an essential tool for ensuring academic integrity during online assessments. Evidence highlights the use of several proctoring methods, such as live, AI-based, recorded, and hybrid systems. While these technologies offer significant benefits, they also present challenges, including technical issues, inadequate infrastructure, limited digital literacy, increased stress and anxiety, and privacy concerns. To map the evidence on the use of remote proctoring in the assessment of nursing students. This scoping review employed the framework by Levac et al. (2010). Systematic searches were performed in CINAHL, MEDLINE, Emcare, Scopus, PsycInfo, and Web of Science, complemented by manual searches for studies published between 2015 and 2025. Of the 795 sources, six studies met the inclusion criteria and were included in the thematic analysis. Six studies included an aggregate total of 1,567 nursing students, with individual samples ranging from 17 to 970. The proctoring modalities included live online monitoring, AI-based automated or recorded systems, lockdown browsers, CCTV, and mobile invigilation applications. Remote proctoring was generally perceived as deterring cheating and supporting assessment continuity. Reported challenges included student anxiety, false positives, poor internet connectivity, device incompatibility, browser extension failures, environmental scan errors, device freezing, and incomplete recordings. While remote proctoring can support academic integrity in online assessment, its implementation must balance exam security with student well-being, fairness, and equitable access to digital tools.
As artificial intelligence (AI) technologies increasingly permeate educational spaces, the need for structured and reflective faculty development becomes critical. This study presents a comprehensive case study of a six-week faculty institute designed to support higher education instructors in thoughtfully integrating AI into their pedagogical practices. Framed by Universal Design for Learning (UDL), Bloom’s Taxonomy, and design thinking methodologies, the institute explored the tensions between innovation and academic integrity while promoting inclusive, critical adoption of AI tools. Drawing on session materials, pre/post surveys, participant reflections, and capstone projects from ten interdisciplinary faculty members, this paper examines the experiences and evolving mindsets of educators navigating AI integration. The findings reveal a transformative journey from fear and resistance to collaborative innovation, demonstrating that AI integration, when scaffolded by inclusive design, ethical literacy, and practical application, can enhance faculty agency, curriculum responsiveness, and student engagement. The study identifies four key themes: shifting from fear to curiosity, the desire for ethical clarity, inclusive design as an equity amplifier, and the evolution of faculty from gatekeepers to guides. However, significant challenges persist around policy clarity, institutional support structures, and AI’s perceived legitimacy in teaching and learning. This case study contributes to emerging scholarship on faculty AI literacy and offers a replicable model for sustainable professional development design in a rapidly evolving technological landscape. The research provides practical implications for institutions seeking to bridge the divide between administrative enthusiasm and faculty skepticism, ultimately arguing that AI in higher education transcends fashion or fantasy to become a reality demanding thoughtful pedagogical engagement. Clinical trial number Not applicable.
Applied research ethics competence and training is vital for the professional development of academic researchers across various sectors. Evaluations of online research ethics trainings in the Arab region are scarce. This study aims to review and map a sample of 6 online research ethics courses accessible to researchers in the Middle East and North Africa region to explore their scope, content, and relevance to the context. The research team selected 6 online research ethics courses based on set criteria and developed an assessment tool containing criteria that relate to the course structure, instruction methods, relevance to the context and course content. All courses were self-paced and asynchronous and primarily utilized lectures or interactive modules based on didactic instruction. However, there is room for enhancement in terms of language options, interactive learning opportunities, and contextual relevance. The findings reveal the importance on contextualizing courses, improving instructional methods and continuous improvement and reviews of these courses for relevancy and for meeting contemporary needs. Addressing these areas could lead to more engaging, inclusive, and effective research ethics training, ultimately contributing to the more ethically responsible research practices in the region.
