Artificial Intelligence Models For Strengthening Educational Decision-Making And Risk Prevention
DOI:
https://doi.org/10.29284/rh8thj96Keywords:
Artificial Intelligence, Educational Decision-Making, Risk Prevention, Predictive Analytics, Machine Learning, Smart Education Systems.Abstract
Artificial Intelligence (AI) has emerged as a transformative force in the education sector, enabling data-driven decision-making and proactive risk management in academic institutions. Traditional educational systems often rely on fragmented data sources and manual evaluation processes, leading to inefficiencies in identifying student performance trends, dropout risks, and institutional challenges. AI models, including machine learning, predictive analytics, and natural language processing, provide integrated and intelligent frameworks that enhance decision-making accuracy and enable early risk detection. This study examines the role of AI models in strengthening educational decision-making and mitigating risks such as student attrition, academic failure, and resource misallocation. The research adopts an analytical framework based on data integration, predictive modeling, real-time analytics, and decision support systems. The findings indicate that AI-driven systems significantly improve institutional transparency, optimize resource allocation, and enable early intervention strategies through predictive insights. Moreover, AI models facilitate personalized learning pathways and adaptive decision-making processes, contributing to improved student outcomes and institutional performance. Despite challenges such as data privacy concerns, model bias, and implementation costs, the long-term benefits of AI adoption outweigh the limitations. The paper concludes that AI-based decision systems are essential tools for modern educational institutions aiming to enhance efficiency, reduce risks, and achieve sustainable academic success.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
