arxiv: 2604.07535 · v1 · submitted 2026-04-08 · 💻 cs.AI

Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction

Baran Shajari , Xiaoran Liu , Kyanna Dagenais , Istvan David This is my paper

Pith reviewed 2026-05-10 17:48 UTC · model grok-4.3

classification 💻 cs.AI

keywords human-AI interactionurgencyself-confidencetrust in AIself-efficacyAI systemsdecision makinguser performance

0 comments

The pith

Urgency in human-AI interactions leaves trust in the AI unchanged but reduces users' self-confidence and self-efficacy.

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

The paper tests whether a sense of urgency when people receive prompts or advice from AI changes how they interact with the system. It finds that urgency has no impact on trust toward the AI yet decreases the user's own sense of competence and belief in their abilities. This matters because repeated drops in personal confidence can produce weaker performance, poorer choices, and errors that make ongoing AI use harder to sustain. The evidence comes from a controlled test with thirty participants who showed higher self-confidence when introduced to the AI setup gradually rather than under immediate pressure.

Core claim

In an experiment with thirty participants, the presence of urgency in human-AI interactions does not affect trust in the AI. It does, however, lower the human user's self-confidence and self-efficacy. Over time the loss of confidence can produce performance loss, suboptimal decisions, human errors, and ultimately unsustainable AI systems. Users felt more confident in their work when eased into the human-AI setup rather than exposed to it without preparation.

What carries the argument

The controlled comparison of urgent versus non-urgent AI prompts, with direct measurements of trust, self-confidence, and self-efficacy.

Load-bearing premise

The self-confidence and self-efficacy changes measured in this thirty-person experiment will hold for other tasks, larger groups, and real-world settings.

What would settle it

A follow-up study that tracks self-confidence across multiple sessions or larger samples and finds no difference between urgent and non-urgent conditions.

Figures

Figures reproduced from arXiv: 2604.07535 by Baran Shajari, Istvan David, Kyanna Dagenais, Xiaoran Liu.

**Figure 1.** Figure 1: Experiment Overview 3.2 Questionnaire development We collect data about demographics and trust attitudes towards AI agents. For the latter, we use Likert-type rating scales to guide participants in expressing their answers. Likert-type scales are psychometric rating scales often employed in questionnaires to measure the attitude of participants towards a specific statement [25]. Here, we measure the atti… view at source ↗

**Figure 3.** Figure 3: Changing trust attitudes of participants [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 5.** Figure 5: Perceived effects of time pressure on self-confidence ■ Strongly disagree ■ Disagree ■ Neutral ■ Agree ■ Strongly agree We observe substantial difference between the groups in terms of self-confidence. Most participants in Group 1, 12 of 15 (80.0%) disagree or strongly disagree that their self-confidence decreased; and no one agrees with this statement. Conversely, in Group 2, only 4 of 15 participants (26… view at source ↗

**Figure 7.** Figure 7: Perceived effects of time pressure on users’ [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

read the original abstract

Studies show that interactions with an AI system fosters trust in human users towards AI. An often overlooked element of such interaction dynamics is the (sense of) urgency when the human user is prompted by an AI agent, e.g., for advice or guidance. In this paper, we show that although the presence of urgency in human-AI interactions does not affect the trust in AI, it may be detrimental to the human user's self-confidence and self-efficacy. In the long run, the loss of confidence may lead to performance loss, suboptimal decisions, human errors, and ultimately, unsustainable AI systems. Our evidence comes from an experiment with 30 human participants. Our results indicate that users may feel more confident in their work when they are eased into the human-AI setup rather than exposed to it without preparation. We elaborate on the implications of this finding for software engineers and decision-makers.

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.

Desk Editor's Note private letter to a colleague

The paper's 30-person experiment finds urgency leaves AI trust intact but lowers user self-confidence, yet the lack of stats and details makes the broader claims hard to trust.

read the letter

The main thing to know is that this paper ran a small experiment with 30 participants and reports that urgency in human-AI interactions does not reduce trust in the AI but does lower users' self-confidence and self-efficacy. They conclude that easing people into the setup helps confidence and that the drop could eventually cause performance issues and make AI systems unsustainable over time. That is the core result they put forward from their new data. What the work actually adds is an original test of urgency as a variable in this setting. Earlier studies have examined trust and efficacy in human-AI pairs, but this one isolates the sense of time pressure as a practical factor that interface designers might face in real use. The direct comparison between urgent and eased-in conditions is a clean way to check the idea. The experiment is limited in ways that matter. With only 30 people and no power analysis, effect sizes, or statistical details visible, it is difficult to judge whether the confidence drop is stable or tied to the specific task and measures they chose. The abstract also skips manipulation checks or controls for individual differences, which leaves room for the result to be narrower than presented. The step from a short-term confidence shift to long-run performance loss, errors, and unsustainable systems is not backed by any data in the study itself. That part stays speculative. This paper would interest people who design or study AI interfaces for high-stakes or time-sensitive work, such as decision support tools. A reader already following human-AI trust research could use it as one data point on how introduction style affects users, but would need the full methods section to assess the numbers. It is coherent enough on its own terms to deserve a serious referee who can check the experiment details and ask for clearer limits on the claims. I would send it to review rather than desk reject, with the expectation that revisions focus on the analysis and scope.

