CET College Recommendation and Smart Option Form Generator using AI: A Review of Machine Learning, Historical Cutoff Analysis, and CAP Option Form Optimization

Author

Harshal Ahire, Pranav Magar, Kadam Kunal, Shriram Shinde, Farendrakumar Ghodichor

Abstract

 The increasing number of engineering colleges, academic specializations, and admission choices has made college selection a complex decision-making process for students appearing for entrance examinations. In Maharashtra, candidates participating in the Maharashtra Common Entrance Test (MHT-CET) Centralized Admission Process (CAP) must evaluate several factors, including percentile, category, applicable seat type, home university, branch preferences, institute location, fees, and historical admission cutoffs. Manual analysis of these factors can be time-consuming and may result in unsuitable or poorly ordered college preferences.

This review examines research areas relevant to artificial intelligence, machine learning, educational recommender systems, admission prediction, historical cutoff analysis, multi-criteria decision-making, and explainable decision-support systems. It organizes the field into recommendation, prediction, ranking, cutoff analysis, and explanation techniques, and outlines criteria for comparing their data requirements, interpretability, limitations, and applicability to MHT-CET admissions. The review methodology specifies literature databases, search queries, inclusion and exclusion criteria, and a data-extraction framework to support a reproducible survey.

Based on these themes, the paper presents candidate research gaps and a conceptual framework integrating student-profile analysis, verified historical CAP data, personalized college recommendations, indicative Dream–Target–Safe groupings, and automated CAP option-form ranking. The framework also considers changing admission rules, data provenance, privacy, fairness, uncertainty, and the need for temporal validation. The framework is a research direction rather than a validated system; its effectiveness must be established through a verified literature survey, implementation, baseline comparisons, and evaluation using reliable admission data. Historical compatibility and model predictions cannot guarantee future admission.

Keywords

Artificial Intelligence; Machine Learning; College Recommendation System; MHT-CET; Centralized Admission Process; Historical Cutoff Analysis; Educational Recommender Systems;

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Additional sources required before submission: verified studies specifically addressing university/college recommendation; admission or student-outcome prediction; educational decision-support and explainable recommendation; and official Maharashtra CET Cell CAP documents for the relevant admission year. Verify bibliographic details against publisher records and add in-text citations. The references above support general technical background and do not constitute a complete MHT-CET-specific literature survey.