Who is the author? A legal and normative view of authorship in Generative AI-aided academic works

• URL: http://arxiv.org/abs/2604.04700v1
• Date: Mon, 06 Apr 2026 14:12:05 GMT
• Title: Who is the author? A legal and normative view of authorship in Generative AI-aided academic works
• Abstract summary: We argue that authorship functions as a qualitative threshold rather than a binary attribute.<n>Authorship may remain attributable to the student where GenAI operates as cognitive support under human intellectual control.<n>By contrast, attribution becomes legally and normatively disputable once AI output displaces creative autonomy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread adoption of generative artificial intelligence (GenAI) tools in higher education has fundamentally altered the conditions under which academic work is produced, challenging long-standing assumptions about authorship, responsibility, and learning. While much of the existing literature has focused on technical, ethical, or pedagogical implications of GenAI, comparatively little attention has been paid to the legal and normative aspects of authorship in AI-aided academic work. In this work, we examine how the use of GenAI intersects with the concept of authorship as understood within European regulatory and institutional frameworks. Drawing primarily on European copyright law, notably the requirement of human intellectual creation, the paper argues that authorship functions as a qualitative threshold rather than a binary attribute. Authorship may remain attributable to the student where GenAI operates as cognitive support under human intellectual control. By contrast, attribution becomes legally and normatively disputable once AI output displaces creative autonomy. The analysis places this doctrinal framework alongside broader regulatory principles arising from the AI Act, data protection law, and emerging suprainstitutional governance practices in higher education. We propose a qualitative threshold framework designed to assist in authorship-sensitive assessment of GenAI-aided academic work. This framework provides criteria for distinguishing legitimate AI-assisted academic production from practices that undermine authorship, responsibility, and academic integrity.

Related papers

• Who Owns the Knowledge? Copyright, GenAI, and the Future of Academic Publishing [0.0]
  The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into scientific research and higher education presents a paradigm shift.<n>This study examines the complex intersection of AI and science, with a specific focus on the challenges posed to copyright law and the principles of open science.
  arXiv  Detail & Related papers  (2025-11-24T10:34:38Z)
• A Inteligência Artificial Generativa no Ecossistema Acadêmico: Uma Análise de Aplicações, Desafios e Oportunidades para a Pesquisa, o Ensino e a Divulgação Científica [0.0]
  The rapid and disruptive integration of Generative Artificial Intelligence in higher education is reshaping fundamental academic practices.<n>Main challenges include threats to academic integrity, the risk of algorithmic bias, and the need for robust AI literacy.<n>The future of academia will not be defined by resistance to this technology, but by the ability of institutions and individuals to engage with it critically, ethically, and creatively.
  arXiv  Detail & Related papers  (2025-07-03T18:23:18Z)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in University-Level Academic Writing [0.0]
  This work explores how Generative Artificial Intelligence (GenAI) serves as both a trigger and amplifier of cognitive dissonance (CD)<n>We introduce a hypothetical construct of GenAI-induced CD, illustrating the tension between AI-driven efficiency and the principles of originality, effort, and intellectual ownership.<n>We discuss strategies to mitigate this dissonance, including reflective pedagogy, AI literacy programs, transparency in GenAI use, and discipline-specific task redesigns.
  arXiv  Detail & Related papers  (2025-02-08T21:31:04Z)
• What is Ethical: AIHED Driving Humans or Human-Driven AIHED? A Conceptual Framework enabling the Ethos of AI-driven Higher education [0.6216023343793144]
  This study introduces the Human-Driven AI in Higher Education (HD-AIHED) Framework to ensure compliance with UNESCO and OECD ethical standards.<n>The study applies a participatory co-system, Phased Human Intelligence, SWOC analysis, and AI ethical review boards to assess AI readiness and governance strategies for universities and HE institutions.
  arXiv  Detail & Related papers  (2025-02-07T11:13:31Z)
• Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [51.85131234265026]
  I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms.<n>I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
  arXiv  Detail & Related papers  (2024-12-06T15:36:13Z)
• The Fundamental Rights Impact Assessment (FRIA) in the AI Act: Roots, legal obligations and key elements for a model template [55.2480439325792]
  Article aims to fill existing gaps in the theoretical and methodological elaboration of the Fundamental Rights Impact Assessment (FRIA)<n>This article outlines the main building blocks of a model template for the FRIA.<n>It can serve as a blueprint for other national and international regulatory initiatives to ensure that AI is fully consistent with human rights.
  arXiv  Detail & Related papers  (2024-11-07T11:55:55Z)
• AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities [14.26619701452836]
  Generative AI has drawn significant attention from stakeholders in higher education. It simultaneously poses challenges to academic integrity and leads to ethical issues. Leading universities have already published guidelines on Generative AI. This study focuses on strategies for responsible AI governance as demonstrated in these guidelines.
  arXiv  Detail & Related papers  (2024-09-03T16:06:45Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
  This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw) A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
  arXiv  Detail & Related papers  (2023-11-11T04:13:37Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.