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.

翻译：本文探讨了在数据科学等复杂领域中优化反馈机制的途径，通过融合两种主流方法：人工智能（AI）与学习者资源。为应对每种方法所固有的挑战，本研究将传统学习者资源与人工智能支持的方法进行了比较。我们报告了一项在数据可视化课程中对72名硕士生进行的随机对照实验结果，比较了两种条件：学生独立编写提示与修改GPT-4生成的提示。研究旨在评估学习者生成提示的质量，考察学生表现对提示质量的影响，衡量学生对有无AI支持编写提示的偏好，并探索学生-AI协作练习在培养关于大语言模型批判性思维方面的潜力。基于我们的发现，我们为设计利用AI支持的学习者资源活动以及优化学生在与大语言模型交互过程中的学习提供了见解。