Robosourcing Educational Resources - Leveraging Large Language Models for Learnersourcing

By Paul Denny, Sami Sarsa, Arto Hellas, Juho Leinonen. 2022

40 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Large Language Models, Human-Computer Interaction

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Citations in the corpus

Comparing Code Explanations Created by Students and Large Language Models (2023, background): “In search of a remedy to this problem, researchers have explored the potential of ‘robosourcing’ (i.e., using AI-based generators to create content or scaffold content creation by humans) learning materials [11, 38], including code explanations [26, 27].”
Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language (2022, background): “The impact on educational practice of such technologies is unknown, with arguments on both sides—highlighting concerns of over-reliance by novices [6], and suggesting that the ability to synthesize code automatically could play a revolutionary role in teaching [9, 13].”
The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023, background): “In two cases, the teaching materials being generated were programming exercises [73, 163].”
Using Large Language Models to Enhance Programming Error Messages (2022, background): “Another possibility would be the use of learnersourcing, where students could ask for help from their peers, or LLM-scaffolded learnersourcing [18].”