Mapping the Ethical Landscape of GenAI: Insights from Applied Linguistics Publication Policies

Akbar, Maryam and Naz, Komal (2025) Mapping the Ethical Landscape of GenAI: Insights from Applied Linguistics Publication Policies. International Journal of Innovative Science and Research Technology, 10 (10): 25oct243. pp. 705-720. ISSN 2456-2165

Abstract

The swift rise of generative AI (GenAI) in 2022 has led to extensive acceptance in academic fields; yet, applied linguists have not achieved agreement on its ethical and suitable application in research. This study underscores the urgent necessity for enhanced GenAI literacy among scholars, especially those involved in composing research articles. We analyze 76 papers chosen from 170 high-impact applied linguistics journals to examine the scope and character of GenAI use guidelines. Three fundamental dimensions—authorship, use cases, and human responsibility—were addressed by the seventeen particular elements and four general requirements that comprised the structured checklist. The results indicate substantial discrepancies among journals. Only fifty percent provided guidelines linked to GenAI for authors, and the comprehensiveness and extent of these suggestions differed significantly. Significant discord existed concerning the applicability of GenAI technologies for functions such as idea generation, image or data creation, data collecting, analysis and interpretation, or manuscript composition. Moreover, the inconsistency in the declaration of GenAI usage further complicated ethical interaction with the technology. In light of these concerns, we suggest implementable solutions for journals to improve their GenAI-related policies and encourage responsible usage among authors. A new conceptual framework describing the competencies researchers need to navigate the ethical and transparent use of GenAI is introduced in our study, GenAI-LR, which is central to research article writing. This study offers pragmatic recommendations based on empirical evidence to assist scholars and editors in harmonizing GenAI practices with advancing academic norms.

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