Transparent Augmentation in Research: Aligning Artificial Intelligence (AI) with Academic Integrity

Kyle S. Wells ()
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Kyle S. Wells: Utah Tech University, Department of Finance, School of Business

A chapter in Business and Sustainable Development in a Globalized and Digitalized Era, 2026, pp 1-19 from Springer

Abstract: Abstract This article examines the integration of generative artificial intelligence (AI) into academic research from the perspective of research integrity. Drawing on the ALLEA Code of Conduct and aligned ethical frameworks, it proposes a model of ‘transparent augmentation’ in which AI is used to support, rather than replace human creative ability. The paper categorizes AI’s functional roles, assesses each against four key integrity principles—reliability, honesty, respect, and accountability—and synthesizes insights from recent literature to develop a proposed checklist for the ethical integration of AI. While generative AI offers powerful tools to accelerate research, it also presents risks related to authorship, attribution, and verifiability. I argue that an explicit and principled framework grounded in widely accepted codes of conduct offers the most sustainable approach.

Keywords: Generative Artificial Intelligence; Research Integrity; Academic Ethics; Ethical AI Integration; AI Transparency; Scholarly Communication; Authorship and Attribution; AI Policy and Regulation; Institutional Governance (search for similar items in EconPapers)
JEL-codes: A20 I23 O33 Z00 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-29346-6_1

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DOI: 10.1007/978-3-032-29346-6_1

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