AI Tools For Speaking Fluency And Pronunciation: Effectiveness And Limitations

Abstract

Artificial intelligence (AI) is changing how we teach speaking in a second language (L2) by making feedback on pronunciation and interactive speaking practice available outside of the classroom. There are two main types of tools that are used today: automatic speech recognition (ASR) systems that turn speech into text and give corrective signals, and conversational agents (like chatbots and speech-enabled assistants) that mimic conversation and keep people talking. Research increasingly indicates that these tools can facilitate quantifiable improvements in specific facets of pronunciation and speaking performance, especially when learners are provided with frequent practice opportunities and when AI-mediated feedback is augmented by peer or teacher support. Classroom studies show that ASR-supported practice can help students' pronunciation and speaking test scores in some situations compared to traditional teaching. It can also boost students' confidence and willingness to speak. There are still some big problems, though. ASR doesn't always rate speech with accents in ways that match how people think it sounds, and its feedback can change depending on the speaker, the task, and the phonological target. Conversational agents can boost the number of interactions, but they might not show how complicated the language is in real life and might favor speech that is easy for machines to understand over speech that is easy for people to understand. Emerging syntheses also underscore ethical risks (privacy, data retention, bias) and pedagogical risks (overreliance, diminished learner autonomy if feedback is not explicitly taught). This article examines research trends, suggests a pragmatic approach for assessing AI tools for speaking fluency and pronunciation, and delineates the circumstances in which advantages are most likely to translate to actual communicative competence.