Helping people identify and pursue personally meaningful career goals at scale remains a key challenge in applied psychology. Career coaching can improve goal quality and attainment, but its cost and limited availability restrict access. Large language model (LLM)-based chatbots offer a scalable alternative, yet the psychological mechanisms by which they might support goal pursuit remain untested. Here we report a preregistered three-arm randomised controlled trial (N = 517) comparing an AI career coach ("Leon," powered by Claude Sonnet), a matched structured written questionnaire covering closely matched reflective topics, and a no-support control on goal progress at a two-week follow-up. The AI chatbot produced significantly higher goal progress than the control (d = 0.33, p = .016). Compared with the written-reflection condition, the AI did not significantly improve overall goal progress, but it increased perceived social accountability. In the preregistered mediation model, perceived accountability mediated the AI-over-questionnaire effect on goal progress (indirect effect = 0.15, 95% CI [0.04, 0.31]), whereas self-concordance did not. These findings suggest that AI-assisted goal setting can improve short-term goal progress, and that its clearest added value over structured self-reflection lies in increasing felt accountability.

翻译：帮助人们大规模识别并追求个人有意义的职业目标仍是应用心理学中的关键挑战。职业辅导能提升目标质量和达成率，但其高昂成本和有限的可及性限制了应用范围。基于大语言模型的聊天机器人提供了一种可扩展的替代方案，但其支持目标追求的心理机制尚未得到检验。本文报告了一项预先注册的三臂随机对照试验（N=517），将AI职业教练（由Claude Sonnet驱动的"Leon"）、匹配的结构化书面问卷（涵盖密切相关的反思主题）以及无支持对照组进行了比较，评估两周随访时的目标进展。AI聊天机器人产生的目标进展显著高于对照组（效应量d=0.33，p=.016）。与书面反思条件相比，AI虽未显著提升整体目标进展，但增加了感知到的社会责任感。在预先注册的中介模型中，感知责任感中介了AI相较于问卷对目标进展的效应（间接效应=0.15，95% CI [0.04, 0.31]），而自我一致性未呈现中介作用。这些发现表明，AI辅助目标设定能改善短期目标进展，其相较于结构化自我反思最明确的附加价值在于增强个体的责任感感知。