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@article{185464,
        author = {Vivek Dhanave and Sangita Lade and Vedant Gaikwad and Lokesh Gulghane and Harshvardhan Choudhari},
        title = {Edubot-Mht: An AI-Driven Admission Assistant for Mht-Cet Aspirants},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {1770-1775},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185464},
        abstract = {The admission process for competitive examinations such as MHT-CET often involves a complex sequence of activities, including application submission, counseling, document verification, and seat allocation. Students frequently face difficulties in accessing timely, accurate, and relevant information, leading to delays, errors, and increased stress. To address these challenges, this paper presents the design and development of an AI-powered student support chatbot integrated with Natural Language Processing (NLP) and Machine Learning (ML) capabilities. The chatbot is designed to provide real-time, personalized assistance to students throughout the MHT-CET admission process. It can answer queries related to exam schedules, cut-off lists,counseling procedures, college options, and required documentation, while also offering reminders and notifications. The system supports multilingual communication and can be integrated with official MHT-CET and university portals for live updates. Experimental evaluation demonstrates that the chatbot significantly improves response time, reduces reliance on manual counseling, and enhances user satisfaction. This solution has the potential to transform admission support services, offering scalable, accessible, and intelligent guidance to students across urban and rural regions.},
        keywords = {AI Chatbot, MHT-CET, Student Support, NLP, Machine Learning, Admission Process},
        month = {October},
        }