Development and Preliminary Validation of a Questionnaire Assessing Artificial Intelligence Education Readiness and Integration among Medical Imaging Stakeholders in South-East Nigeria

Background: Artificial intelligence (AI) is increasingly influencing healthcare delivery, clinical decision-making, and medical imaging practice, creating a need for appropriate AI-related competencies within undergraduate radiography education. In resource-constrained settings, curriculum capacity, infrastructure, faculty expertise, and digital access may influence how AI education can be integrated. Validated instruments that capture both stakeholders’ perceptions and contextual barriers remain limited.
Objective: To develop and preliminarily evaluate a context-sensitive questionnaire assessing radiography students’ perceptions of AI education readiness and integration in medical imaging in South-East Nigeria.
Methods: A cross-sectional pilot study was conducted among 39 undergraduate radiography students from three accredited universities in South-East Nigeria. The 56-item questionnaire was developed from a critical literature review, expert consultation, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), and designed to evaluate stakeholders across radiography education, with this pilot psychometric evaluation purposely restricted to student cohort. Five subject-matter experts assessed item relevance and clarity using a four-point ordinal scale. Content validity was quantified using the Item Content Validity Index (I-CVI) and Scale Content Validity Index/Average (S-CVI/Ave). Preliminary construct validity was examined separately by construct using principal component analysis with Varimax rotation, while internal consistency was assessed using Cronbach’s alpha.
Results: The overall S-CVI/Ave was 0.89. Fifty-two of 56 items had I-CVI values of at least 0.80; four items were rephrased after expert review. KMO values ranged from 0.626 to 0.849, and Bartlett’s tests were statistically significant (p < 0.001). Most evaluated constructs produced a single component, whereas Implications and Concerns produced three components explaining 72.66% of cumulative variance. Cronbach’s alpha ranged from 0.764 to 0.928.
Conclusion: The questionnaire demonstrated strong preliminary content validity, acceptable sampling adequacy for exploratory analysis, and good-to-excellent internal consistency. It provides a context-sensitive instrument for assessing student-reported AI education perceptions, readiness, competencies, and structural barriers. Its factor structure and broader measurement properties require confirmation in a larger independent sample before high-stakes use.

Artificial Intelligence; Medical Imaging; Radiography Education; Questionnaire Development; Content Validity; Exploratory Factor Analysis; Psychometrics; Utaut; Nigeria

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