[论文解读] AI-Assisted Goal Setting Improves Goal Progress Through Social Accountability

Michel Schimpf, Julian Voigt+1 more|arXiv (Cornell University)|Mar 18, 2026

Psychological Treatments and Assessments被引用 0

一句话总结

preregistered three-arm RCT shows an AI career coach increases short-term goal progress versus no-support, mainly by enhancing perceived accountability; over a matched written reflection, AI showed higher accountability but not significantly higher progress.

ABSTRACT

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.

研究动机与目标

• 识别AI辅助目标设定教练是否在工作成年人群体中改善短期目标进展。
• 分离AI教练影响进展的心理机制，聚焦问责感和自我调适（self-concordance）。
• 将AI教练与结构性匹配的书面反思以及无支持对照进行比较，以解析对话效应。
• 检验AI教练是否影响目标具体性以及对工具的感知满意度。

提出的方法

• Three-arm preregistered randomized controlled trial (N=517) with conditions: AI career coach (Leon), matched written questionnaire, and no-support control.
• Goal progress assessed at two-week follow-up using a 9-item, 7-point scale (alpha=.86).
• Mediators (accountability and self-concordance) measured at T1; mediation tested via parallel mediation with bootstrap (5,000 resamples).
• Manipulation checks showed AI was more interactive and perceived reflection higher than control.
• Exploratory analyses included LLM-based goal specificity coding and goal-domain classification.

实验结果

研究问题

• RQ1 AI辅助目标设定是否相比无支持和结构化书面反思能提升短期目标进展？
• RQ2 感知问责是否为AI对比控制及AI对比问卷效应对目标进展的中介因素？
• RQ3 自我调适在AI驱动进展中是否起中介作用，目标具体性与结果之间有什么关系？
• RQ4 AI教练与结构化反思在感知互动性和感知结构化反思方面有何差异？
• RQ5 AI目标是否包含更多非职业内容，AI教练下目标是否更具体？

主要发现

• AI教练在两周时的目标进展高于对照组（平均值3.45 vs 3.02；d=0.33；p=.016）。
• AI教练相比问卷与对照在感知问责方面提升显著（d=0.43和d=0.68；p=.002和p<.001）。
• 相较于问卷，问责在AI对比问卷效应对目标进展的中介作用显著（间接效应=0.15；95% CI [0.04,0.31]）。
• 自我调适在各组间无显著差异，且未中介AI效应。
• 探索性分析表明AI目标比其他条件更具体，且AI呈现更多非职业目标（非职业内容在AI为40.3%，问卷为13.2%，对照为10.6%）。
• NPS（用户满意度）在T1显著高于两组对照条件（d=0.86对照组；d=0.52问卷组；p<.001）。