Jul 2026· International Journal of Advanced Research in Science, Communication and Technology· pp. 222· 0 citations· 10 references
TL;DR
It is argued that detection-centred enforcement is a structurally weak control and proposed instead a layered institutional framework in which policy and governance, pedagogy and assessment redesign, and technology-based assurance operate as mutually reinforcing controls, sustained by a continuous audit and improvement cycle.
Abstract
The public release of ChatGPT in late 2022, and the wave of generative artificial intelligence (GenAI) tools that followed it, has unsettled long-standing assumptions about how learning is demonstrated and assessed in higher education. Essays, reports, code, and even reflective writing can now be produced in seconds by systems whose output is fluent, personalised, and largely indistinguishable from student work. This paper examines the resulting collision between GenAI and academic integrity from a governance and assurance perspective. Drawing on the rapidly growing literature published since 2022, it maps the principal challenges facing institutions: the unreliability and demonstrated bias of AI-text detection tools, the erosion of assessment validity, widening equity gaps, fragmented and reactive policy, limited faculty capacity, and the contamination of scholarly work by fabricated references and hallucinated content. The paper argues that detection-centred enforcement is a structurally weak control and proposes instead a layered institutional framework in which policy and governance, pedagogy and assessment redesign, and technology-based assurance operate as mutually reinforcing controls, sustained by a continuous audit and improvement cycle. Future directions are discussed, including two-lane assessment models, AI literacy as a graduate attribute, provenance and watermarking infrastructure, and the emergence of academic integrity as an auditable domain of institutional risk management
This research report examines the impact of Generative Artificial Intelligence (GenAI) on academic integrity in higher education on a global scale while specifically analyzing professional education in the USA. In this era marked by the widespread presence of Large Language Models like ChatGPT, DeepSeek and Gemini, the traditional ways of assessment are challenged as never before. This study refers to the literature published in the last five years (2021-2026) to assess the pedagogical, ethical, and technical aspects of the use of AI. The analysis finds that there seems to be an important change in the "honor code" approach of the traditional system for a more complex system that has been recognized as "contract cheating 2.0" and biases of algorithmic detection. Main findings include the opportunities that GenAI brings to personalized learning and student productivity as well as the need to radically reimagine assessment frameworks. The report presents a comparison of the existing different detection methods and draws attention to the potential incompleteness of detection, including the dangers of false positives and bias against non-native speakers. Last, it provides recommendation for both the USA Law Schools and regulators for a future of "AI Literacy" and pedagogical integrity rather than punishment-based surveillance.
Sathy Akter*· British Journal of Arts and...· 3 citations
The findings suggest that academic integrity in the age of artificial intelligence (AI) cannot be focused solely on preventing fraud, and this needs to expand to support ethical digital literacy, redesign learning tasks that require human reasoning, and ensure fairness in automated decision-making systems.
W. Phornprasert, W. Nuankaew, Pratya Nuankaew· International Journal of Adv...· 0 citations
Generative artificial intelligence is often framed in higher education as a problem of academic integrity, assessment security, or technology adoption. This framing is necessary but insufficient for doctoral education, where writing, reading, coding, synthesizing literature, and interpreting evidence are not merely academic tasks but formative practices through which students become scholars.
Based on qualitative interviews with twenty-one doctoral students at a large research university in the United States, this study examines how doctoral students understand and negotiate generative AI in their scholarly work. The study began with students in education and was extended through purposive and snowball recruitment to include students across a range of other disciplines, so that the account would reflect more than one scholarly context; interviews were semi-structured.
The findings show that AI functions as an access infrastructure, lowering linguistic barriers for some students and technical barriers for others depending on the demands of their scholarly work. At the same time, students engage in careful boundary work between assistance and authorship, distinguishing grammar support, translation, coding help, and conceptual orientation from intellectual substitution. The analysis further suggests that, among these participants, generative AI is shifting doctoral labor from production toward verification: students' distinctive responsibility increasingly lies in judging the accuracy, legitimacy, ownership, and defensibility of machine-assisted work. Under conditions of policy ambiguity, doctoral students also become primary governors of their own AI use, managing disclosure, caution, verification, and risk.
The article argues that the leadership of digital education should move beyond broad AI policies toward context-sensitive guidance, verification literacy, transparent disclosure norms, and process-based assessment, including the culminating site of doctoral assessment, the dissertation defense. These claims are offered as analytic propositions grounded in a single-site interpretive study rather than as generalizable findings. Generative AI has not made doctoral education less necessary; it has made its purposes more urgent.
With the rapid development of artificial intelligence (AI), generative artificial intelligence (GenAI) has been widely applied in higher education, specifically bringing both opportunities and potential challenges. This review focuses on the application of GenAI in teaching, learning, assessment and institutional governance within the higher education context. By adopting a literature review approach, this paper reviews and analyses research on GenAI in education. The findings reveal that GenAI can effectively improve teaching efficacy, enable personalised learning experiences, and streamline assessment procedures. However, its implementation also draws attention to concerns regarding academic integrity, data privacy, algorithmic bias, and ethical governance. In accordance, higher education institutions should strengthen AI literacy training for faculty and students, while improving institutional guidelines and establishing responsible governance mechanisms. Future research is recommended to conduct longitudinal investigations, cross-cultural comparative analyses, and in-depth studies on teacher professional development, in order to support the sustainable and long-term integration of GenAI into higher education.
Xi Bi· Exploring Science Academic C...· 0 citations
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations