ArticleFrontiers in psychology2023
Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence.
Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Patient Reactions to Artificial Intelligence-Clinician Discrepancies: Web-Based Randomized Experiment.Journal of medical Internet research · 2025Trial
- Cross-cultural adaptation and psychometric evaluation of the artificial intelligence learning intention scale (AILIS) among medical sciences students: a methodological study.Medical education online · 2026Article
- Psychometric Evaluation of the Artificial Intelligence Perception Scale for Cancer Patients: A Structural Equation Modeling Approach.Journal of evaluation in clinical practice · 2026Article
- Artificial intelligence attitudes in relation to Mediterranean diet adherence and environmental footprints: a cross-sectional study.BMC public health · 2026Article
- Artificial Intelligence Self-Efficacy, Technostress, and Critical Thinking: Evidence from Peruvian University Students.European journal of investigation in health, psychology and education · 2026Article
- Relationship Between Generative AI Use and Life Satisfaction and the Mediating Role of AI Literacy Among Hong Kong Adults: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Development and psychometric analysis of 5-item Artificial Intelligence Attitude (AIAT-5) for students.BMC psychology · 2026Article
- Development and validation of the Oxford Attitudes to Artificial Intelligence in Medicine Scale.JAAD international · 2026Article
- Artificial Intelligence Literacy Levels and Attitudes Toward Artificial Intelligence Technology Among Intensive Care Nurses: A Cross-Sectional Study.Journal of evaluation in clinical practice · 2026Article
- Development and validation of the student readiness and anxiety scale for AI-assisted biology teaching (SRAS-AIBT).Scientific reports · 2026Article
- Attitudes toward artificial intelligence tools in university students: associations with body appreciation and e-healthy diet literacy - implications for digital health education.BMC medical education · 2026Article
- Associations between digital healthy diet literacy, artificial intelligence attitudes, mediterranean diet adherence, and health-related quality of life in adults.BMC public health · 2026Article
- Development and validation of an AI use scale for sport and exercise science students.Scientific reports · 2026Article
- Information leaflets vs artificial intelligence: comparing perceptions of stroke survivors and professionals in a mixed-methods study.European stroke journal · 2026Article
- Validity and Reliability Analysis of the Artificial Intelligence-Digital Life Balance Scale.The Psychiatric quarterly · 2026Article
- Real-World Use of a Mental Health AI Companion: Multiple Methods Study.JMIR formative research · 2026Article
- Generative AI use and advisory performance among agricultural extension agents in Benin.Frontiers in artificial intelligence · 2026Article
- Article
- Student perceptions of artificial intelligence in higher education: a structural analysis at an Italian university.Frontiers in psychology · 2026Article
- Smartwatch and physical education exam stress: the role of exercise self-efficacy and physical self-concept.Frontiers in psychiatry · 2026Article
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Abstract
The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals' beliefs about AI's influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale's structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users' general attitudes toward AI.
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