LEVERAGING AI TO ANALYZE ESL LEARNERS’ SPEECH PATTERNS ACROSS PROFICIENCY LEVELS - NABIYEVA, Diyora; ABDURAMANOVA, Diana Valeryevna (The Lingua Spectrum, 2025)
This study investigates the use of artificial intelligence (AI) to analyze speech patterns in English as a Second Language (ESL) learners across different proficiency levels, focusing on pronunciation, vocabulary, and syntactic errors. Traditional methods of evaluating learner speech, such as manual transcription and teacher feedback, are often time-consuming, subjective, and difficult to scale. By leveraging automatic speech recognition (ASR) and natural language processing (NLP) tools, this research aims to detect and categorize common speech errors, comparing their frequency and types among beginner, intermediate, and advanced learners. Hypothetical findings suggest that beginners exhibit the highest error rates, particularly in segmental pronunciation, vocabulary misuse, and syntactic constructions, while intermediates show improvement with occasional errors in complex structures, and advanced learners display relatively low error rates, with subtle mistakes in nuanced vocabulary and advanced syntax. AI detection is expected to align well with human annotations for clear pronunciation and grammatical errors, though subtler aspects such as intonation, rhythm, and pragmatics may show lower agreement. The study highlights the potential of AI-assisted analysis to provide objective, scalable feedback, inform curriculum design, and enhance personalized instruction. Limitations of AI, such as sensitivity to nonnative accents and subtle pragmatic errors, are acknowledged, suggesting that AI should complement, rather than replace, human evaluation in ESL learning.