  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.
