AI-Driven College Admissions and Counselling *
1st Dr.Radhakrishna Naik
Department of Computer Engineering, Sanjivani College of Engineering,
Kopargaon, India radhakrishnanaikcomp@sanjivani.org.in
2nd Swapnil Jadhav
Department of Computer Engineering, Sanjivani College of Engineering,
Kopargaon, India swapniljadhav3181@gmail.com
3rd Rajwardhan jadhav
Department of Computer Engineering, Sanjivani College of Engineering,
Kopargaon, India rajwardhanjadhav7666@gmail.com
Abstract—Over the past few years, the integration of Artificial Intelligence (AI) in education has significantly improved student support systems. However, the college admissions and counseling process remains largely manual, leading to inefficiencies and limited accessibility for students seeking personalized guidance. Traditional methods often fail to provide real-time, data-driven assistance, making it challenging for students to make informed decisions about college selection and admission criteria.This paper presents an AI-Driven College Admissions and Counseling Assistant, designed to streamline the admissions process by offering personalized, real-time responses to student inquiries. The system utilizes machine learning and Natural Language Processing (NLP) techniques to analyze structured data from official admission portals, including cutoffs, rankings, and seat availability. The model is built using a Retrieval-Augmented Generation (RAG) approach, where student queries are processed using a Chroma vector database, and responses are generated by OpenAI’s GPT model. The predictive component of the system was evaluated using multiple classifiers, including Support Vector Machine (SVM), Random Forest, and Logistic Regression, achieving an accuracy of 87.5% with an AUC of 0.94%[1]. Further optimization with feature selection techniques improved accuracy to 90%, ensuring high precision in query classification and response generation.The counseling assistant is deployed on scalable cloud platforms such as Azure, Render, and Hostinger, providing seamless access through a user-friendly web interface. By automating admissions counseling, the system enhances accessibility, minimizes student uncertainty, and empowers them with relevant, data-backed insights to make well-informed academic decisions.
Keywords -1. AI in education, 2. College admission coun- seling, 3. Machine learning in student guidance, 4. AI chatbot for admissions, 5. NLP-based admission query classification,
6. Predictive analytics in education.
