AI Driven Education Systems: Integrating Faith Informed Ethics for Human Centered Engineering and Technology Learning

Abstract

The rise of artificial intelligence implemented in academic structures has led to the large acceptance of data based models for learner investigation, performance forecasting, and adapted educational assistance. The main focus of this study is how logistic regression is implemented as an educational tool and technique for data mining. Although AI-based educational classifications continue to increase, there is a rising necessity to observe how such models can be practically and reliably used in academic sectors, particularly when shaping the culture via moral and transparent judgment making in an artificial intelligence based environment. These datasets comprise educational attainment, class engagement patterns, and knowledge progress, which are appropriate for binary or multiple class classification responsibilities using logistic regression. The stochastic output of logistic regression allows the implementation of model decisions, permitting educationalists and institutes to recognize how precise learning features affect estimates. These faith and moral support interpretations using logistic regression operate as an explainable AI tool to meet academic values. The current interest of this study focuses on increasing the accessibility of informative data and the rising request for explainable AI models in learning data analytics. This study focuses on Muslim based majority educational institutes to use digital educational platforms using artificial intelligence techniques. The results show the positive use of AI and machine learning models, especially for the upcoming inclusion of learning Islamic culture and moral values for supervising AI integration in the current academic system. The future application of this study can be improved by applying decision tree models to provide a transparent structure that permits challenging model decisions.