L ang B ot-Language Learning Chatbot (2024)

Abstract: Chatbots are being widely used in educational domain to revolutionize how students interact and learn along with traditional methods of learning. This paper presents our work on LangBot, a chatbot developed for learning Tamil language. LangBot developed integrates the interactive features of chatbots with the study material of the Tamil courses offered by Tamil Virtual Academy, Government of Tamil Nadu. LangBot helps students in enhancing their learning skills and increases their interest in learning the language. Using semi-automatic methods, we generate question and answers related to all topics in the courses. We then develop a generative language model and also Retrieval Augmented Generation (RAG) so that the system can incorporate new syllabus changes. We have performed manual user studies. The results obtained are encouraging. This approach offers learners an interactive tool that aligns with their syllabus. It is observed that this enriches the overall learning experience.

## introduction with the emergence of conversational ai, chatbots or dialogue systems are becoming central tools in many applications such as virtual assistants helping the citizens in getting authentic information from governments, academia, health sector and industry and so on. since user interests may change frequently over time, the ai agents may continuously see unknown (new) user intents. it is necessary and essential to understand the intent and accurately identify the intent behind the user utterance. this will help in generating the correct response for the user intent. data created manually by manual annotation cannot catch up with the requirement and algorithms which use annotated data cannot give accurate understanding of the intent by the user.the history of chatbots was started in 1950 as a result of the turing test, and ideas of that test essentially laid the foundation for the revolution of chatbots. then, many chatbots have come to the stage such as eliza (1966), parry, jabberwacky, drsbaitso (1992), alice (1995), smarterchild (2001), ibm's @xcite , siri (2010), google now (2012), alexa (2015), cortana (2015)etc . natural language processing has transformed many domains, enabling machines to understand the human and technical language helping to carry out the task more efficiently and correctly. one such domain is the educational domain, where nlp has significantly improved by assisting in automatic content generation, enabling teachers in their up skilling. in recent years, significant efforts have been directed toward developing large language models such as openai, mistral2b, med-palm, biobert, and clinicalberetc, these have been designed and trained on vast corpora of texts. the rest of the paper is organized as follows. in section 2, we review related works.in section 3, we give our approach along with corpus description. section 4 describes the results followed by conclusion. ## related work the journey toward developing chatbots to aid in learning has not gathered much attention both in the academics and industry oriented research. a growing body of research has explored the integration of general-purpose models with domain-specific knowledge through techniques like retrieval-augmented generation (rag). this approach involves augmenting the model's generative capabilities with relevant external knowledge retrieved from large datasets, which has been demonstrated to significantly boost the langbot-languagelearning chatbot madhubala s, pattabhi rk rao and sobha lalitha devi au-kbc research centre, mit campus of anna university, chennai, india sobha@au-kbc.org accuracy of model predictions in a particular domain. for instance, the study presented in(xiong et al., 2024) exemplifies how incorporating retrieval mechanisms into generative models can lead to substantial improvements in the accuracy of biomedical information processing. there has been many studies to understand user intent from various domains, ranging from search engine questions @xcite to medical queries @xcite . the approaches used for intent classification include deep learning models such as convolutional neural networks (cnn) (xu and sarikaya, 2013) and attentionbased recurrent neural networks (rnn) @xcite . advancements have been made in creating toolkits and frameworks designed to facilitate the development of rag models tailored to specific domains. the work by @xcite introduces a comprehensive toolkit aimed at streamlining the creation of rag models to build reliable models for various medical tasks. additionally, innovative applications of rag have extended beyond traditional healthcare, as seen in @xcite , which explores the use of ragmodels to provide psychological support to survivors of trauma. in this work rag methodis used to develop a generative, interactive chat system. ## our approach here are the steps that were followed in the development of the chatbot a) data preparation b) model training c) retrieval augmented generation (rag) and ## results and evaluation this chatbot has been tested using human evaluators in a practical environment. at first, people have been selected with different levels of their educational qualification levels and they were divided into four groups of 10 each such as certificate level, diploma level, higher diploma level and graduate level respectively, as the next step, the system was freely distributed among them and asked them to chat with the system. after that they were asked to score for based on five parameters: a) clarity of user interface b) ease of use c) validity of responses d) usefulness e) efficiency and reliability as can be observed from the score table the system has overall satisfactory level of 85%.majority of the participants have expressed satisfaction with the system. the system needs to improve in the area of generation of valid responses which means the answers need to be more specific and to the point.