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

2026-05-12

AI Summary

This study investigates the motivational consequences of AI-assisted goal setting by comparing self-generated goals with those produced by large language models (LLMs) based on users’ personal reflections. Despite LLM-generated goals adhering more closely to SMART criteria and exhibiting superior structural quality, they significantly reduce users’ psychological ownership, commitment, and subsequent behavioral engagement. Through a preregistered experiment, the research uncovers a decoupling between goal quality and motivation in AI-supported goal setting, identifying psychological ownership as a critical mediating mechanism. This detrimental effect is particularly pronounced among individuals with low self-efficacy, suggesting that the perceived origin of a goal—whether self-authored or algorithmically generated—plays a pivotal role in sustaining motivation and action.

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.

Problem

• AI-authored goals
• psychological ownership
• motivation
• goal-setting
• self-efficacy

Innovation

• AI-authored goals
• psychological ownership
• goal-setting
• motivation
• self-efficacy