ADVANCED NLP MODELS FOR TECHNICAL UNIVERSITY INFORMATION CHATBOTS

Abstract

To achieve quality education, a key goal of sustainable development, it is essential to provide stakeholders with accurate and relevant information about educational institutions. Prospective students often face challenges in obtaining consistent and reliable information about universities or institutes, especially regarding unique courses and opportunities. These inconsistencies, stemming from sources such as websites, rankings, and brochures, can lead to confusion and influence decision-making. A robust solution to address this challenge is the implementation of a chatbot application on the university's official website. A chatbot, powered by artificial intelligence, can simulate human-like conversations and respond promptly to student inquiries. By leveraging Natural Language Processing (NLP) techniques, a chatbot can provide predefined, accurate, and uniform information 24/7, making it a valuable tool for the counseling process. In this research, a chatbot was developed using NLP concepts, specifically the NLTK library, and trained using neural networks to achieve exceptional performance. The system processed and structured user queries by creating an intents.json file, tokenizing and lemmatizing input text, and converting data into a bag-ofwords representation. The neural network, optimized using advanced techniques, achieved an impressive accuracy of 99%. This approach demonstrated the effectiveness of sequential models, which prevent overfitting and excel in handling contextual queries. Additionally, the chatbot incorporated pattern matching and semantic analysis to enhance real-time query resolution. By integrating advanced NLP methods and neural networks, this research provides a robust and scalable chatbot solution, offering precise, consistent, and accessible information to prospective students, ultimately aiding them in making well-informed academic decisions.