Convo Tutor: Personalized Language Learning Tool
Aditya Menon, D. Philip, Sunayana Chawla, Muthunagai Su · 2025
The current traditional language learning model involves physical textbooks and tutors to be present at all times. Furthermore, traditional chatbots often struggle with contextual understanding, resulting in generic and repetitive interactions that hinder engagement. Artificial Intelligence and Machine Learning have revolutionized language learning with interactive tools such as chatbots. To address this challenge, the proposed work presents Convo Tutor: Personalized Language Learning Tool, offers a practical alternative to traditional methods by allowing real-time conversations that enhance fluency and confidence. This model comprises Retrieval-Augmented Generation (RAG) power Convo Tutor to dynamically retrieve and transform textbook knowledge into interactive dialogues. Fine-tuned language models such as Llama and effective retrieval systems such as Chroma ensure personalized, skill-level-appropriate responses. The proposed model helps in education fields for productivity, curiosity, and motivation. In addition, the proposed model gives personalized feedback and corrects mistakes while mirroring real-world conversations, resulting in better experience in learning instead of human-to-human approaches. Models like GPT-3 and T5 have been further developed in creating conversational AI, allowing adaptive and engaging learning environments. The integration of retrieval processes and personalized feedback within RAG fills the gap between theoretical and practical language learning and brings about a new revolution for how learners become fluent.