Artificial Intelligence (AI) is transforming education by offering innovative tools that enhance teaching, learning, and administrative processes. However, its integration introduces significant ethical challenges that demand critical attention. This systematic literature review (SLR) explores key ethical concerns associated with AI-driven educational technologies, including data privacy, algorithmic bias, student autonomy, and inclusivity. It systematically analyzing existing literature to provide actionable guidelines for promoting ethical AI use, emphasizing transparency, fairness, and accountability. The review also examines the impact of AI on the dynamics of instructor-student relationships, highlighting both opportunities for personalized learning and risks of reduced human interaction. By addressing these challenges and proposing strategies for responsible AI implementation, this study aims to guide educational institutions in navigating the complexities of AI adoption while fostering equitable and meaningful learning experiences.

Federated Learning (FL) allows healthcare organizations to train models using diverse datasets while maintaining patient confidentiality collaboratively. While promising, FL faces challenges in optimizing model accuracy and communication efficiency. To address these, we propose an algorithm that combines feature selection with synthetic data generation, specifically targeting medical datasets. Our method eliminates irrelevant local features, identifies globally relevant ones, and uses synthetic data to initialize model parameters, improving convergence. It also employs a zero-trust model, ensuring that data remain on local devices and only learned weights are shared with the central server, enhancing security. The algorithm improves accuracy and computational efficiency, achieving communication efficiency gains of 4 to 14 through backward elimination and threshold variation techniques. Tested on a federated diabetic dataset, the approach demonstrates significant improvements in the performance and trustworthiness of FL systems for medical applications.

This innovative practice full paper describes cyber-security Awareness With an AI-Integrated Mobile Application. In the current digital age, where technology and interactions are closely intertwined, the importance of cybersecurity awareness has escalated. It is essential for protecting individuals, organi-zations, and national security. This awareness enables people to make well-informed decisions and apply effective measures against cyberattacks. Human errors and behaviors often in-advertently lead to vulnerabilities, risking exposure to cyber threats. This paper focuses on developing an AI -enhanced mobile application tailored for diverse user groups: children under 14, teenagers between 14 and 18, adults over 18 (including university students, graduates, and the unemployed), and employees. The application aims to evaluate and offer extensive cybersecurity education content divided into three levels for each category, including lessons, videos, stories, scenarios, and exercises to enhance individual awareness levels. Additionally, it leverages AI to provide engaging cybersecurity responses, assess individuals, and support users with chatbot assistance. This strategy educates and empowers users, contributing to a more secure digital landscape.

Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems.

Federated Learning (FL) is a hot new topic in collaborative training of machine learning problems. It is a privacy-preserving distributed machine learning approach, allowing multiple clients to jointly train a global model under the coordination of a central server, while keeping their sensitive data private. The problem with FL systems is that they require intense communication between the server and clients to achieve the final machine learning model. Such complexity increases with the number of clients participating and the complexity of the model sought. In this paper, we introduce synthetic data generation into FL systems with the intention of reducing the number of iterations required for model convergence. In this novel method, clients generate synthetic datasets modeling their private data. The synthetic datasets are then sent to the central server and are used to generate a cognizant initial model. Our experiments show that such conscious method for generating the initial model lowers the number of iterations by a factor of more than 4 without affecting the model accuracy. As such it enhances the overall efficiency of FL systems.