Academic Integrity in the Age of Generative Artificial Intelligence: Legal and Ethical Perspectives
Unknown authors
2026· Revue Internationale du Law & Education· Vol 1· 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.
The proliferation of Generative Artificial Intelligence (GenAI) in higher education, particularly among undergraduate student population, has raised major contradiction about the traditional notions of academic integrity. This study reviewed related literatures to evaluate the balance of choice between digital governance and academic integrity, with intention of shifting the focus of educators from enforcement of academic integrity to student empowerment strategy through AI-literacy. Altogether, over 50 peer-reviewed articles and institutional policy frameworks published in peer-reviewed journals between 2021 and 2026 were solicited, and synthesized as part of the systematic review process. The key findings revealed a significant policy gap in which over 64% of undergraduate students are utilizing AI tools without formal institutional guidance or constraints. The other significant findings show that the traditional detection-based methods of academic dishonesty are losing their effectiveness, and could lead to a stability contradiction where ambiguous rules cause educators to struggle with competing balance of choice between AI adoption and academic integrity. Overall, the research findings draw the attention of educators and stakeholders to a new empowerment strategy that view AI-literacy as a competency ability to reduce intentional misbehavior in academic process. The study concludes 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. The other key suggestions include making AI-literacy classes mandatory for first-year students, and establishing uniform disclosure policies to encourage transparency and intellectual responsibility.
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
Artificial intelligence (AI) has rapidly transitioned from a research curiosity to a fundamental component of the infrastructure of teaching, research, writing, supervision, and publishing. This conceptual paper contends that the current academic discourse, framed as a binary between prohibition and permission, inadequately addresses the ethical challenges at hand. Drawing upon global AI ethical guidelines, scholarship on academic integrity, and publisher policies, the paper proposes that the ethical utilisation of AI in academic writing should be governed by a coherent system of accountability rather than by isolated regulations. It introduces an original framework, the TRACE model, Transparency, Responsibility, Authorship-accountability, Comprehension, and Equity, as an integrative conceptual tool for educators, researchers, supervisors, editors, and policymakers. This article concludes 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 accelerating deployment of artificial intelligence (AI) across public and private life has outpaced the moral vocabularies available to evaluate it. Prevailing AI-ethics frameworks, articulated largely within Western traditions, converge on principles such as fairness, transparency, and accountability, yet remain thin on the spiritual and communal dimensions of moral judgement in Muslim societies. This study asks how maqāṣid al-sharīʿah, the higher objectives of Islamic law, may be operationalised as an evaluative instrument for AI governance rather than a declarative ideal. Employing qualitative library research, the study analyses primary Islamic sources alongside peer-reviewed scholarship from 2021 to 2026 through content analysis and philosophical-normative reasoning. The analysis maps four recurring problems (the erosion of privacy, algorithmic bias, opacity and diffuse accountability, and the attrition of human moral agency) and evaluates each against the five essential objectives (al-ḍarūriyyāt al-khams). The resulting framework translates every objective into governance criteria and correlates them with globally recognised AI principles, yielding an eleven-criterion instrument together with an ordering rationale for cases of value conflict. 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
Artificial intelligence (AI) is transforming business, government, and society at a pace that exceeds the development of governance frameworks. This article presents an academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026, examining Indonesia's strategic position in the evolving global AI landscape. The presentation argued that Indonesia should become a rule shaper rather than a rule taker by developing governance frameworks that reflect national and regional priorities instead of relying solely on external regulatory models. Responsible AI requires more than ethical principles; it depends on capable institutions, effective implementation, qualified professionals, and credible regulatory mechanisms. Key challenges identified include fragmented governance, implementation gaps, and operational risks associated with large language models, including prompt injection, hallucination, and data leakage. The presentation further emphasised the importance of independent AI assurance, certification systems, and institutional readiness to support responsible AI adoption across strategic sectors. Building digital talent and governance capability was presented as the essential foundation for reducing implementation gaps and strengthening long-term competitiveness. The presentation concluded that developing national capability while adapting international best practices provides the most appropriate pathway for enabling Indonesia to contribute to the future development of AI governance.
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