Jul 2026· British Journal of Arts and Humanities· 3 citations· 25 references
TL;DR
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.
Abstract
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.
Current practices related to AI use are examined, focusing on LLM-based ghostwriting and the reliability of disclosed interactions as evidence of authentic use, and the possibility of mimicking authentic interactions, which raises concerns about the effectiveness of current approaches.
This document outlines the conceptual, theoretical, and methodological underpinning of the AI-Augmented Pedagogy Integration Model (AAPIM), which has now been further supported by a growing evidence base of 2025–2026 meta-analyses and systematic reviews.
Ahnaf Afsin, Rumaysha Tahan Towaa, Kasif Suhail Ayate et al.· Frontiers in Computer Scienc...· 0 citations
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.
Dr. G. Purushothaman, Dr. S. Ganapathy, Mr. Saurabh Jaiswal, Mr. Thanga Kumaran M· International Journal of Adv...· 0 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
By late 2022, generative artificial intelligence (GenAI) chatbots had gained significant traction and began raising questions about their impact on education. While technological advancements might appear objective and free from human bias due to their calculated precision, they often inherit the partialities embedded in their training data, which tend to reflect the inequities already present in society (Vicente and Matute, 2023). In the process towards a more diverse and inclusive technological society, the role and accountability of universities is essential. Biases related to racism, LGBTQ+phobia, xenophobia, sexism, or ableism can inadvertently seep into the educational process, negatively affecting students’ learning experiences. It is crucial for educators to recognize the potential dangers these technologies pose in the pursuit of a more equitable society and discrimination-free educational environments. Notwithstanding, AI-based tools also hold immense potential in enhancing the transmission of knowledge. This article explores the dual role of GenAI in higher education, particularly in addressing issues of multiculturalism and racial discrimination in the European university classroom. It studies GenAI’s potential to hinder and/or contribute to equity and casts light upon GenAI’s ability to help lecturers and students overcome prejudice. This article employs a qualitative integrative review methodology, synthesizing current scholarship on algorithmic bias with a critical policy analysis of the European Parliament’s 2024 AI Act and recent Eurydice reports (2022, 2023). This dual-layered approach allows for the identification of systemic gaps between macro-legal frameworks and micro-pedagogical needs in multicultural European Higher Education Area (EHEA) classrooms. Finally, the article proposes a series of challenges and considerations that universities must take into account when welcoming GenAI into their spaces.
The rapid advancement of Artificial Intelligence (AI) has led to its widespread adoption in highereducation. A growing body of research has examined its educational implications, includingeffects on learning and academic integrity. Building on this literature, this paper investigateshow students’ orientations toward learning, effort, and ethics relate to their use of AI inacademic contexts. The study was conducted on a sample of 115 undergraduate and graduatebusiness students using a structured questionnaire. Data were analyzed using Chi-squaretests and Cramer’s V coefficients. The results reveal significant relationships betweenstudents’ orientations and AI use. Students who prioritize efficiency, minimal effort, and finaloutcomes are more likely to use AI frequently, trust its outputs without verification, and rely onit for assignments. Likewise, students with more tolerant attitudes toward unethical academicbehavior are more likely to use AI as a shortcut in academic tasks. In contrast, students whovalue the learning process, theoretical knowledge, and academic integrity are more likely tocritically evaluate AI-generated content and consult additional sources. The findings suggestthat AI acts primarily as an amplifying tool rather than an independent driver of academicbehavior, reflecting pre-existing attitudes toward learning and ethics.
Aleksandar Vučković, Ernest Vlačić, A. Davidovic· Notitia· 0 citations