Published Paper Details:

The last few years have seen artificial intelligence systems transform education at an unprecedented rate. Such systems have far-reaching implications-they bring opportunities and challenges-and their development must adhere to ethical principles that make learning contexts inclusive, equitable, and effective. This conceptual review offers a discussion on the possibility of Bayesian networks as a framework for modelling and addressing complex interconnectivity between ethical considerations, societal impacts, and technical feasibility within AI-driven educational systems. Bayesian networks allow stakeholders to see dependencies and trade-offs that lie underneath informed decision-making and responsible design of AI. It therefore focuses on empowering diverse groups, including women, fully to take an active role in lobbying for ethical development of AI, discussing salient topics in attention to issues like bias, privacy, accountability, and accessibility. This paper gives a theoretical footing from which it is possible to integrate ethical decision-making into AI system design and underlines the need for multidisciplinary education. The study is going to be a roadmap for researchers, educators, and policymakers interested in the development of ethical AI for education.