Development and Validation of the <scp>AI</scp> Learning, Metacognitive, and Epistemic Barriers Scale ( <scp>AIME</scp> ‐B) for Secondary School Principals in Jordan

ABSTRACT Background Rapid adoption of artificial intelligence (AI) in education is reshaping teaching, learning, and administration. Beyond technical skills, AI integration requires cognitive, metacognitive/self‐regulatory, and epistemic preparedness; thus, latent barriers may constrain how secondary school principals interpret, evaluate, and use AI‐generated information in decision‐making, creating a need for a psychometrically sound instrument. Objectives This study aimed (1) to develop and (2) to validate the AI Learning, Metacognitive, and Epistemic Barriers Scale (AIME‐B) for secondary school principals in Jordan, to assess cognitive, metacognitive/self‐regulatory, and epistemic constraints that hinder reflective, context‐appropriate engagement with AI outputs in school administration and decision‐making. Methods Using an exploratory mixed‐methods design, data were collected from 712 secondary school principals in Jordan (2025). The initial psychometric form contained 40 items; following item purification, the final scale retained 38 items loading on six factors. Results and Conclusions Internal consistency was high (α = 0.891–0.958; ω = 0.880–0.958), and within‐model convergent evidence was acceptable (AVE &gt; 0.50; CR &gt; 0.84). The first‐order CFA supported six correlated dimensions. Revision‐stage comparisons using the item‐level confirmatory dataset favoured the correlated six‐factor model over one‐factor, four‐factor, two theoretically plausible five‐factor, second‐order, and bifactor alternatives. The bifactor solution did not support a strong general factor, reinforcing primary interpretation of the six subscales. Gender invariance and bootstrapped EGA provided supplementary evidence regarding score comparability and clustering stability. Overall, the AIME‐B provides initial, context‐specific evidence supporting the internal structure and internal consistency of scores among Jordanian secondary school principals; temporal stability, external validity, and cross‐context generalizability require further study.

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Journal
Journal of Computer Assisted Learning
Published
2026-07-27
DOI
https://doi.org/10.1002/jcal.70301
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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0.00

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article

Development and Validation of the AI Learning, Metacognitive, and Epistemic Barriers Scale ( AIME ‐B) for Secondary School Principals in Jordan

Mohamad Ahmad Saleem Khasawneh, Najwa Ahmad Saleem Khasawneh, Asma Jadallah Khasawneh, Yusra Jadallah Abed Khasawneh et al.
Journal of Computer Assisted Learning
Artificial Intelligence in Healthcare and Education
article

Development and Validation of the AI Learning, Metacognitive, and Epistemic Barriers Scale ( AIME ‐B) for Secondary School Principals in Jordan

Mohamad Ahmad Saleem Khasawneh, Najwa Ahmad Saleem Khasawneh, Asma Jadallah Khasawneh, Yusra Jadallah Abed Khasawneh, Nabil Saleh Sufyan
article en

Abstract

ABSTRACT Background Rapid adoption of artificial intelligence (AI) in education is reshaping teaching, learning, and administration. Beyond technical skills, AI integration requires cognitive, metacognitive/self‐regulatory, and epistemic preparedness; thus, latent barriers may constrain how secondary school principals interpret, evaluate, and use AI‐generated information in decision‐making, creating a need for a psychometrically sound instrument. Objectives This study aimed (1) to develop and (2) to validate the AI Learning, Metacognitive, and Epistemic Barriers Scale (AIME‐B) for secondary school principals in Jordan, to assess cognitive, metacognitive/self‐regulatory, and epistemic constraints that hinder reflective, context‐appropriate engagement with AI outputs in school administration and decision‐making. Methods Using an exploratory mixed‐methods design, data were collected from 712 secondary school principals in Jordan (2025). The initial psychometric form contained 40 items; following item purification, the final scale retained 38 items loading on six factors. Results and Conclusions Internal consistency was high (α = 0.891–0.958; ω = 0.880–0.958), and within‐model convergent evidence was acceptable (AVE &gt; 0.50; CR &gt; 0.84). The first‐order CFA supported six correlated dimensions. Revision‐stage comparisons using the item‐level confirmatory dataset favoured the correlated six‐factor model over one‐factor, four‐factor, two theoretically plausible five‐factor, second‐order, and bifactor alternatives. The bifactor solution did not support a strong general factor, reinforcing primary interpretation of the six subscales. Gender invariance and bootstrapped EGA provided supplementary evidence regarding score comparability and clustering stability. Overall, the AIME‐B provides initial, context‐specific evidence supporting the internal structure and internal consistency of scores among Jordanian secondary school principals; temporal stability, external validity, and cross‐context generalizability require further study.

Journal of Computer Assisted LearningVol. 42(5)
Jadara University (JO), Ajloun National University (JO), King Khalid University (SA), Irbid National University (JO)
Deanship of Scientific Research, King Khalid University, King Khalid University
Gender equality
Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare and Education
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