As an increasing number of students move to online learning platforms that deliver personalized learning experiences, there is a great need for the production of high-quality educational content. Large language models (LLMs) appear to offer a promising solution to the rapid creation of learning materials at scale, reducing the burden on instructors. In this study, we investigated the potential for LLMs to produce learning resources in an introductory programming context, by comparing the quality of the resources generated by an LLM with those created by students as part of a learnersourcing activity. Using a blind evaluation, students rated the correctness and helpfulness of resources generated by AI and their peers, after both were initially provided with identical exemplars. Our results show that the quality of AI-generated resources, as perceived by students, is equivalent to the quality of resources generated by their peers. This suggests that AI-generated resources may serve as viable supplementary material in certain contexts. Resources generated by LLMs tend to closely mirror the given exemplars, whereas student-generated resources exhibit greater variety in terms of content length and specific syntax features used. The study highlights the need for further research exploring different types of learning resources and a broader range of subject areas, and understanding the long-term impact of AI-generated resources on learning outcomes.

翻译：随着越来越多学生转向提供个性化学习体验的在线学习平台，对高质量教育内容的生产需求日益增长。大语言模型（LLMs）似乎为大规模快速创建学习材料提供了有前景的解决方案，从而减轻教育者的负担。本研究通过比较LLM生成的学习资源与学生作为学习者源活动（learnersourcing）所创建资源的质量，探讨了LLM在入门级编程教学情境中生成学习资源的潜力。在盲评实验中，学生分别对AI生成的资源和同伴生成的资源在正确性与有用性方面进行评分，而两种资源最初均基于相同的示例模板。结果表明，学生感知的AI生成资源质量与其同伴生成的资源质量相当，这提示AI生成资源在特定情境下可作为可行的补充材料。LLM生成的资源倾向于紧密贴近给定示例，而学生生成的资源在内容长度及具体语法特征的使用上表现出更大多样性。本研究表明，需进一步探索不同类型的学习资源、更广泛的学科领域，并理解AI生成资源对学习成效的长期影响。