AI-Assisted Goal Setting Improves Goal Progress Through Social Accountability

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.

Figures (6)

• Figure 1: Screenshots of the study application across the three conditions. G1 (left): the final goal-entry screen, where participants typed their goals directly. G2 (centre): an example structured reflection prompt ("Priorities & Impact"), one of five written questions completed before goal entry. G3 (right): a mid-conversation exchange with "Leon," the AI career coach, probing the participant's work situation before collaborative goal formulation.
• Figure 2: Primary outcome and main mediator by condition. Panel A shows goal progress at Time 2; Panel B shows perceived accountability at Time 1. Bars show condition means; error bars are 95% confidence intervals. Key preregistered contrasts are annotated. $n$ per condition is shown within each panel.
• Figure 3: Preregistered two-mediator parallel mediation model for the AI Chatbot versus Questionnaire contrast. Green paths are statistically significant (95% CI excludes zero, 5,000 bootstraps); grey dashed paths are non-significant. Path coefficients shown are unstandardised. The indirect effect through Accountability ($ab = 0.15$, 95% CI $[0.04, 0.31]$) indicates mediation of the AI-versus-Questionnaire contrast; the indirect effect through Self-Concordance was negligible ($ab = -0.002$, 95% CI $[-0.06, 0.04]$, ns).
• Figure A1: Participant flow. $N = 517$ randomised; 89 excluded at Time 1 (84 did not complete the session, 5 failed the attention check), leaving $n = 428$ who completed Time 1. The high T1 dropout in the Questionnaire condition ($n = 53$, all non-completions) reflects the platform-enforced 20-minute minimum engagement period. At Time 2, 93 did not return and 12 failed the attention check, yielding an analytic sample of $n = 323$ (62.5% of randomised).
• Figure A2: Exploratory parallel mediation model for the AI Chatbot versus Questionnaire contrast. Green paths: accountability; orange paths: goal specificity; grey dashed: self-concordance. Indirect effects: accountability $ab = 0.14$, 95% CI $[0.03, 0.29]$; goal specificity $ab = 0.27$, 95% CI $[0.02, 0.52]$. All based on 5,000 bootstraps, $n = 211$.