Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes
Kingsley Ugwuanyi, Christian Mair, Sender Dovchin, Iker Erdocia, Maria Kuteeva, Esther Airemionkhale
cs.CL
Submitted: 2026-07-30
Comments: 23 pages, 1 figure
Journal ref: Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes. Journal of English for Research Publication Purposes, 2025, 6(2), 265-289
License: http://creativecommons.org/licenses/by/4.0/
The gist: The rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the legitimacy of diverse Englishes in global
Terminology
Abstract
The rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the legitimacy of diverse Englishes in global scholarly communication. This article responds to these questions through a structured scholarly dialogue involving five sociolinguists from World Englishes and adjacent fields. Organised around five guiding questions, the dialogue interrogates how GenAI tools influence writing practices, reinforce or disrupt dominant language norms, and raise ethical challenges. Contributors reflect on the potential of GenAI to democratise writing processes while also raising concerns about GenAI's tendency to marginalise minoritised varieties and flatten nuance in scholarly writing. Across the dialogue, themes of linguistic (in)justice, researcher agency, and institutional responsibility emerge, with contributors calling for equity-informed policies, critical AI literacy, and inclusive co-design in GenAI development. The article shows the value of dialogic reflection in understanding GenAI's role in AWP. It concludes that while GenAI may reinforce existing hierarchies, it can also serve as a site of resistance, depending on how it is designed, governed and used within scholarly communities committed to linguistic diversity.
Sources
- An Efficient Approach for Studying Cross-Lingual Transfer in Multilingual Language Models
- Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination
- The Artificial Intelligence Disclosure (AID) Framework: An Introduction
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