Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing

Abstract: In this article, we introduce and evaluate the concept of robosourcing for creating educational content. Robosourcing lies in the intersection of crowdsourcing and LLMs, where instead of a crowd of humans, requests to LLMs 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 LLMs. 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.