Advanced NLP Models for Technical University Information Chatbots

Abstract - Achieving quality education—a cornerstone of sustainable development—requires providing stakeholders with accurate, relevant, and accessible information about educational institutions. Prospective students often struggle to find consistent and trustworthy details about universities, particularly regarding specialized programs and unique academic opportunities. Discrepancies across websites, brochures, and ranking systems can create confusion, thereby affecting decision-making processes. A viable solution to this issue is the integration of a chatbot application on the official university website. Powered by artificial intelligence, chatbots can simulate human conversations and offer real- time responses to student inquiries. By employing Natural Language Processing (NLP) techniques, such a chatbot ensures the delivery of accurate, standardized information around the clock, significantly enhancing the student counseling experience.[5][10] In this research, a chatbot was developed using NLP methodologies, particularly with the NLTK library, and trained via neural networks to ensure high performance. The chatbot framework involved structuring and interpreting user queries through the creation of an intents.json file, followed by tokenization, lemmatization, and conversion of input data into a bag-of-words format. The neural network, refined using advanced optimization techniques, attained a remarkable accuracy of 99%.[3] This system effectively utilized sequential models known for minimizing overfitting and managing contextual interactions with precision. Further, the integration of pattern matching and semantic analysis significantly improved the chatbot's ability to resolve queries in real time. By combining sophisticated NLP strategies with deep learning, this research delivers a scalable and dependable chatbot solution that provides consistent, clear, and immediate information—empowering students to make informed educational choices. [2][9] KeyWords: Natural Language Processing (NLP),Chatbot,Neural Networks

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Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-05-09
DOI
https://doi.org/10.55041/ijsrem47406
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
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article

Advanced NLP Models for Technical University Information Chatbots

Indhumukhi Kondreddy
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
AI in Service Interactions
article

Advanced NLP Models for Technical University Information Chatbots

Indhumukhi Kondreddy
article en

Abstract

Abstract - Achieving quality education—a cornerstone of sustainable development—requires providing stakeholders with accurate, relevant, and accessible information about educational institutions. Prospective students often struggle to find consistent and trustworthy details about universities, particularly regarding specialized programs and unique academic opportunities. Discrepancies across websites, brochures, and ranking systems can create confusion, thereby affecting decision-making processes. A viable solution to this issue is the integration of a chatbot application on the official university website. Powered by artificial intelligence, chatbots can simulate human conversations and offer real- time responses to student inquiries. By employing Natural Language Processing (NLP) techniques, such a chatbot ensures the delivery of accurate, standardized information around the clock, significantly enhancing the student counseling experience.[5][10] In this research, a chatbot was developed using NLP methodologies, particularly with the NLTK library, and trained via neural networks to ensure high performance. The chatbot framework involved structuring and interpreting user queries through the creation of an intents.json file, followed by tokenization, lemmatization, and conversion of input data into a bag-of-words format. The neural network, refined using advanced optimization techniques, attained a remarkable accuracy of 99%.[3] This system effectively utilized sequential models known for minimizing overfitting and managing contextual interactions with precision. Further, the integration of pattern matching and semantic analysis significantly improved the chatbot's ability to resolve queries in real time. By combining sophisticated NLP strategies with deep learning, this research delivers a scalable and dependable chatbot solution that provides consistent, clear, and immediate information—empowering students to make informed educational choices. [2][9] KeyWords: Natural Language Processing (NLP),Chatbot,Neural Networks

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(05)
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AI in Service Interactions
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