用AI模拟神经多样性学生，优化编程课教学材料
DiverseClaire: Simulating Students to Improve Introductory Programming Course Materials for All CS1 Learners
- 用大模型模拟不同学习者角色，测试教学材料适配性
- 神经多样性学生在传统课件下平均得分显著偏低
- 结果支持多格式教学，适合所有学习风格的课程设计
尽管计算机科学课程数量激增，入门课程如CS1仍普遍采用统一教学模式，可能加重认知负荷，对自闭症、注意力缺陷、阅读障碍等神经多样性学习者不利。为此，我们提出DiverseClaire，一项基于大模型与多样化角色的试点研究，通过布卢姆分类学和通用学习设计（UDL）框架，对比了传统课件与经过UDL改造的课件。通过控制实验评估，采用平均分作为指标，结果显示，模拟的神经多样性学生在非适配格式的课件中学习表现显著更差。这表明需为不同学习偏好提供多格式教学材料。本研究数据将公开，供未来CS1教师使用。
原文摘要 · Abstract (English)
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.
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