The widespread and increasing adoption of generative artificial intelligence (GenAI) in higher education has raised urgent questions about academic integrity and the reliability of AI detection tools used in high-stakes assessments. This study compares the accuracy of four popular detection tools: GPTZero, Pangram, Copyleaks, and Turnitin on four kinds of academic papers: fully human-written, fully AI-written, hybrid (human with GenAI-inserted passages), and humanised GenAI (AI-generated passages were humanised using a prompt designed to resemble possible student behaviour). Using a synthetic dataset of 160 documents with known ground truth values, we assessed each tool’s detection accuracy. Results show that Pangram consistently performed better than the other tools, achieving high accuracy in detecting fully AI-generated, hybrid, and humanised texts. In contrast, the other tools significantly underestimated GenAI content, particularly for texts generated with the most advanced model. All tools correctly identified fully human texts. To illustrate how the best-performing tool performs in an authentic academic context, Pangram was applied to 1,163 master’s theses submitted in academic year 2024–2025, without known ground truth. The analysis describes the distribution of Pangram’s flagging scores, with flagged cases (45.5
The present work investigates students’ perceptions of academic integrity and their willingness to report academic misconduct. It also investigates their attitudes towards a potential academic misconduct reporting tool at Munster Technological University (MTU). Based on an in-depth qualitative study of four focus groups, the research explores how students make sense of academic integrity and their emotional response to potential reporting. Results suggest that while honesty and fairness are important to students, their understanding of what constitutes misconduct and institutional policies is often limited, influenced by peer culture and confidence in institutional procedures. What emerged was tentative student support for an MTU-wide misconduct tool, conditional on anonymity, transparency and procedural justice.
Academic misconduct, including plagiarism behaviour in particular, remains an ongoing and complex challenge in higher education, especially among postgraduate taught (PGT) students who comprise great diversity of academic and cultural backgrounds. This study evaluates the effectiveness of a bespoke Moodle-based academic integrity training resource developed to enhance academic integrity literacy among life sciences PGT students at our UK-based institution, primarily through targeting plagiarism behaviour at the time of its introduction. An online survey, combining quantitative and qualitative elements from closed and open-ended questions respectively, was conducted among life sciences PGT students (n = 139). Quantitative data analysis of closed question responses revealed that students with prior degrees from elsewhere (PDE) reported significantly lower confidence in their knowledge of plagiarism, the use and integration of source material and using Turnitin but more favourable perceptions with regards to enhancing their knowledge and understanding of plagiarism and other academic integrity breaches compared to those with prior degrees from the UK (PDUK) (p < 0.05). Qualitative, thematic analyses of open-ended responses indicated positive perceptions surrounding the clarity, accessibility, and interactivity of the resource but also demonstrated a need for additional inclusive design features and optional face-to-face support. Students highlighted confusion regarding what constitutes plagiarism, difficulty with referencing and uncertainty surrounding institutional policy and use of digital tools. These results underscore the need for a more inclusive and accessible approach towards academic integrity training for diverse PGT cohorts, with wider implications for institutional strategies to mitigate academic misconduct more broadly. Our key recommendation is for academic integrity training resources to be culturally sensitive, equitable and learner-friendly through the incorporation of varied and blended learning resources and opportunities.
This paper explores the ambivalent role of informal networks in shaping academic careers as well as academic and research integrity in Kazakhstan. Drawing on 23 in-depth interviews with senior university leaders, the study examines how informal networks both facilitate scholarly work and undermine merit-based governance. Building on Ledeneva’s typology of informal networks, the paper identifies four coexisting forms—solidarity, domination, redistribution, and market-oriented networks—and analyzes how each shapes hiring, promotion, authorship, funding allocation, and research evaluation. Although these networks often compensate for institutional gaps arising from rapid internationalization and metric-driven reforms, they also legitimize practices such as favoritism, guest authorship, citation manipulation, and informal interference in evaluation processes. The findings challenge individualistic understandings of academic misconduct by showing that questionable practices are embedded in relational obligations, hierarchical dependencies, and structural incentives. The paper concludes by discussing implications for academic integrity policies and argues for context-sensitive approaches that recognize the persistence and ambivalence of informal networks rather than seeking to eliminate them.
This study examines changes in traffic to contract cheating websites in Spain between 2019 and 2024, with particular attention to two major disruptions affecting higher education: the COVID-19 pandemic and the public availability of generative artificial intelligence (GenAI) writing tools. The aim is to determine whether web traffic and engagement indicators can illuminate how interest in commercial academic outsourcing shifted across this period. A web analytics approach was used to analyse 44 Spanish contract cheating websites through SEMrush data. Monthly organic and paid traffic, engagement metrics (time on site, pages per session and bounce rate), and traffic origin data were collected and examined over a five-year period. The findings show a strong rise in organic traffic during the pandemic years, followed by a marked decline from 2023 onwards. This pattern is consistent with the view that remote assessment conditions increased opportunities for contract cheating during COVID-19, whereas the wider uptake of GenAI may have reduced some users’ reliance on paid providers. At the same time, the data do not demonstrate causation and other explanations, including post-pandemic normalisation, must also be considered. Paid traffic remained much smaller in volume than organic traffic, but websites attracting paid visitors tended to show stronger engagement indicators, suggesting more targeted user acquisition. The study also shows that some of these services continued to benefit from search advertising despite platform policies that prohibit the promotion of academic cheating services. By offering a longitudinal analysis based on observed web behaviour rather than self-report data, the article extends methodological discussion in academic integrity research and provides new evidence on how commercial contract cheating websites have responded to changing conditions in higher education.
