Academic Integrity in the Age of Generative Artificial Intelligence: Legal and Ethical Perspectives
Unknown authors
2026· Revue Internationale du Law & Education· 0 citations
TL;DR
Examination of the legal and ethical perspectives of use of generative AI in academic backgrounds through a doctrinal and thematic analysis of recent scholarship, institutional guidance and evolving governing instruments concludes that transparent disclosure frameworks, redesigned assessment practices and layered governance models offer a more secure path forward.
Abstract
The generative artificial intelligence (AI) plays a significant role in fields of higher education and research. It has disturbed the established concepts of authorship, novelty and academic integrity. This paper examines the legal and ethical perspectives of use of generative AI in academic backgrounds through a doctrinal and thematic analysis of recent scholarship, institutional guidance and evolving governing instruments. It reflects that there is inadequacy of binding statutory regulations, governance currently focus on institutional policy, publisher and funder mandates and developing rules of disclosure rather than legislation. Ethically, there has a shift of the debate from individual misconduct towards general accounts that involve assessment design, institutional culture and equitable access to AI tools. The analysis identifies continuing tensions between detection-based enforcement and due process, and between innovation and fairness. The paper concludes that transparent disclosure frameworks, redesigned assessment practices and layered governance models offer a more secure path forward than total prohibition or continued reliance on untrustworthy technologies of detection.
It is concluded that in order for undergraduate education to continue to be relevant in a society where AI is pervasive, governance must change toward process-oriented evaluation and relational originality.
Joe Mutebi, Brian Mugisha, Ibrahim Adabara et al.· F1000Research· 0 citations
The rapid adoption of generative artificial intelligence (GenAI) in higher education has outpaced institutional readiness, creating urgent ethical and regulatory challenges that threaten academic integrity, data privacy, and educational equity. While global frameworks like UNESCO’s Guidance for Generative AI in Education (2023) advocate for human-centric design, and national laws such as FERPA mandate student data protection, no existing model systematically integrates these domains into a cohesive governance structure. This study addresses this critical gap by proposing the Ethico-Regulatory Governance (ERG) Framework, a conceptual model designed to bridge global ethics with local compliance. Developed through a systematic synthesis of 68 peer-reviewed studies, policy documents, and institutional guidelines, the ERG Framework consists of four interlocking layers: Foundational Principles (UNESCO values), Regulatory Anchors (FERPA/GDPR alignment), Institutional Mechanisms (audits, disclosure, training), and Pedagogical Integration (process-based assessment, prompt engineering). The framework transforms abstract principles into actionable practices, enabling institutions to move beyond reactive policies toward proactive, accountable governance. Key findings demonstrate that effective AI integration requires not only technical oversight but also stakeholder co-design, bias mitigation, and continuous feedback loops. By operationalizing ethics through enforceable mechanisms, the ERG Framework offers a scalable, adaptable solution for universities navigating the complexities of GenAI. Its implementation can safeguard core academic values while fostering innovation, ensuring that AI serves as a partner—not a replacement—for human judgment in teaching, learning, and research.
Christian Roberto Cabezas Freire, Nayana Desai· Revista Hambatu Science· 0 citations
It is concluded that originality, authorship, integrity, fairness, and transparency are interdependent concerns rather than separate issues, and that responsible AI use is best understood as a disciplined, disclosed collaboration with a non-author tool.
K. A. Badaru· Interdisciplinary Journal of...· 0 citations
The study contributes an operational, value-based model that complements rather than displaces existing regulatory approaches, offering developers, regulators, and Shariah boards a design vocabulary for anticipating harm before deployment.
Maman Supardi, Hilmiy Hanif, Mursyid Rahman et al.· West Science Islamic Studies· 0 citations
An academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026 is presented, examining Indonesia's strategic position in the evolving global AI landscape.
Patrick Rudolf Dannacher Dannacher· Proceeding Jakarta Geopoliti...· 1 citation
The
rapid adoption of Gen-AI tools such as ChatGPT and Gemini in science education
has created unprecedented opportunities for learning, while simultaneously
threatening core ethical scientific attitudes. This conceptual paper examines
how honesty and integrity, as foundational values in science, are being tested
in an AI-mediated educational environment. The paper argues that the ease of
fabricating data, generating lab reports, and obscuring AI use necessitates a
rethinking of how ethical scientific attitudes are taught, assessed and
modelled in science education research. Based on literature in science ethics
and educational technology, a three-dimensional ‘‘AI Disclosure-Accountability
Model’’ is proposed emphasizing truthfulness in reporting, transparency in AI
use, and responsibility for verifying AI outputs. The paper further discusses
pedagogical strategies, assessment redesigns, and institutional policies needed
to foster honesty and integrity among students and researchers. It concludes
that intentional cultivation of ethical attitudes is essential to preserve the
credibility and integrity of science education in the age of Gen-AI
Geoffrey Aondolumun Ayua, Tertsea Caleb Kwagh· Journal of Studies in Scienc...· 0 citations