# Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation

arXiv: https://arxiv.org/abs/2306.06386 · HTML: https://mlsanity.com/paper/2306.06386/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 167 references that are in the map.
- First posted on arXiv 10 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level](https://mlsanity.com/paper/2112.15594/) (2021)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2104.04034/) (2021)
- [RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities](https://mlsanity.com/paper/1910.05522/) (2019)
- [QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation](https://mlsanity.com/paper/2210.04320/) (2022)
- [Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing](https://mlsanity.com/paper/2211.04715/) (2022)
- [Learning to Incentivize: Eliciting Effort via Output Agreement](https://mlsanity.com/paper/1604.04928/) (2016)
- [DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions](https://mlsanity.com/paper/2111.10058/) (2021)
- [RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests](https://mlsanity.com/paper/1704.00556/) (2017)
- [A Multivariate Elo-based Learner Model for Adaptive Educational Systems](https://mlsanity.com/paper/1910.12581/) (2019)

## Built on by (most cited first, 6 of 6)

- [Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters](https://mlsanity.com/paper/2308.06088/) (2023)
- [Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources](https://mlsanity.com/paper/2306.10509/) (2023)
- [Automating Autograding: Large Language Models as Test Suite Generators for Introductory Programming](https://mlsanity.com/paper/2411.09261/) (2024)
- [Large Language Models Meet User Interfaces: The Case of Provisioning Feedback](https://mlsanity.com/paper/2404.11072/) (2024)
- [Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models](https://mlsanity.com/paper/2309.10444/) (2023)
- [Adaptive Experiments Under Data Sparse Settings: Applications for Educational Platforms](https://mlsanity.com/paper/2501.03999/) (2025)

## Abstract (arXiv)

Engaging students in creating novel content, also referred to as learnersourcing, is increasingly recognised as an effective approach to promoting higher-order learning, deeply engaging students with course material and developing large repositories of content suitable for personalized learning. Despite these benefits, some common concerns and criticisms are associated with learnersourcing (e.g., the quality of resources created by students, challenges in incentivising engagement and lack of availability of reliable learnersourcing systems), which have limited its adoption. This paper presents a framework that considers the existing learnersourcing literature, the latest insights from the learning sciences and advances in AI to offer promising future directions for developing learnersourcing systems. The framework is designed around important questions and human-AI partnerships relating to four key aspects: (1) creating novel content, (2) evaluating the quality of the created content, (3) utilising learnersourced contributions of students and (4) enabling instructors to support students in the learnersourcing process. We then present two comprehensive case studies that illustrate the application of the proposed framework in relation to two existing popular learnersourcing systems.