As AI tools become embedded in productivity and self-improvement contexts, a pressing question emerges: what happens when AI does the goal-setting for us? While large language models can generate goals that are objectively well-formed, the motivational consequences of delegating this cognitively and emotionally significant task remain unknown. In a preregistered experiment (N = 470), we compared self-authored goals against LLM-authored goals derived from a personal reflection. Although LLM-generated goals scored higher on SMART criteria (specificity, measurability, achievability, relevance, and time-boundedness; d = 2.26), participants in the LLM condition reported lower psychological ownership (d = 1.38), commitment (d = 1.19), and perceived importance (d = 1.13). At two-week follow-up, 72.8% of self-authored participants had acted on two or more of their goals, compared to 46.6% in the LLM condition. Mediation analyses identified psychological ownership as the mechanism: it mediated the authorship effect on every downstream motivational and behavioral outcome, while objective goal quality did not. Critically, individuals low in trait self-efficacy, those most likely to seek AI assistance, experienced the steepest ownership erosion. These findings reveal a quality-motivation dissociation in AI-assisted goal-setting and identify authorship preservation as a design priority for AI tools deployed in identity-relevant, behavior-dependent tasks.

翻译：随着人工智能工具嵌入生产与自我提升场景，一个紧迫问题浮现：当AI替我们设定目标时会发生什么？尽管大语言模型能生成客观上结构良好的目标，但将这一兼具认知与情感重要性的任务委托给AI所产生的动机后果仍属未知。在一项预先登记的实验（N=470）中，我们比较了自我撰写目标与基于个人反思由LLM生成的目标。虽然LLM生成目标在SMART标准（具体性、可衡量性、可实现性、相关性和时限性；d=2.26）上得分更高，但LLM组参与者报告的心理所有权（d=1.38）、目标承诺（d=1.19）和感知重要性（d=1.13）均更低。在两周后的追踪调查中，72.8%的自我撰写目标参与者已对两个或以上目标采取行动，而LLM组该比例仅为46.6%。中介分析揭示心理所有权是核心机制：它中介了作者身份对每个下游动机与行为结果的影响，而客观目标质量未起中介作用。关键的是，特质自我效能感较低的个体——那些最可能寻求AI协助的人群——经历了最严重的所有权侵蚀。这些发现揭示了AI辅助目标设定中质量与动机的分离，并将作者身份保留确立为部署在身份相关、行为依赖任务中的AI工具的设计优先级。