Design and Development of AI-Based Student Performance Prediction System

Abstract

In the contemporary education landscape, predicting student performance plays a vital role in early action and improving academic outcomes. This thesis presents the creation of an AI-enabled Student Performance Prediction Solution that leverages machine learning techniques to accurately forecast students’ academic success and identify those at risk of underperforming. Using Python along with robust libraries such as Scikit-learn and Pandas, the system analyzes diverse student data, including attendance records, assignment scores, exam results, and historical academic performance. Multiple classification algorithms — comprising Random Forest and Support Vector Machine (SVM), Decision Tree, and k- immediate Proximate unit— were implemented and rigorously evaluated. The Random Forest algorithm proved to be the most efficient, attaining an accuracy of 88%, thereby demonstrating its potential in assisting educators with data-driven decision-making. By identifying at-risk students early, the system facilitates targeted support, helping to reduce dropout rates and enhance overall educational quality. This research underscores the transformative role of AI in education and paves the way for integrating intelligent predictive analytics into academic environments. The thesis concludes with suggestions for future work, including expanding data features and real-time performance monitoring.

Introduction

The text discusses the transformation of education through digital technologies like data science, machine learning (ML), and artificial intelligence (AI). Educational institutions generate vast data (exam scores, attendance, behavior), but traditionally rely on manual, biased methods for assessing student performance. AI-driven predictive models can forecast academic outcomes, identify at-risk students early, and enable timely interventions to improve retention and personalized learning.

The motivation behind this research is addressing high dropout and failure rates in higher education by developing an AI-based system that predicts student performance proactively using machine learning tools such as Scikit-learn and Pandas. The main problem tackled is the lack of predictive tools in educational institutions to monitor student progress before academic decline occurs.

The study aims to collect and preprocess student data, compare ML algorithms, build predictive models, evaluate their effectiveness, and identify key factors influencing performance. The system targets higher education institutions, classifying students as “At Risk” or “Likely to Pass” and providing insights for educators.

The methodology involves data collection, preprocessing, model selection (Decision Trees, Random Forest, SVM, k-NN), training, evaluation, and analysis. The study benefits educators, administrators, students, and parents by enabling early interventions and promoting digital transformation in education.

A literature review covers the evolution of AI in education from basic tutoring systems to advanced predictive analytics, highlighting traditional statistical methods and modern ML approaches including deep learning. Common datasets and evaluation metrics (accuracy, precision, recall, F1-score) are discussed, along with identified gaps like limited personalization, lack of real-time analytics, and poor LMS integration.

The system design emphasizes modular architecture for scalability and reliability, integrating data ingestion, preprocessing, model training, evaluation, and user-friendly output to assist academic stakeholders in decision-making.

References

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Copyright

Copyright © 2025 Ritik Atri, Mr. Kapil Singh. 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.