| Category | Details |
|---|---|
| Description | The AI Knowledge Scale (Kerstan, Bienefeld, & Grote, 2024) was designed to measure participants’ objective knowledge of general artificial intelligence (AI) principles. Developed for a study examining trust associations with AI versus physicians, the scale assesses how knowledge relates to risk-benefit perceptions and preferences for AI integration in healthcare. Based on a literature review (e.g., Topol, 2019) and a pretest with a Prolific sample, the six-item scale was evaluated through factor analysis, item response theory, and reliability and validity testing. |
| Author | Kerstan, Sophie; Bienefeld, Nadine; Grote, Gudela |
| Purpose | To assess participants’ objective knowledge of AI. |
| Construct | Artificial Intelligence Knowledge |
| Instrument Type | Rating Scale |
| Test Type | Original |
| Test Year | 2024 |
| Affiliation | ETH Zurich, Department of Management, Technology, and Economics |
| Author Identifier | Sophie Kerstan: ORCID, Nadine Bienefeld: ORCID, Gudela Grote: ORCID |
| Sophie Kerstan: [email protected] | |
| Correspondence Address | Sophie Kerstan, ETH Zurich, Department of Management, Technology, and Economics, Work and Organizational Psychology, Weinbergstrasse 56/58, Zurich, Switzerland, 8092 |
| Format | True/False statements where participants indicate confidence (0 = unsure, 1 = slightly unsure, 2 = slightly sure, 3 = sure). Correct responses with high confidence (score of 3) are coded as 1, while incorrect or uncertain responses are coded as 0. |
| Administration Method | Electronic |
| Number of Items | 6 |
| Reliability | Internal Consistency: Molenaar Sijtsma statistic = 0.62, consistent with similar knowledge measures (Bearth et al., 2019). |
| Validity | Item Response Theory: Mokken scale analysis ensured appropriate item scalability (H = 0.34). Construct Validity: Positive correlation between scientific knowledge and AI knowledge supports validity. |
| Factor Analysis | Confirmatory Factor Analysis: Five-factor model showed acceptable fit (χ² = 1008.46, df = 547, p < 0.001, χ²/df = 1.84, CFI = 0.91, TLI = 0.90, RMSEA = 0.04, SRMR = 0.06). All items significantly contributed to their factors. |
| Test Methodology | Test Validity; Construct Validity; Test Reliability; Internal Consistency; Factor Analysis; Confirmatory Factor Analysis; Item Response Theory |
| Classification | Human-Computer Interaction |
| Age Group | Adulthood (18 yrs & older) |
| Population Group | Human; Male; Female |
| Population Details | Location: United States; Respondents: Adult Participants |
| Keywords | Artificial Intelligence; Objective Knowledge; General Principles |
| Index Terms | Artificial Intelligence; Human-Computer Interaction; Knowledge Level; Human-Computer Interaction Measures |
| Files | No file available for download. |
| Reference | Kerstan, S., Bienefeld, N., & Grote, G. (2024). Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare. Risk Analysis, 44(4), 939–957. https://doi.org/10.1111/risa.14216 |
Al Knowledge Scale
Variable: Objective AI knowledge (self-developed)
| Item Number | Item/Stimuli | Correct Response |
| 1 | Deep learning employs artificial neural networks with multiple layers. | True |
| 2 | Al-based outputs are free of biases. | False |
| 3 | FLOPS is a measure of computer performance. | True |
| 4 | Unsupervised machine learning methods make use of training cases with labelled data. | False |
| 5 | The Turing test determines if a human is more intelligent than a machine. | False |
| 6 | At its core, Al always relies on decision rules that are predefined by humans. | False |
Response Options
Two-faceted scale:
-
Correctness of item:
| Option |
| True |
| False |
-
Certainty about answer:
| Response | Numerical Value |
| Unsure | 1 |
| Fairly unsure | 2 |
| Fairly sure | 3 |
| Sure | 4 |
Cite this article
Mohammed looti (2026). AI Knowledge Scale. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/s/ai-knowledge-scale/
Mohammed looti. "AI Knowledge Scale." PSYCHOLOGICAL SCALES, 4 Apr. 2026, https://scales.arabpsychology.com/s/ai-knowledge-scale/.
Mohammed looti. "AI Knowledge Scale." PSYCHOLOGICAL SCALES, 2026. https://scales.arabpsychology.com/s/ai-knowledge-scale/.
Mohammed looti (2026) 'AI Knowledge Scale', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/s/ai-knowledge-scale/.
[1] Mohammed looti, "AI Knowledge Scale," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, April, 2026.
Mohammed looti. AI Knowledge Scale. PSYCHOLOGICAL SCALES. 2026;vol(issue):pages.