[論文レビュー] 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被引用数 0

ひとこと要約

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.

研究の動機と目的

• Identify whether an AI-assisted goal-setting coach improves short-term goal progress in working adults.
• Isolate the psychological mechanisms by which AI coaching affects progress, focusing on accountability and self-concordance.
• Compare AI coaching to a structurally matched written reflection and a no-support control to parse conversational effects.
• Examine whether AI coaching influences goal specificity and perceived satisfaction with the tool.

提案手法

• 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.

実験結果

リサーチクエスチョン

• RQ1 AI支援の目標設定が、ノーサポート対照および構造化された書面反映と比較して短期的な目標進捗を改善するか？
• RQ2 AI対照およびAI対照＋質問票の効果に対する認知的アカウンタビリティが媒介要因となるか？
• RQ3 自己一貫性はAIによる進捗の媒介要因となるか、また目標の特異性は結果とどのように関係するか？
• RQ4 AIコーチングと構造化された反映は、相互作用性の認識と構造化反映の認識に差があるか？
• RQ5 AIの目標はキャリア以外の内容を含む割合が多く、AIコーチング下で目標はより具体的か？

主な発見

• AIコーチングは2週間後の目標進捗を対照より高く示した（平均3.45対3.02；d=0.33；p=.016）。
• AIコーチングは、質問票および対照の両方と比較して認知的アカウンタビリティを高めた（d=0.43およびd=0.68、p=.002およびp<.001）。
• 質問票に対するアカウンタビリティは、AI対質問票効果の媒介要因となった（間接効果＝0.15；95%CI [0.04,0.31]）。
• 自己一貫性は条件間で差がなく、AI効果を媒介しなかった。
• 探索的分析では、AIの目標は他条件より特異性が高く、非キャリア目標が多く浮上した（AIは非キャリア内容が40.3%、質問票が13.2%、対照が10.6%）。
• NPS（ユーザー満足度）は、T1でAIが他の比較条件より著しく高かった（d=0.86対対照、d=0.52対質問票、p<.001）。