LangLingual: A Personalised, Exercise-oriented English Language Learning Tool Leveraging Large Language Models
Sammriddh Gupta, Sonit Singh, Aditya Joshi, Mira Kim
TL;DR
LangLingual is a personalized language learning tool that uses large language models to provide real-time feedback, generate exercises, and track progress, enhancing engagement and learning outcomes.
Contribution
This paper introduces LangLingual, a novel LLM-powered conversational agent tailored for personalized, exercise-oriented English language learning with real-time feedback and proficiency tracking.
Findings
High usability demonstrated in user studies
Positive improvements in learner engagement
Effective real-time, grammar-focused feedback
Abstract
Language educators strive to create a rich experience for learners, while they may be restricted in the extend of feedback and practice they can provide. We present the design and development of LangLingual, a conversational agent built using the LangChain framework and powered by Large Language Models. The system is specifically designed to provide real-time, grammar-focused feedback, generate context-aware language exercises and track learner proficiency over time. The paper discusses the architecture, implementation and evaluation of LangLingual in detail. The results indicate strong usability, positive learning outcomes and encouraging learner engagement.
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