ABSTRACT
The potential of Artificial Intelligence (AI) to improve education has been a focus of interest for over half a century. Developments in AI such as deep learning and reinforcement learning have fueled the rapid expansion of AI in Education (AIED). Indeed, the potential of AI to deliver significantly better student outcomes through personalization has never looked more promising. This chapter discusses AIED use cases and considers the challenges that system-wide AIED applications have faced. The authors address data estate modernization, data governance and AI, and efforts and frameworks that Microsoft and international organizations need to study and introduce in parallel with AIED development efforts. The authors conclude that moving from standalone AI projects to system-wide applications requires a holistic approach to AIED, including the development of a data-driven decision-making culture and investments in teaching and learning, faculty capacity, and change management.
