NextGen College Advisor

Abstract

Navigating the college admission process in India is often overwhelming for students due to fluctuating cutoff ranks, diverse domains, and the need to consult multiple sources. Traditional methods of college selection rely heavily on manual research, which is time-consuming and error-prone. To address this challenge, NextGen College Advisor leverages machine learning specifically the Random Forest algorithm to predict students\' admission chances based on historical KCET cutoff trends. The platform offers real-time, category-specific (General, OBC, SC, ST) predictions across various domains such as engineering, veterinary, and agriculture. It features secure OTP-based authentication, an intuitive dashboard, and multilingual support to enhance accessibility. By automating data analysis and college recommendation, the system minimizes human effort, increases prediction accuracy, and empowers students with personalized guidance for informed decision-making.

Introduction

I. Introduction

• College admissions in India are complex, decentralized, and data-intensive.

• Students face challenges comparing cutoffs across colleges, especially with category-based reservations and shifting trends.

• NextGen College Advisor is proposed as an AI-powered guidance system to simplify this process.

• Using Random Forest machine learning, it predicts admission chances based on historical KCET data.

• Students input rank, domain, and category; the system provides real-time, personalized college recommendations through a multilingual dashboard.

II. Literature Review

• Traditional platforms are static and lack real-time analysis.

• AI/ML methods (e.g., decision trees, Naïve Bayes) improve personalization but often ignore dynamic cutoff trends and reservation categories.

• Many models focus only on engineering and lack support for other domains (e.g., veterinary, agriculture).

• Language barriers and lack of flexibility limit accessibility.

• Newer hybrid systems add user authentication, localized features, and better data handling.

• NextGen Advisor addresses these gaps with:

  • Domain diversity (engineering, agriculture, veterinary)

  • Multilingual support

  • Category-aware predictions

  • Scalable, secure, and real-time architecture

III. Methodology

• Architecture Overview:

  • Frontend: React.js with multilingual interface

  • Backend: Node.js + Express.js

  • Database: MongoDB for storing user data and prediction logs

• User Workflow:

  • OTP-based login → Input KCET rank, branch, category

  • Random Forest model processes input and outputs admission probabilities

  • Dashboard shows personalized, data-backed recommendations

• System is designed for speed, scalability, and ease of use

IV. Results and Discussion

• Prediction Accuracy: 85%–95% using 6 years of KCET cutoff data

• Key Features:

  • Multilingual Support: Broadens accessibility across regions

  • Interactive Dashboard: Shows predicted cutoffs, colleges, admission chances, and category-wise insights

  • Data Logging: Enables performance monitoring and updates with new trends

Conclusion

NextGen College Advisor offers an intelligent solution to the challenges of college admissions by integrating machine learning with a user-friendly, multilingual platform. Unlike traditional methods that require manual research and comparison across multiple websites, this system automates the prediction of admission chances based on historical KCET cutoff data, providing personalized, category-wise recommendations. Its scalable architecture supports diverse academic domains and ensures accessibility for students from various backgrounds. By streamlining the admission process, reducing effort, and improving decision-making accuracy, NextGen College Advisor empowers students with data-driven guidance and sets a new benchmark for AI-assisted educational support systems.

References

[1] S. P. Jadhav, D. A. Patil, S. A. Kamble, and S. S. Shinde, “College Recommendation System using Machine Learning,” International Research Journal of Engineering and Technology (IRJET), vol. 7, no. 6, pp. 1234–1239, 2020. [2] S. Sonawane, M. Gosavi, A. Pawar, O. Pawar, and D. Dighe, “College Recommendation System Using Artificial Intelligence And Machine Learning,” International Research Journal of Modernization in Engineering Technology and Science, vol. 5, no. 4, pp. 456–462, 2023. [3] S. Bhoite, V. Magar, and C. H. Patil, “AI-driven global talent prediction: Anticipating international graduate admissions,” in Proc. of MIT World Peace Univ., Pune, 2024, ISBN: 9781003471059. [4] I. A. Iwara and I. Dickson, “Development of an automated English-to-local language translator,” International Journal of Scientific & Engineering Research, vol. 9, no. 7, pp. 88–94, Jul. 2018. [5] I. Sommerville, Software Engineering, 9th ed. Boston, MA, USA: Addison-Wesley, 2011.

Copyright

Copyright © 2025 Dr. B. M. Vidyavathi, Manoj B, Damodhar Singh R, Keerthana S, Mohammed Muzzakir. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.