Referee Report

2 major / 1 minor

Summary. The paper claims that urgency in human-AI interactions does not affect users' trust in AI but can detrimentally impact their self-confidence and self-efficacy. Evidence is drawn from a 30-participant experiment showing higher confidence when users are eased into the setup rather than exposed abruptly; the authors argue this may lead to long-term performance loss, suboptimal decisions, errors, and unsustainable AI systems, with implications for software engineers and decision-makers.

Significance. If the core empirical pattern holds after addressing methodological gaps, the result would usefully extend human-AI trust literature by isolating urgency as a factor that selectively erodes self-confidence without altering AI trust. This could inform interface design guidelines for time-sensitive AI deployments (e.g., advisory or decision-support systems) to preserve user agency and reduce downstream risks of over-reliance or disengagement.

major comments (2)

[Experiment section] Experiment section: The manuscript reports results from only 30 participants. No power analysis, a priori sample-size justification, or effect-size reporting (e.g., Cohen's d or partial eta-squared) is provided for the self-confidence/self-efficacy measures. Because the central claim rests on the reliability of the observed detriment under urgency, the absence of these statistics leaves the statistical robustness and generalizability of the finding unclear.
[Results section] Results section: The abstract and summary state that urgency 'does not affect the trust in AI' yet 'may be detrimental' to self-confidence, but no statistical tests, p-values, confidence intervals, or manipulation-check outcomes are referenced. Without these details it is impossible to assess whether the null effect on trust and the negative effect on confidence are distinguishable from noise or from individual-difference confounds.

minor comments (1)

[Abstract and Introduction] The abstract and introduction use 'self-confidence' and 'self-efficacy' interchangeably without clarifying whether distinct validated scales were administered or whether they were treated as a single construct.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback, which highlights important areas for improving the methodological transparency of our work. We address each major comment below and commit to revisions that enhance the statistical reporting without altering the core claims.

read point-by-point responses

Referee: [Experiment section] The manuscript reports results from only 30 participants. No power analysis, a priori sample-size justification, or effect-size reporting (e.g., Cohen's d or partial eta-squared) is provided for the self-confidence/self-efficacy measures. Because the central claim rests on the reliability of the observed detriment under urgency, the absence of these statistics leaves the statistical robustness and generalizability of the finding unclear.

Authors: We agree that N=30 is modest for the central claims and that the original submission lacks sufficient statistical justification and effect-size reporting. The study was designed as an initial exploration of urgency effects in human-AI interaction, drawing on similar small-sample paradigms in the trust literature. In the revised manuscript we will add a post-hoc power analysis for the self-confidence and self-efficacy measures, report effect sizes (Cohen's d and partial eta-squared), include an a priori sample-size rationale based on prior human-AI studies, and expand the limitations section to discuss generalizability. We will also consider whether a follow-up study with larger N is warranted. revision: yes
Referee: [Results section] The abstract and summary state that urgency 'does not affect the trust in AI' yet 'may be detrimental' to self-confidence, but no statistical tests, p-values, confidence intervals, or manipulation-check outcomes are referenced. Without these details it is impossible to assess whether the null effect on trust and the negative effect on confidence are distinguishable from noise or from individual-difference confounds.

Authors: We acknowledge that the abstract and high-level summary do not reference the underlying statistics, which reduces clarity. The full results section contains the relevant analyses (non-significant effects on trust measures with associated p-values and CIs; significant negative effects on self-confidence/self-efficacy with p-values, CIs, and effect sizes), along with manipulation checks for the urgency induction. In the revision we will update the abstract and summary to explicitly cite these statistics, add a dedicated manipulation-check subsection, and discuss potential individual-difference confounds with any available covariates. This will allow readers to evaluate the null and significant findings directly. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical experiment reports observed outcomes with no derivation or equation-based claims

full rationale

The paper is a report of a 30-participant human experiment measuring effects of urgency on AI trust and user self-confidence/self-efficacy. No equations, derivations, first-principles claims, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The central claim rests on direct experimental observations rather than any reduction to inputs by construction. Generalization beyond the specific setup is an empirical assumption (not a circular step), and the reader's assessment of score 1.0 aligns with the absence of any load-bearing definitional or predictive circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of self-report measures for trust and self-confidence plus standard assumptions for comparing experimental conditions; no free parameters, new entities, or ad-hoc axioms are introduced.

axioms (1)

standard math Standard assumptions for statistical comparison of conditions in a user study (e.g., appropriate use of t-tests or ANOVA)
Implicit when interpreting differences between urgency and no-urgency groups

pith-pipeline@v0.9.0 · 5462 in / 1130 out tokens · 35096 ms · 2026-05-10T17:48:37.685197+00:00 · methodology

discussion (0)

Reference graph

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