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PERSPECTIVE article
Front. Commun.
Sec. Culture and Communication
Volume 10 - 2025 | doi: 10.3389/fcomm.2025.1598988
This article is part of the Research TopicTeaching and Assessing with AI: Teaching Ideas, Research, and ReflectionsView all 3 articles
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The rise of generative artificial intelligence (GenAI) such as ChatGPT fundamentally challenges traditional assumptions about student authorship and assessment in higher education. Drawing on Michel Foucault's notion of the 'author function' and Roland Barthes' critique of textual authority, this paper argues that AI chatbots expose critical tensions in how we understand and evaluate student work. After examining why conventional approaches to ensuring assessment integrity have become obsolete, I propose a 'tapas model' of assessment that combines different evaluation types: pure human work, bounded AI use, and full AI integration. This model moves beyond binary notions of AI detection and cheating, instead embracing AI as a co-participant in knowledge production while ensuring students develop both traditional and AI-enhanced competencies.The paper argues for shifting from punitive AI detection to transparent AI declaration, treating AI as a methodological consideration rather than a threat to academic integrity. This approach acknowledges that knowledge creation has always involved complex networks and suggests that education must evolve beyond simplistic notions of individual authorship to embrace more nuanced forms of assessment suited to an AI-augmented world.
Keywords: assessment, Authorship, Digital Epistemology, Educational Technology, higher education, Pedagogical innovation, Generative AI, Knowledge production
Received: 24 Mar 2025; Accepted: 14 Apr 2025.
Copyright: © 2025 Hau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mark F. Hau, Roskilde University, Roskilde, 4000, Zealand, Denmark
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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