Language Ideology Formation in AI-Mediated Educational Context: A Sociolinguistic Analysis
TL;DR
The analysis suggests that technologies such as NLP and large language models may both reproduce and challenge existing linguistic hierarchies, raising important questions about representation, diversity, and equity in digital environments.
Abstract
This study explores the evolving intersection between Artificial Intelligence (AI) and Sociolinguistics by combining bibliometric mapping with keyword-driven content analysis. Drawing on 69 unique publications identified after a systematic deduplication process (2013–2024), the research integrates quantitative trend analysis with keyword-based thematic interpretation. From an initial dataset of 98 records retrieved from Scopus (n = 64) and Web of Science (n = 34), a subset of 48 publications was selected for deeper examination based on their conceptual relevance. Bibliometric analysis was conducted using ScientoPy and VOSviewer to identify publication trajectories, leading contributors, influential journals, geographical distribution, and emerging research clusters. This mapping was complemented by a qualitative analysis centred on five key terms—Computational Sociolinguistics, Natural Language Processing (NLP), ChatGPT, language, and machine learning—allowing the study to trace dominant themes and conceptual developments shaping the field. The findings show a clear increase in scholarly attention to the sociolinguistic dimensions of AI-mediated communication, with particular emphasis on language ideology, identity formation, and the role of algorithms in shaping discourse. Rather than presenting computational approaches solely as neutral tools, the analysis suggests that technologies such as NLP and large language models may both reproduce and challenge existing linguistic hierarchies, raising important questions about representation, diversity, and equity in digital environments. This study maps the intersection of AI and Sociolinguistics by combining bibliometric mapping with keyword-based interpretation, providing an overview of the field’s development over time. These findings suggest that discussions around ethical and culturally inclusive AI design are becoming increasingly visible within the literature.
