Optimized but Unowned: How AI-Authored Goals Undermine the Motivation They Are Meant to Drive

TL;DR

Delegating goal-setting to AI models improves goal quality but significantly reduces personal ownership and motivation, especially among individuals with low self-efficacy, impacting follow-through.

Contribution

This study reveals that AI-generated goals, despite being well-formed, undermine psychological ownership and motivation, highlighting the importance of authorship in AI-assisted goal-setting.

Findings

LLM-generated goals scored higher on SMART criteria.

Participants with AI-generated goals reported lower ownership and commitment.

Self-authored goals led to higher follow-through rates.

Abstract

As AI tools become embedded in productivity and self-improvement contexts, a pressing question emerges: what happens when AI does the goal-setting for us? While large language models can generate goals that are objectively well-formed, the motivational consequences of delegating this cognitively and emotionally significant task remain unknown. In a preregistered experiment (N = 470), we compared self-authored goals against LLM-authored goals derived from a personal reflection. Although LLM-generated goals scored higher on SMART criteria (specificity, measurability, achievability, relevance, and time-boundedness; d = 2.26), participants in the LLM condition reported lower psychological ownership (d = 1.38), commitment (d = 1.19), and perceived importance (d = 1.13). At two-week follow-up, 72.8% of self-authored participants had acted on two or more of their goals, compared to 46.6% in the LLM condition. Mediation analyses identified psychological ownership as the mechanism: it mediated the authorship effect on every downstream motivational and behavioral outcome, while objective goal quality did not. Critically, individuals low in trait self-efficacy, those most likely to seek AI assistance, experienced the steepest ownership erosion. These findings reveal a quality-motivation dissociation in AI-assisted goal-setting and identify authorship preservation as a design priority for AI tools deployed in identity-relevant, behavior-dependent tasks.