Procedural Content Generation (PCG) is a method to automatically generate content with little to no human assistance required. It emerges as a promising tool to generate educational content tailored to individual learning needs, a fundamental aspect of effective teaching.
This review aims to provide an overview of existing research on the educational applications of PCG across disciplines. The key objectives of this paper include identifying the range of domains that have profited from the educational use of PCG, the challenges faced by specific disciplines as well as discerning the similarities and differences in their approaches. By addressing these objectives, this literature review seeks to identify areas for further research and provide insight into understanding the obstacles preventing the adoption of PCG in certain educational domains. By carefully surveying a sample of research conducted on the educational use of PCG in a broad range of disciplines, we found that computer science and mathematics have extensively leveraged PCG to enhance learning experiences, while natural sciences and social sciences could benefit from further research. To the best of our knowledge, fields like geography, physics and biology as well as arts, business and economics remain largely unexplored. Furthermore, challenges such as adjusting content difficulty, generating coherent exercises, and educational accuracy issues have been identified as significant barriers in various fields.