DiverseClaire: Simulating Students to Improve Introductory Programming Course Materials for All CS1 Learners

TL;DR

DiverseClaire uses large language models to simulate neurodiverse students, revealing that accessible, multi-format course materials improve learning outcomes in introductory programming courses for diverse learners.

Contribution

This study introduces a novel simulation approach using LLMs to model neurodiverse students, emphasizing the importance of Universal Design for Learning in CS1 courses.

Findings

Simulated neurodiverse students struggled with inaccessible lecture slides.

Accessible, multi-format materials improved simulated student performance.

Data supports inclusive pedagogical strategies for diverse learners.

Abstract

Although CS programs are booming, introductory courses like CS1 still adopt a one-size-fits-all formats that can exacerbate cognitive load and discourage learners with autism, ADHD, dyslexia and other neurological conditions. These call for compassionate pedagogies and Universal Design For Learning (UDL) to create learning environments and materials where cognitive diversity is welcomed. To address this, we introduce DiverseClaire a pilot study, which simulates students including neurodiverse profiles using LLMs and diverse personas. By leveraging Bloom's Taxonomy and UDL, DiverseClaire compared UDL-transformed lecture slides with traditional formats. To evaluate DiverseClaire controlled experiments, we used the evaluation metric the average score. The findings revealed that the simulated neurodiverse students struggled with learning due to lecture slides that were in inaccessible formats. These results highlight the need to provide course materials in multiple formats for diverse learner preferences. Data from our pilot study will be made available to assist future CS1 instructors.