Abstract: The increasing demand for efficient communication in educational institutions has highlighted the limitations of traditional enquiry handling systems, which rely heavily on manual responses from administrative staff. These conventional approaches are often time-consuming, prone to delays, and unable to scale effectively with the growing number of student queries. With advancements in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), intelligent chatbot systems have emerged as a viable solution to automate and enhance user interactions. This paper presents EduConnect AI, an AI-driven admission counselor chatbot designed to provide instant, accurate, and context-aware responses to student enquiries across multiple institutions.

The proposed system utilizes a hybrid architecture that combines rule-based response handling with NLP-driven intent recognition to ensure both reliability and adaptability. User queries are processed through an intelligent pipeline where text inputs are analyzed, classified into predefined intents such as admissions, courses, fees, and facilities, and mapped to relevant responses. The backend is implemented using FastAPI, enabling high-performance request handling and seamless integration with frontend components developed using modern web technologies such as HTML, CSS, and JavaScript.

To improve response accuracy and user trust, the chatbot incorporates explainable reasoning by highlighting key factors influencing its responses. Additionally, a multi-institution recommendation module allows users to explore and compare information across different educational providers without manual intervention. The system also supports scalable data storage and analysis through structured datasets, enabling efficient tracking and visualization of user interactions.

Furthermore, the platform is designed with a user-friendly interface that ensures smooth navigation and real-time communication, enhancing overall user experience. Experimental evaluation demonstrates that the chatbot significantly reduces response time, improves accessibility to information, and minimizes the workload on administrative staff. The results highlight the effectiveness of AI-based chatbot systems in transforming traditional enquiry processes into intelligent, automated, and scalable solutions for modern educational environments.

Keywords: AI Chatbot; Educational Enquiry System; Natural Language Processing; Machine Learning; FastAPI; Admission Counseling; User Interaction; Decision Support System; Automation; Web-Based System.

Downloads: | DOI: 10.17148/IARJSET.2026.13437