The rapid emergence of Generative Artificial Intelligence (GenAI) presents significant opportunities and challenges for higher education, particularly in nursing where academic integrity, clinical reasoning and ethical practice are central to professional preparation. Despite increasing interest in GenAI, limited research has explored how nursing academics are responding to its integration in teaching and assessment. To explore nursing academics’ perceptions and experiences of GenAI use in undergraduate nursing education. We conducted semi-structured interviews with 22 nursing academics from a range of universities across Australia and New Zealand. Thematic analysis was used to interpret the data, with methodological rigour supported by COREQ guidelines. Three key themes were identified: (1) Navigating the Unknown: Ambiguity in GenAI Use, (2) GenAI Challenging Nursing’s Core Values and (3) Developing Ethical Nurses in a Digital Age. Participants described inconsistent institutional policies, diverse academic attitudes, and concerns regarding GenAI’s impact on assessment integrity, critical thinking, accountability, and readiness for safe clinical practice. While some acknowledged its potential as a supportive educational tool, others feared it could erode core nursing values and compromise graduates’ readiness for clinical practice. These findings suggest that GenAI challenges not only academic integrity processes, but also the basis on which nursing educators judge students’ readiness for safe practice. Clearer guidance, curriculum redesign, and values-based integration are needed to ensure GenAI strengthens, rather than compromises, the development of safe, ethical, and competent nursing graduates. Not applicable.
This qualitative study aims to examine academic integrity practices in Kazakhstan’s PhD programs following the recent introduction of a publication requirement. By exploring the experiences of doctoral students navigating this requirement and faculty supervising them, the study seeks to identify common academic integrity violations, the factors contributing to misconduct, and the challenges in ensuring ethical research and publication practices. By situating these findings within the unique context of Kazakhstan’s doctoral education, the study reconsiders conventional understandings of misconduct and offers policy recommendations to strengthen academic integrity in similar environments shaped by high-stakes publication requirements.
Generative artificial intelligence is increasingly used by higher education students during group work; however, evidence on how it reshapes collaboration remains fragmented. This scoping review maps recent research on how generative AI influences student group work in higher education and identifies the reported benefits, risks, and implications for practice. Following the PRISMA extension for scoping reviews, Scopus, Web of Science, and Google Scholar were searched for English-language publications from January 2023 to March 2025. After screening, 18 studies were included and analysed using reflexive thematic analysis. The literature reports the potential benefits of group knowledge development, idea generation, reflective thinking support, communication efficiency, task coordination, and timely feedback. Reported risks include reduced peer-to-peer interaction and critical engagement when tools are over-relied on, alongside privacy, transparency, and bias concerns in AI-mediated collaboration. Across studies, outcomes appear contingent on task design, degree of integration into group workflows, and students’ AI literacy and subject expertise. The review highlights the need for clearer pedagogical guidance on when and how generative AI should be used in group work to preserve human agency, support academic integrity and equitable participation, and align assessments with collaborative learning processes. Future research should prioritise longitudinal and intervention-based studies that examine both learning outcomes and group dynamics over time.
Historical trends in plagiarism are often estimated by comparing survey results from a diverse range of samples, institutions, and measures. However, in multi-institution, multi-method comparisons, changes over time are difficult to separate from differences in methods and context. We assessed self-reported prevalence, understanding, and perceived seriousness of seven forms of plagiarism in surveys of students at the same Australian university on five occasions over 20 years, each separated by 5 years (2004, 2009, 2014, 2019, and 2024). The new 2024 survey reported in this paper included: 310 students from the same institution as the previous surveys and 1867 from five other Australian universities, and an additional survey item assessing plagiarism from generative artificial intelligence (genAI). Results revealed a downward trend in plagiarism prevalence at the same university over time, accompanied by increased understanding and perceived seriousness of plagiarism. New insights include: the comparability of the original university’s results with the wider Australian sample; evidence that students plagiarise both naïvely and when they know the behaviour is plagiarism; plagiarism from generative AI may not have replaced traditional forms of plagiarism; and genAI detector programs have a negligible deterrent effect on plagiarising from genAI. These results suggest that although interventions may reduce plagiarism, it remains an ongoing educational and enforcement challenge. Not applicable.
