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As an increasing number of students move to online learning platforms thatdeliver personalized learning experiences, there is a great need for theproduction of high-quality educational content. Large language models (LLMs)appear to offer a promising solution to the rapid creation of learningmaterials at scale, reducing the burden on instructors. In this study, weinvestigated the potential for LLMs to produce learning resources in anintroductory programming context, by comparing the quality of the resourcesgenerated by an LLM with those created by students as part of a learnersourcingactivity. Using a blind evaluation, students rated the correctness andhelpfulness of resources generated by AI and their peers, after both wereinitially provided with identical exemplars. Our results show that the qualityof AI-generated resources, as perceived by students, is equivalent to thequality of resources generated by their peers. This suggests that AI-generatedresources may serve as viable supplementary material in certain contexts.Resources generated by LLMs tend to closely mirror the given exemplars, whereasstudent-generated resources exhibit greater variety in terms of content lengthand specific syntax features used. The study highlights the need for furtherresearch exploring different types of learning resources and a broader range ofsubject areas, and understanding the long-term impact of AI-generated resourceson learning outcomes.

Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources