Implementation and Validation of AI-Based Predictive Admission Models Using soft computing and Simulation Techniques

Abstract

KEYWORDS Soft Computing, AI, ML, and admission model. ABSTRACT A key component of preparing people for the rigors of the working world is a university education. The admissions process is critical to the success of universities in providing high-quality education to students. For undergraduate students who want to go to graduate school, getting in requires meeting requirements, doing well on tests, and getting through a difficult application procedure. The challenge is choosing appropriate universities depending on each student's profile, which frequently results in uncertainty and problems caused by inexperience. This study suggests an AI & Soft computing-based admission System as an effective solution to this problem and presents a systematic framework to validate the adaptiveness. Adaptiveness, defined as the ability of the model to adjust to changing inputs, data distributions, and external conditions, is critical for ensuring reliable predictions in dynamic educational environments. By leveraging synthetic data generation, scenario-based simulations, and dynamic testing methods, this research evaluates model performance under varying conditions, including demographic shifts, policy changes, and seasonal trends. The results highlight key metrics, such as accuracy drift, robustness, and time-to-stability, that inform improvements to model adaptability. Using machine learning approaches, such as Linear Regression, the results show that the model performs better than the others, giving students important information early in the application process about their prospects of admission. This rule-based predictive admissions system helps universities and students make well-informed decisions about pursuing higher education by increasing the effectiveness and transparency of university admissions.