This paper explores the space of optimizing feedback mechanisms in complex domains, such as data science, by combining two prevailing approaches: Artificial Intelligence (AI) and learnersourcing. Towards addressing the challenges posed by each approach, this work compares traditional learnersourcing with an AI-supported approach. We report on the results of a randomized controlled experiment conducted with 72 Master's level students in a data visualization course, comparing two conditions: students writing hints independently versus revising hints generated by GPT-4. The study aimed to evaluate the quality of learnersourced hints, examine the impact of student performance on hint quality, gauge learner preference for writing hints with or without AI support, and explore the potential of the student-AI collaborative exercise in fostering critical thinking about LLMs. Based on our findings, we provide insights for designing learnersourcing activities leveraging AI support and optimizing students' learning as they interact with LLMs.
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