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

TL;DR

This paper analyzes how European legal and normative frameworks interpret authorship in AI-assisted academic work, proposing a qualitative threshold model to distinguish legitimate AI support from authorship undermining practices.

Contribution

It introduces a novel qualitative threshold framework based on European law to assess authorship legitimacy in AI-aided academic work.

Findings

Authorship is a qualitative threshold, not binary, under European law.

GenAI support remains legitimate if under human intellectual control.

Displacement of creative autonomy by AI raises authorship disputes.

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.