LangLingual: A Personalised, Exercise-oriented English Language Learning Tool Leveraging Large Language Models

2025-10-27

AI Summary

Language educators often face challenges in delivering timely feedback and personalizing practice, hindering their ability to address learners’ heterogeneous needs. To address this, we propose an interactive English learning system leveraging large language models (LLMs) integrated with the LangChain framework. Our approach innovatively combines fine-grained grammatical error detection, context-aware adaptive exercise generation, and dynamic learning progress modeling to enable end-to-end personalized language instruction. The system employs natural language understanding and generation to respond to user input in real time and iteratively refines its feedback strategies through continuous interaction. Empirical evaluation demonstrates statistically significant improvements in learner engagement, grammatical accuracy, and self-regulated learning efficacy. These results validate the effectiveness and scalability of LLM-driven adaptive language pedagogy, establishing a robust foundation for intelligent, responsive language education systems.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Addresses limited personalized feedback in language education
• Provides real-time grammar-focused corrections for learners
• Generates adaptive language exercises using AI models

Innovation

• Uses LangChain framework for conversational agent
• Provides real-time grammar-focused feedback system
• Generates context-aware language exercises dynamically