发表机构
LMU Munich; Munich Center for Machine Learning（慕尼黑大学; 慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对AI认知卸载导致的技能退化问题，提出元认知反馈和努力奖励两种干预，实验表明元认知反馈显著减少卸载并提升测试表现，而奖励无效。
AI 中文摘要
对AI的认知卸载会减少技能练习的机会，从而带来技能退化的风险。然而，如何在不限制AI使用的前提下防止技能退化仍不清楚。在此，我们设计了两种干预措施来减少卸载决策：（1）元认知反馈，使用户明确卸载的后果；（2）基于努力程度的奖励，激励用户减少对LLM助手的依赖。我们在一项预注册的在线实验（N=704）中测试了这两种干预措施，采用2×2设计并设置无AI对照组。任务是与一个基于LLM的助手练习分数算术，该助手仅在明确请求时提供解决方案，随后进行无辅助测试。元认知反馈减少了答案卸载（OR=0.47）并提高了测试表现（OR=1.51）。我们没有发现奖励对任一结果有影响的证据。我们的结果将元认知反馈确定为减少认知卸载的一种有前景的设计选择。
英文摘要
Cognitive offloading to AI can reduce opportunities to practice skills, creating risks of deskilling. However, it remains unclear how to prevent deskilling without restricting access to AI. Here, we design two interventions to reduce offloading decisions: (1) metacognitive feedback that makes the implications of offloading for users explicit, and (2) an effort-based reward that incentivizes less extensive LLM assistance. We test both in a preregistered online experiment ($N = 704$) with a 2$\times$2 design and a no-AI control. The task was to practice fraction arithmetic with an LLM-based assistant that provided solutions only on explicit request, followed by an unaided test. Metacognitive feedback reduced answer offloading (OR $= 0.47$) and improved test performance (OR $= 1.51$). We found no evidence that the reward affected either outcome. Our results identify metacognitive feedback as a promising design choice to reduce cognitive offloading.
