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Design and Implementation of an Intelligent Web-Based Academic Advising System for Caritas University
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, Software Engineering
DOI: https://doi.org/10.64388/IREV10I1-1719825
Abstract
Academic advising plays a crucial role in guiding students toward timely graduation and academic success. However, traditional advising methods are often time-consuming, inconsistent, and limited by advisor availability. This study presents the design and implementation of an intelligent web-based academic advising system for Caritas University. The system integrates rule-based intelligence to provide personalized course recommendations, prerequisite checking, degree progress monitoring, and automated responses to students' queries. The platform was developed using PHP, MySQL, JavaScript, HTML, CSS, and Bootstrap. The system was evaluated through functional testing and user feedback from students and academic advisers. Results indicate that the system improves advising efficiency, accessibility, and decision-making for both students and advisers.
Keywords
Intelligent Academic Advising, Artificial Intelligence, Web-Based System, Academic Recommendation System, Decision Support System, Higher Education.
How to cite this paper
@article{1719825,
author = {Ezekiel Charles Ochojila, Chidera Samuel Madu},
title = {Design and Implementation of an Intelligent Web-Based Academic Advising System for Caritas University},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {1619-1631},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719825.pdf},
abstract = {Academic advising plays a crucial role in guiding students toward timely graduation and academic success. However, traditional advising methods are often time-consuming, inconsistent, and limited by advisor availability. This study presents the design and implementation of an intelligent web-based academic advising system for Caritas University. The system integrates rule-based intelligence to provide personalized course recommendations, prerequisite checking, degree progress monitoring, and automated responses to students' queries. The platform was developed using PHP, MySQL, JavaScript, HTML, CSS, and Bootstrap. The system was evaluated through functional testing and user feedback from students and academic advisers. Results indicate that the system improves advising efficiency, accessibility, and decision-making for both students and advisers.},
keywords = {Intelligent Academic Advising, Artificial Intelligence, Web-Based System, Academic Recommendation System, Decision Support System, Higher Education.},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1719825}
}