Acharya 2 0 ai tutor supports hindi students with educational content
Acharya 2.0: An AI Tutor for Self-Learning Subjects in Hindi
Computation and Language
Summary
People who speak Hindi have fewer technology tools because there isn’t much data available to teach computers their language. The authors created an AI tutor called Acharya 2.0 that helps Hindi-speaking students by combining smart language models with extra knowledge sources. This makes it easier and faster for the AI to understand and answer students’ questions accurately. The tutor works through a website allowing students to get academic help in Hindi more effectively.
What this means in practice
- •For web developers: Build educational websites that provide academic help in Hindi by integrating the Acharya 2.0 AI tutor with retrieval-augmented generation technology.
- •For language technology teams: Develop better Hindi language tools and applications by using Acharya 2.0’s design combining external knowledge with large language models for low-resource languages.
Authors
Shantipriya Parida, Debasish Dhal, Sonal Khosla, Sambit Sekhar, Muhammed Abdur Rahmaan Kamaldeen
Abstract
Text processing is crucial for low-resource languages as it enables the development of tools and technologies that bridge the digital divide and provide equitable access to information and services. By empowering low-resource languages with robust text processing, we can preserve linguistic diversity, enhance education, improve communi- cation, and foster inclusivity in a rapidly digitizing world. Due to the lack of sufficient annotated datasets and lin- guistic resources, it is challenging to develop accurate and reliable language technologies for these low-resource languages. Large Language Models (LLMs) have attracted sig- nificant attention with their performance and the value they produce. However, with the ever-evolving language, adapting LLMs for a specific task is time-consuming and resource-intensive. Thus, Retrieval-Augmented Genera- tion (RAG) has overcome this limitation by augmenting LLMs with external knowledge, thereby improving their efficiency and extensive task-specific adaptation. Most applications of the RAG technique have been limited to English and has seen limited application in In- dic languages. This paper introduces the RAG-powered AI-Tutor application, Acharya 2.0, designed to produce academic assistance to Hindi-speaking students. The client-server-based web application integrates many fea- tures to support students in accessing and interacting with educational content.