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CompetiLearn: A Retrieval-Augmented System for Complementing Learnersourcing and AI for Data Science Learners
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Author / Producer
Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Recent advancements in Large Language Model (LLM)-based intelligent support systems like ChatGPT have been trained on a vast amount of data available on the internet, including learner-generated content that is publicly accessible online. While these tools can generate high-quality responses to learners’ queries, they may inadvertently discourage interaction within the online learner
community by providing direct answers instead of fostering discussion among learners in forums. Rather than replacing the learner community, how can AI systems foster its development? In this
paper, we studied a data science learning community, Kaggle, and explored how AI models like Retrieval-Augmented Generation can complement learner sourcing with AI. We designed a Jupyter note-book extension, CompetiLearn, which generates responses to learners’ questions about a particular competition. These responses can be traced back to other learners’ sharing of high-quality analysis notebooks from the Kaggle platform. We propose to evaluate this approach through a comparative study, examining how learners perceive the quality, relevance, and trustworthiness of the gener ated content. Additionally, we will collect feedback on how learners perceive that this approach can help them engage with the broader data science learning community.
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External links
Editor
Book title
Proceedings of Learnersourcing: Student-generated Content @ Scale (Learning @ Scale 2024)
Journal / series
Volume
Pages / Article No.
Publisher
Event
2nd Annual Workshop on Learnersourcing: Student-Generated Content @ Scale (LSGCS2)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Retrieval-augmented model; Competitions; Coding education; iPython notebooks
Organisational unit
09820 - Wang, April Yi / Wang, April Yi