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arxiv: 2605.12344 · v2 · submitted 2026-05-12 · 💻 cs.HC

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

Andreas G\"oldi, Lyle Ungar, Roman Rietsche, Sharath Chandra Guntuku, Vivienne Bihe Chi

Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:41 UTC · model grok-4.3

classification 💻 cs.HC
keywords psychological ownershipAI goal settingmotivationLLMhuman-AI interactiongoal pursuitself-efficacySMART criteria
0
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The pith

AI-authored goals reduce psychological ownership and motivation despite superior objective quality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper examines the motivational impact of using AI to create personal goals instead of writing them oneself. Although goals generated by large language models scored much higher on standard quality metrics like being specific and time-bound, people felt less ownership over them, leading to lower commitment and less follow-through two weeks later. The key mechanism was psychological ownership, which explained all the differences in motivation and behavior, unlike the actual quality of the goals. Those with lower self-efficacy saw the biggest negative effects, even though they might turn to AI for help more often. The results point to a trade-off in AI tools for self-improvement where better goals come with less personal drive to achieve them.

Core claim

Participants in the self-authored condition reported higher psychological ownership (d = 1.38), commitment (d = 1.19), and perceived importance (d = 1.13) compared to the LLM-authored condition, despite the latter having higher SMART scores (d = 2.26). At two-week follow-up, 72.8% of self-authored participants had acted on two or more goals versus 46.6% in the LLM condition. Mediation analyses showed psychological ownership as the mechanism mediating the authorship effect on all motivational and behavioral outcomes, while objective goal quality did not mediate these effects.

What carries the argument

Psychological ownership over the goals, which acts as the mediator between goal authorship and subsequent motivation and behavior.

If this is right

  • AI-generated goals may not translate to better real-world pursuit even when they meet higher quality standards.
  • Preserving user authorship should be a priority in designing AI tools for goal-setting and self-improvement.
  • People low in self-efficacy are at higher risk of reduced motivation when using AI for goal creation.
  • Objective goal quality improvements do not automatically lead to increased commitment or action.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This effect could apply to other AI-assisted tasks involving personal identity, such as writing or planning personal projects.
  • AI systems might benefit from features that allow users to feel ownership, like collaborative editing of goals.
  • Longer-term studies could test if the ownership gap persists or if users adapt over time to AI-generated goals.

Load-bearing premise

That self-reported goal pursuit at two weeks accurately measures real behavior without influence from demand characteristics or participants wanting to appear successful.

What would settle it

An experiment that measures actual goal completion through verifiable means, such as app logs or external records, and finds equivalent pursuit rates for AI-authored and self-authored goals.

Figures

Figures reproduced from arXiv: 2605.12344 by Andreas G\"oldi, Lyle Ungar, Roman Rietsche, Sharath Chandra Guntuku, Vivienne Bihe Chi.

Figure 1
Figure 1. Figure 1: Study procedure. After completing a trait self-efficacy measure and a guided narrative reflection on the past [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of goal action counts at two-week follow-up by condition ( [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript reports a preregistered experiment (N=470) comparing self-authored goals to LLM-authored goals generated from participants' personal reflections. LLM goals scored higher on SMART criteria (d=2.26) but produced 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 acted on two or more goals versus 46.6% in the LLM condition. Mediation analyses showed psychological ownership as the mechanism linking authorship to all motivational and behavioral outcomes, while objective goal quality did not mediate; effects were strongest among low self-efficacy participants.

Significance. If the results hold, the work makes a meaningful contribution to HCI and AI-assisted self-improvement by documenting a quality-motivation dissociation: objectively superior AI-generated goals can erode the psychological ownership that drives follow-through. The preregistration, large sample, reported effect sizes, and mediation analysis provide solid empirical grounding. The finding that low self-efficacy individuals (those most likely to use such tools) suffer the largest ownership loss has clear design implications for AI tools in identity-relevant domains.

major comments (1)
  1. [Two-week follow-up measurement] Two-week follow-up: The behavioral outcome (acting on ≥2 goals) is measured exclusively via self-report with no objective logs, third-party confirmation, or pre-registered validation against actual pursuit. This measure is load-bearing for the central mediation claim that ownership transmits the authorship effect to behavioral outcomes; demand characteristics or differential social-desirability bias between conditions could artifactually strengthen the indirect effect while weakening interpretation of the null mediation through SMART quality.
minor comments (1)
  1. [Methods] Methods: Provide more detail on the exact LLM prompting procedure used to convert personal reflections into goals and on the randomization and blinding procedures.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address the concern regarding the two-week follow-up behavioral measure below.

read point-by-point responses

  1. Referee: Two-week follow-up: The behavioral outcome (acting on ≥2 goals) is measured exclusively via self-report with no objective logs, third-party confirmation, or pre-registered validation against actual pursuit. This measure is load-bearing for the central mediation claim that ownership transmits the authorship effect to behavioral outcomes; demand characteristics or differential social-desirability bias between conditions could artifactually strengthen the indirect effect while weakening interpretation of the null mediation through SMART quality.

    Authors: We appreciate this methodological point and agree that self-reported behavioral data introduces risks of demand characteristics and social desirability bias. For highly personal and idiosyncratic goals, however, objective verification (e.g., activity logs or third-party confirmation) is impractical without compromising ecological validity or participant privacy. The two-week follow-up item was explicitly preregistered, and the authorship effect on psychological ownership mediates the outcomes for commitment and perceived importance—measures collected at baseline that are less susceptible to the same post-intervention biases. We will revise the Discussion to add an explicit limitations paragraph addressing reliance on self-report for behavioral outcomes, potential demand characteristics, and the boundary conditions this imposes on interpreting the behavioral mediation. We will also clarify that the quality-motivation dissociation and ownership mediation are robust across the non-behavioral outcomes. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical mediation study with direct participant measurements

full rationale

This paper reports a preregistered experiment (N=470) that collects self-reported and behavioral outcome data from participants, then applies standard mediation analysis to test psychological ownership as a mechanism. No mathematical derivations, equations, fitted parameters renamed as predictions, or self-referential definitions appear. Central claims rest on measured differences (e.g., d=1.38 on ownership) and statistical mediation, not on any reduction to the paper's own inputs by construction. No load-bearing self-citations of uniqueness theorems or ansatzes are present. The analysis chain is self-contained via data collection and conventional statistics.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests entirely on empirical data from the randomized experiment and standard statistical mediation tests rather than new theoretical axioms or invented entities.

axioms (1)
  • standard math Standard assumptions underlying Cohen's d effect sizes and mediation analysis (e.g., independent observations, approximate normality for large N)
    Invoked implicitly when reporting d values and identifying psychological ownership as mediator.

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