  In this article, we introduce and evaluate the concept of robosourcing for
creating educational content. Robosourcing lies in the intersection of
crowdsourcing and large language models, where instead of a crowd of humans,
requests to large language models replace some of the work traditionally
performed by the crowd. Robosourcing includes a human-in-the-loop to provide
priming (input) as well as to evaluate and potentially adjust the generated
artefacts; these evaluations could also be used to improve the large language
models. We propose a system to outline the robosourcing process. We further
study the feasibility of robosourcing in the context of education by conducting
an evaluation of robosourced and programming exercises, generated using OpenAI
Codex. Our results suggest that robosourcing could significantly reduce human
effort in creating diverse educational content while maintaining quality
similar to human-created content.
