Ethical and Privacy-Preserving Framework for AI-Based Personalized Learning Systems

Abstract

The fast development of digital learning technologies has presented the tremendous opportunity based on the creation of intelligent educational systems that serve the needs of different learners. Conventional online learning systems tend to be based on fixed instructional systems, which do not respond well to the behavior of individual learners, their level of engagement, or their learning rate. Consequently, the need to have data-driven learning models, which are able to offer customized directions and guarantee data ethics as well as data privacy, is growing. The current research introduces a smart learning system, which combines behavioral learning analytics, artificial intelligence, and privacy-saving solutions to improve customer engagement and educational performance. The data in educational formats were gathered through the digital learning platforms and preprocessed using the structured preprocessing techniques such as data cleaning, data normalization, and feature engineering. The following machine learning models were used to study the patterns of interaction between learners and provide adaptive learning advice: collaborative filtering, classification algorithms, and deep learning methods. Privacy-saving approaches (such as federated learning and different privacy mechanisms) were incorporated to provide safe model training and maintain sensitive information about learners. The fairness assessment processes were applied to determine possible biases in model predictions and ensure fair learning contingency among various groups of learners. The behavioral engagement indicators that were used to evaluate the experimental approach included study time, completion rate of modules, and forum interaction, as well as assessment performance. The outcomes indicate that the combination of adaptive learning algorithms and behavioral analytics has a significant positive impact on the level of engagement of learners, the rate of completing the module, and the overall outcomes of academic performance. The quantitative analysis also shows that the engagement behavior of learners and assessment performance are closely related and correlated, which explains the necessity of maintaining the constant interaction of the learner in the digital learning process. On the whole, the results indicate that the given intelligent learning framework can be useful to deliver personalized learning experiences and ensure privacy protection, fairness, and secure data processing in the contemporary digital educational systems.