In computing, procedural generation is a method of creating data algorithmically as opposed to manually, typically through a combination of human-generated assets and algorithms coupled with computer-generated randomness and processing power (ref).
The amazing thing about procedural generation is that infinite unique possibilities can be created with little work from humans (ref). I've been interested in procedural for a few years, and would encourage you to take a peek at the following resources to learn about procedural generation:
What is cool about procedural generation is how easy it is to start with procedural generation. In the example code below, we can get the following output:
# procedural generator to write a brief history
origin_1 = ["Born", "Hatched", "Invoked", "Discovered"]
origin_2 = ["in the land of", "in the wilds of", "in the forest of", "in the ocean of", "in the small village of", "in the modest hamlet of"]
origin_3 = ["Tr'lor", "Kor'mer", "Kobiyashi", "Greenest", "Mordora", "Gondor'e", "Rivendell", "Mirkwood"]
story_part_1 = random.choice (origin_1) + " " + random.choice(origin_2) + " " + random.choice(origin_3)
I have some students working at high levels of complexity and other students working with more basic levels, as seen above. But for all of them, this is a fun approach to deconstructing a complex system, identifying the patterns within the system, and introducing the correct randomness to the system to make it unique.
Procedural generation gets us close to modeling and simulation where a student must understand a system in order to create a model of it. In my opinion, modeling and simulation is close to the the very best learning we can get.
Procedural generation goes into the stratosphere when students apply machine learning to highly complex systems.