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力和自我提升场景，一个紧迫的问题浮现：当AI替代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合理的目标，但将这一兼具认知与情感负荷的任务委托给AI的动机后果仍属未知。在一项预注册实验（N=470）中，我们比较了自主撰写目标与基于个人反思由大语言模型生成的目标。尽管AI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AI组参与者的心理所有权（d=1.38）、承诺度（d=1.19）和感知重要性（d=1.13）均显著降低。两周后追踪显示，自主撰写组72.8%的参与者完成了两个以上目标，而AI组仅为46.6%。中介分析揭示心理所有权的作用机制：该变量中介了自主撰写效应对所有下游动机与行为结果的影响，而目标客观质量未呈现中介效应。关键的是，低特质自我效能感个体——最可能寻求AI辅助的群体——经历了最严重的所有权侵蚀。这些发现揭示了AI辅助目标设定中的质量-动机分离现象，并将作者权保留确立为面向身份关联性行为依赖任务的AI工具的设计优先级。