ArticleFrontiers in artificial intelligence2025
Measuring trust in artificial intelligence: validation of an established scale and its short form.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trust and interaction design in AI-enabled systems: a systematic literature review.Frontiers in psychology · 2026Pooled it
- First-in-human evaluation of real-time pixel-level AI-assisted anatomical segmentation in neurosurgery: pituitary surgery as an exemplar.Pituitary · 2026Article
- Generative AI Engagement and Perceived Nutrition-Oriented Feeding Practices Among Urban Indonesian Mothers: Mixed Methods Study.JMIR pediatrics and parenting · 2026Article
- Trust in AI: scale development and validation for online distance learners.BMC psychology · 2026Article
- Conditional effects of source expertise and pre-existing attitudes on objective knowledge in AI-assisted health information verification.Scientific reports · 2026Article
- Determinants of Trust in Artificial Intelligence (AI) for Health-Related Decision-Making Among Adults in Saudi Arabia: A Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2026Article
- Development, Network Analysis, and Validation of the Ethical-Spiritual Algorithmic Trust Calibration Scale (ES-ATCS): Exploring Teachers' Ethical and Spiritual Trust in AI Integration Within Jordanian Secondary Education.Journal of religion and health · 2026Article
- Modeling AI trust and AI self-efficacy in AI-assisted EFL classrooms: the role of basic psychological needs in classroom engagement.Frontiers in psychology · 2026Article
- The context of AI transformation: perceptions of AI ethicality and their impact on trust and use intentions.Frontiers in artificial intelligence · 2026Article
- Construction and validation of the Emotional Trust in Artificial Intelligence Scale (CEIA).Frontiers in psychology · 2026Article
- From Synergy to Strain: Exploring the Psychological Mechanisms Linking Employee-AI Collaboration and Knowledge Hiding.Behavioral sciences (Basel, Switzerland) · 2025Article
- Development and Validation of the Artificial Intelligence in Mental Health Scale: Application for AI Mental Health Chatbots.Healthcare (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An understanding of the nature and function of human trust in artificial intelligence (AI) is fundamental to the safe and effective integration of these technologies into organizational settings. The Trust in Automation Scale is a commonly used self-report measure of trust in automated systems; however, it has not yet been subjected to comprehensive psychometric validation. Across two studies, we tested the capacity of the scale to effectively measure trust across a range of AI applications. Results indicate that the Trust in Automation Scale is a valid and reliable measure of human trust in AI; however, with 12 items, it is often impractical for contexts requiring frequent and minimally disruptive measurements. To address this limitation, we developed and validated a three-item version of the TIAS, the Short Trust in Automation Scale (S-TIAS). In two further studies, we tested the sensitivity of the S-TIAS to manipulations of the trustworthiness of an AI system, as well as the convergent validity of the scale and its capacity to predict intentions to rely on AI-generated recommendations. In both studies, the S-TIAS also demonstrated convergent validity and significantly predicted intentions to rely on the AI system in patterns similar to the TIAS. This suggests that the S-TIAS is a practical and valid alternative for measuring trust in automation and AI for the purposes of identifying antecedent factors of trust and predicting trust outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.