AI-Assisted Goal Setting Improves Goal Progress Through Social Accountability

2026-03-18

AI Summary

This study addresses the challenge of scaling effective career coaching in large populations, where traditional approaches are often hindered by high costs and limited accessibility. Through a pre-registered randomized controlled trial (N=XXX), we compared the impact of a Claude Sonnet–powered large language model AI coach (“Leon”), a structured written reflection questionnaire, and a no-intervention control condition on participants’ progress toward self-set career goals over two weeks. Results indicated that the AI coach significantly enhanced goal progress relative to the control group (d = 0.33, p = .016). Although it did not significantly outperform the questionnaire condition, the AI coach indirectly facilitated goal attainment by strengthening participants’ perceived social accountability (indirect effect = 0.15). These findings highlight simulated social interaction as a key mechanism through which AI coaches can foster accountability, offering a scalable paradigm for career interventions.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: AI for Accessibility
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

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.

Problem

• goal setting
• career coaching
• social accountability
• AI chatbot
• goal progress

Innovation

• AI-assisted goal setting
• social accountability
• large language model
• randomised controlled trial
• goal progress