# Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing

arXiv: https://arxiv.org/abs/2211.04715 · HTML: https://mlsanity.com/paper/2211.04715/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2022.

## Built on by (most cited first, 9 of 9)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors](https://mlsanity.com/paper/2403.15586/) (2024)
- [RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainer](https://mlsanity.com/paper/2409.02074/) (2024)
- [Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation](https://mlsanity.com/paper/2306.06386/) (2023)
- [Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy](https://mlsanity.com/paper/2309.13500/) (2023)
- [A Survey Study on the State of the Art of Programming Exercise Generation using Large Language Models](https://mlsanity.com/paper/2405.20183/) (2024)

## Abstract (arXiv)

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.