Feedback is a crucial component of student learning. As advancements in technology have enabled the adoption of digital learning environments with assessment capabilities, the frequency, delivery format, and timeliness of feedback derived from educational assessments have also changed progressively. Advanced technologies powered by Artificial Intelligence (AI) enable teachers to generate different types of feedback supporting student learning. Despite the rapid uptake of digital technologies in education, previous studies on educational feedback primarily focused on the theoretical underpinnings of feedback practices, which are limited in terms of their coverage of AI-based technologies. This paper aims to inform both researchers and practitioners about the present and future of AI applications in feedback practices, identify and organize potential areas for the use of AI for feedback purposes, and establish venues for AI research and practice in educational feedback. Furthermore, the role of the three branches of AI (i.e., natural language processing, educational data mining, and learning analytics) in feedback practices and potential areas for their future development are discussed.