Luiz Barboza, Andrew Tawfik, Andrew M. Olney
Using LLMs to dynamically theme datasets for student case studies boosts engagement but can introduce confusing real-world logical errors.
Researchers developed a method using LLMs to transform generic datasets into custom, themed data tailored to specific case studies and student interests. While this thematic personalization successfully improves student engagement and supports open educational resources (OERs), it preserves the original statistical correlations under new labels. This preservation can sometimes lead to confusing or unrealistic data relationships in the newly themed context.
Instructional designers can use LLMs to scale the creation of themed datasets, matching learning materials to diverse student interests. However, designers must carefully audit these AI-generated datasets to ensure the mapped variables make logical sense in their new context. Clear framing should also be provided to students to prevent the misinterpretation of simulated relationships.
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