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Background <p>Artificial intelligence (AI), including applications such as radiographic image analysis, caries detection, and treatment planning, is increasingly integrated into dental diagnostics, education, and clinical workflows. However, validated instruments to evaluate dental professionals’ preparedness for AI remain limited, particularly those capturing both readiness and acceptance in clinical contexts. This study developed and psychometrically validated the Dental Artificial Intelligence Readiness and Acceptance Instrument (DAI-RAI) to assess readiness and acceptance toward AI adoption in dentistry.</p> Methods <p>Items were constructed based on the Technology Readiness Index (TRI) and Technology Acceptance Model (TAM), adapted to the dental AI context, and refined through expert review and pilot testing. The final validation sample included 941 dental professionals. Exploratory factor analysis (EFA) using Maximum Likelihood with Varimax rotation examined factor structure, followed by confirmatory factor analysis (CFA) using AMOS and R. Internal consistency and convergent validity were evaluated using Cronbach’s alpha (α), Composite Reliability (CR), and Average Variance Extracted (AVE).</p> Results <p>Sampling adequacy was confirmed using the Kaiser–Meyer–Olkin (KMO) measure, which was excellent (KMO = 0.95; Bartlett’s χ²= 6640.32, <i>p</i> &lt; 0.001). EFA supported a two-factor solution accounting for 61.8% of the variance, and one low-loading item (loading = 0.37) was removed based on the predefined threshold of ≥ 0.40. CFA indicated an acceptable overall fit (CFI = 0.915; TLI = 0.900; SRMR = 0.047), although RMSEA (0.108) suggested moderate model misfit. The final validation was conducted on a large sample of dental professionals (<i>N</i> = 941). The final instrument, comprising two modified constructs, AI-Technology Readiness (AI-TR) and AI-Technology Acceptance (AI-TA), consisted of 15 items (AI-TR = 8, AI-TA = 7). The DAI-RAI demonstrated excellent reliability and convergent validity (AI-TR: α = 0.92, AVE = 0.621; AI-TA: α = 0.94, AVE = 0.659). Partial measurement invariance was established across professional roles.</p> Conclusion <p>The DAI-RAI is a concise, reliable, and theory-grounded measure that evaluates dental professionals’ AI readiness and acceptance. Its validated structure supports its application in educational planning, workforce development, and the ethical implementation of AI in dental care.</p>

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Development and validation of the Dental Artificial Intelligence Readiness and Acceptance Instrument (DAI-RAI) for dental professionals

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Narmin Helal,
  • Mohammed Ghazi Aljohani,
  • Ahmed Ghazi Aljohani,
  • Osama Adel Basri,
  • Ola B. Al-Batayneh,
  • Bahn Agha,
  • Maryam Quritum,
  • Mohanid Almozughi,
  • Mohammad Zeinalddin,
  • Nader Abdulhameed,
  • Hanaa Mohammed Alhalki,
  • Heba Jafar Sabbagh

摘要

Background

Artificial intelligence (AI), including applications such as radiographic image analysis, caries detection, and treatment planning, is increasingly integrated into dental diagnostics, education, and clinical workflows. However, validated instruments to evaluate dental professionals’ preparedness for AI remain limited, particularly those capturing both readiness and acceptance in clinical contexts. This study developed and psychometrically validated the Dental Artificial Intelligence Readiness and Acceptance Instrument (DAI-RAI) to assess readiness and acceptance toward AI adoption in dentistry.

Methods

Items were constructed based on the Technology Readiness Index (TRI) and Technology Acceptance Model (TAM), adapted to the dental AI context, and refined through expert review and pilot testing. The final validation sample included 941 dental professionals. Exploratory factor analysis (EFA) using Maximum Likelihood with Varimax rotation examined factor structure, followed by confirmatory factor analysis (CFA) using AMOS and R. Internal consistency and convergent validity were evaluated using Cronbach’s alpha (α), Composite Reliability (CR), and Average Variance Extracted (AVE).

Results

Sampling adequacy was confirmed using the Kaiser–Meyer–Olkin (KMO) measure, which was excellent (KMO = 0.95; Bartlett’s χ²= 6640.32, p < 0.001). EFA supported a two-factor solution accounting for 61.8% of the variance, and one low-loading item (loading = 0.37) was removed based on the predefined threshold of ≥ 0.40. CFA indicated an acceptable overall fit (CFI = 0.915; TLI = 0.900; SRMR = 0.047), although RMSEA (0.108) suggested moderate model misfit. The final validation was conducted on a large sample of dental professionals (N = 941). The final instrument, comprising two modified constructs, AI-Technology Readiness (AI-TR) and AI-Technology Acceptance (AI-TA), consisted of 15 items (AI-TR = 8, AI-TA = 7). The DAI-RAI demonstrated excellent reliability and convergent validity (AI-TR: α = 0.92, AVE = 0.621; AI-TA: α = 0.94, AVE = 0.659). Partial measurement invariance was established across professional roles.

Conclusion

The DAI-RAI is a concise, reliable, and theory-grounded measure that evaluates dental professionals’ AI readiness and acceptance. Its validated structure supports its application in educational planning, workforce development, and the ethical implementation of AI in dental care.