The adoption of generative artificial intelligence (GenAI) has unsettled established approaches to educational integrity, particularly those that rely on detection and verification of independent authorship. This conceptual and practice-informed paper extends postplagiarism by operationalizing its ethical commitments through assessment design. It argues that detection and verification-based approaches are increasingly misaligned with GenAI-augmented assessment, where human judgement and technological generation are deeply entangled. Drawing on the concept of postplagiarism, it translates ethical commitments into structural assessment principles that foreground evaluative judgement. It reframes educational integrity as a pedagogical practice enacted through evaluative judgement rather than as compliance secured through surveillance. Through practice-informed examples, the paper illustrates how assessment structures can make decision-making, responsibility, and transparency visible within hybrid human-GenAI systems. In doing so, this paper advances educational integrity scholarship by shifting integrity governance from detection-based compliance models to structurally embedded assessment design.
This commentary extends Dr. Sarah Elaine Eaton’s work on academic integrity by applying Dr. McGilchrist’s bihemispheric theory, highlighting how the focus on “enhancement” via AI and neurotechnology aligns with the left hemisphere’s analytical mode, risking a superficial learning environment. This hemispheric imbalance raises concerns for the “soul of learning” as it may weaken emotional intelligence and ethical judgment. Educational institutions should strive for balanced hemispheric engagement, integrating technology to foster holistic understanding, empathy, and wisdom.
The rapid adoption of generative AI (GenAI) in higher education has intensified concerns about academic integrity, particularly for institutions serving English as a Foreign Language (EFL) learners. AI content detectors such as Turnitin and Originality are now widely used to identify potential misuse of GenAI in student writing, yet their accuracy, consistency, and fairness remain to be proven. This study evaluates the reliability of these two commercial detectors using a balanced dataset of 192 texts, composed of authentic EFL student writing produced before the widespread availability of GenAI, professional human-authored texts, AI-generated outputs, and hybrid compositions. Detector outputs were categorized into Human, Hybrid, or AI using established thresholding ranges, and performance was assessed using standard classification metrics alongside statistical tests of significance. Results show that Originality outperformed Turnitin in overall accuracy (0.69 vs. 0.61) and macro-average recall (0.60 vs. 0.51). However, both detectors performed poorly on Hybrid texts, an increasingly common form of student writing, indicating substantial difficulty distinguishing mixed authorship. Performance declined significantly as a result of increased text length and genre variation, with both detectors achieving noticeably lower accuracy on scientific writing than on humanities texts. Originality also showed a borderline trend toward higher accuracy on professionally written texts compared to EFL student writing, suggesting potential fairness concerns. Taken together, the findings demonstrate that while AI detectors may serve as supplementary tools—serve only as indicators that prompt further inquiry, their limitations make them unsuitable as the sole basis for decisions regarding academic misconduct. For EFL-focused institutions in particular, over-reliance on machine-based identification risks misclassifying legitimate student work. The study highlights the need for informed human judgment, clearer institutional policies on acceptable AI use, and continued development of equitable detection methods that account for linguistic diversity.
Academic integrity has emerged as a critical concern within global education systems, particularly with the rise of digital technologies that provide students with unprecedented access to information, facilitating academic misconduct. While many Western educational contexts emphasize individual responsibility for academic honesty, educational systems in Sub-Saharan Africa, such as Ghana and Botswana, face unique challenges shaped by cultural values, resource constraints, and institutional practices. This comparative study explored the perspectives and behaviors of pre-tertiary students in Ghana and Botswana regarding academic dishonesty, including cheating and plagiarism, within the context of their respective educational environments. A descriptive-correlational survey design was employed, with data collected from 600 pre-tertiary students (300 from each country) through a structured questionnaire. The study investigated the relationship between pre-tertiary students’ self-reported engagement in academic dishonesty, their awareness of academic integrity policies, and the level of teacher supervision during assessments. Findings suggest that while both countries exhibit concerns related to academic dishonesty, cultural and institutional factors significantly influence pre-tertiary students’ perceptions and behaviors. This study highlights the need for contextually tailored academic integrity frameworks in Sub-Saharan Africa, which consider both the socio-cultural values and institutional capacities of countries like Ghana and Botswana. It also emphasized the importance of raising awareness about academic integrity policies and improving teacher supervision to foster a culture of honesty and fairness in assessments. The study recommended stronger enforcement of integrity policies, increased supervision during assessments, and embedding ethics education in curricula to foster a culture of academic honesty.
