Supporting Inquiry-Based Learning and Data Science Education Through AI-enabled Data Personalization

This paper examines the potential of artificial intelligence (AI) and large language models (LLMs) in education, with a focus on personalized data science education to support the design and development of open educational resources (OERs). The paper introduces a novel method for personalizing datasets in case-based learning by leveraging LLMs for thematic data transformation, enhancing student engagement through customized learning materials that dynamically align with the case materials. While acknowledging the pedagogical advantages and increased student engagement, it also addresses the potential for misinterpretation due to preserved correlations in transformed data. By providing practical examples and highlighting the scalability of the proposed data transformation method, this research contributes to the ongoing discussion on leveraging AI to support the design and development of learning resources.