2026· International Journal of Multidisciplinary Research and Growth Evaluation· Vol 7, pp. 386-392· 0 citations
TL;DR
The results indicate that the utilization of AI in academic advising accelerates assistance and enhances student retention in higher education.
Abstract
This study aims to develop and implement an intelligent academic monitoring system, the Academic Intelligence Centre. The primary objective is to monitor student grades and attendance in real time. This methodology employs a statistical risk model that integrates grades and absences to provide a 'risk index.' The platform was developed using the Streamlit framework and the Plotly library for visual analysis. This configuration facilitates fundamental, predictive, and advisory data tasks. The technology recognized students at danger of failure prior to finals and offered automatic assistance, including advice programs. The solution provides a comprehensive overview of student data. It assists educators in transitioning from basic reporting to intelligent, AI-driven judgments. The results indicate that the utilization of AI in academic advising accelerates assistance and enhances student retention in higher education.
An Explainable Artificial Intelligence (XAI)-based Learning Analytics Framework designed to identify at-risk students at an early stage of a programme or semester while providing interpretable, human-understandable justifications for each prediction is proposed.
Ankit Kumar Singh, Rubi Singh, Mohd Nadeem· International Journal of Sci...· 0 citations
Recent advances in artificial intelligence have created new opportunities for efficient student management in higher vocational colleges, especially in campuses where academic, behavioral, psychological, and employment data are generated through digital platforms and wireless information systems. Based on empirical dat...
Feng Quan, Peng Liu, Kai Quan· Advanced Electromagnetics· 0 citations
The findings demonstrate the capability of AI models in minimising dropout risks and enhancing academic performance through prompt, data-informed assistance.
A Machine Learning–based system that analyzes academic and behavioral data to predict student performance in advance and improves academic monitoring, enables early intervention, and enhances overall student success rates through data-driven decision-making is proposed.
Shaheela Y, Vigashini S, Surya Prakash Av et al.· International Journal Of Rec...· 0 citations
Student monitoring in elementary education remains challenging when academic records, attendance, activeness, and emotional indicators are managed through fragmented administrative processes. This condition limits teachers’ ability to identify student risks early and provide timely interventions. This study aimed to de...
Reza Dwi Putra Hasian, Kamdan Kamdan, H. Hermanto· bit-Tech· 0 citations
The research showed that using multiple data types significantly increases the predictive power of the model, and among all methods evaluated, multiple linear regression had the best balance between accuracy and computational requirement for predicting student performance.
Botan Onat, A. Bilge, A. Akın· International journal of 3d...· 0 citations
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