TRENDS AND TOOLS IN AI-SUPPORTED PHONOLOGY TEACHING: A CORPUS-BASED META-ANALYSIS
Keywords:
Artificial Intelligence (AI), Phonology Teaching, Pronunciation Learning, Corpus Based Analysis, Intelligent Tutoring Systems, Speech Recognition, Adaptive Learning, Language Education, Automated Feedback, AI in EducationAbstract
The current paper presents a meta-analysis of the research literature on Artificial Intelligence (AI) integration in the teaching of phonology and trends, tools, and implications of AI integration on the pedagogical field, in particular. The paper relies on a corpus of international and regional research published from 2013 to 2024 to analyze the application of AI technologies in teaching phonology, namely, speech recognition, automated feedback systems, and adaptive learning platforms. The analysis determines common methodologies, the most important tools, and current trends, denoting the potential of AI to improve the process of pronunciation training as well as the obstacles linked to the readiness of teachers and the integration of technology. The evidence indicates that AI-based phonology training has the potential to enhance engagement and accuracy among learners, but successful adoption requires thorough design, training of teachers, and addressing ethical concerns. The present review will help to comprehend the changing role of AI in language education as it provides advice to educators, researchers and policymakers.
