Artificial Intelligence and Linguistic Landscape research

Abstract

Abstract This article explores applications of artificial intelligence (AI) technologies in Linguistic Landscape research. Traditionally, LL research has relied on manual data collection and analysis, often involving photographs of public signage, advertisements, and other visual language displays. However, this manual approach can present challenges, including time-consuming data collection, inconsistent data quality, and potential researcher bias. Two AI technologies in particular hold promise for addressing these challenges in LL research: computer vision (CV) and large language models (LLMs). CV automates the identification and extraction of text from images, improving data accuracy and enabling large-scale image analysis. LLMs, based on natural language processing, can detect, translate, and interpret multilingual text. This article explores the affordances and challenges of using AI technologies in LL research and discusses methods to improve data collection, enhance accuracy, and support the analysis of multilingual environments. It also raises ethical issues and limitations of the technologies.

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