International Research Journal of Education and Technology

In recent years, conversational agents have gained significant attention in educational institutions for providing instant and automated information services. This paper presents the design and implementation of a customized college information chatbot developed using Natural Language Processing (NLP) and Machine Learning techniques. The proposed system aims to assist students, faculty, and visitors by answering college-related queries such as admissions, courses, departments, examinations, and placements in an efficient and user-friendly manner. The chatbot utilizes the NLTK library for text preprocessing tasks including tokenization, stopword removal, and stemming, which helps in improving the quality of input data. Feature extraction is performed using the TF-IDF vectorization technique, and intent classification is achieved through machine learning models implemented using scikit-learn. Based on the predicted intent, appropriate responses are retrieved from a predefined knowledge base. The system is lightweight, operates without internet dependency, and can be deployed on low-resource environments, making it suitable for academic institutions. Experimental results demonstrate that the chatbot provides accurate and relevant responses for domain-specific queries. The proposed approach offers a cost-effective and scalable solution for automating information access in college environments and serves as a foundation for future enhancements using advanced deep learning models.

Index Terms - Natural Language Processing, Chatbot, Machine Learning, Scikit-learn, NLTK, Institutional Assistant, Text Processing, AI Chatbot.