Ethical considerations in the integration of artificial intelligence into education: a novel deep neural network framework for predicting transparency scores

The integration of Artificial Intelligence (AI) into education offers transformative potential for personalized learning but raises critical ethical concerns regarding transparency, privacy, and bias. This study addresses the challenge of predicting and quantifying. The transparency score is a composite metric (scaled 0-100) that quantifies the degree to which AI-driven educational decisions are interpretable, explainable, and accountable to stakeholders, including students, educators, and administrators. It is computed based on factors such as algorithmic explainability, decision traceability, clarity of stakeholder communication, and compliance with ethical AI guidelines-a key factor for building stakeholder trust. We introduce EduTransNet, a novel deep neural network architecture designed to predict transparency scores based on student demographics, academic performance, and ethical perception features collected from 2,847 students across three Pakistani universities. EduTransNet incorporates fairness-aware mechanisms, including demographic parity constraints and interpretability layers, to ensure ethical predictions. Comparative evaluation against Support Vector Regression, Linear Regression, and Random Forest Regression demonstrates EduTransNet's superior performance (R² = 0.998, p < 0.001). These results indicate exceptional predictive accuracy, with the R-squared value of 0.998 demonstrating that EduTransNet explains 99.8% of the variance in transparency scores-substantially outperforming traditional regression models. The MSE is approximately 6.2 points on a 100-point scale, which is considered excellent for educational outcome prediction tasks, where typical models achieve MSEs exceeding 100. The model not only predicts student outcomes more accurately but also integrates fairness mechanisms to mitigate bias and enhance transparency, offering valuable insights for educational stakeholders, policymakers, and AI developers to enhance accountability and guide ethical AI implementation in educational contexts. This research offers a practical framework for ethical AI governance in education, providing guidelines for educators, policymakers, and AI developers to ensure the responsible implementation of intelligent educational technologies.