研究通过元认知反馈减少用户对大模型的认知外包
Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants
这个研究很实用，教你怎么设计AI助手，避免用户过度依赖它，比如通过反馈机制让用户自己思考，而不是直接用AI替你做所有事。
本研究设计两种干预措施来减少用户对大语言模型的认知外包决策：一是元认知反馈，让用户明确外包对自身技能练习的影响；二是基于努力的奖励机制，激励用户减少对大模型协助的依赖。在包含704名参与者的在线实验中，元认知反馈显著降低了答案外包率（OR=0.47），并提升了测试成绩（OR=1.51），而奖励机制则未产生明显效果。
Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants
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.
