A Conversational Intelligent Tutoring System for Improving English Proficiency of Non-Native Speakers via Debriefing of Online Meeting Transcriptions

Abstract

This paper presents work-in-progress on developing a conversational system designed to enhance non-native English speakers' language skills through post-meeting analysis of the transcriptions of video conferences in which they have participated. Following recent advances in chatbots and agents based on large language models (LLMs), our tutoring system leverages pre-trained LLMs within an ecosystem that integrates different techniques, including in-context learning, external non-parametric memory retrieval, efficient parameter fine-tuning, grammatical error correction models, and error-preserving speech synthesis. A detailed analysis of the different technologies employed in each of these aspects is provided, along with a description of the datasets used. The system is currently in development, with a planned pilot study to evaluate its effectiveness among students of L2-English.