Assessing artificial intelligence competence in vocational education: validation of the AICo scale and certification-based differences

Purpose This study aimed to validate the six-dimensional artificial intelligence competence (AICo) instrument among vocational teachers in Indonesia and explore differences in perceived AI competence across teacher certification groups. Design/methodology/approach Using a quantitative cross-sectional design, data were collected through convenience sampling from 622 Indonesian vocational teachers, comprising uncertified teachers (UT), certified teachers (CT), and teachers enrolled in the Professional Teacher Education programme (PT). Confirmatory factor analysis (CFA) was conducted to evaluate the higher-order measurement model, Cronbach's alpha and composite reliability were used to assess internal consistency, the Kruskal–Wallis test was employed to compare observed AICo scores across the three groups, and hierarchical regression examined certification-group differences after limited adjustment for age. Findings The findings yielded three main results. First, the CFA supported the satisfactory fit, reliability, convergent validity, and discriminant validity of the six-dimensional AICo model. Second, participants reported a relatively high overall level of perceived AI competence (M = 3.78). Third, UT and PT reported slightly higher observed AICo scores than CT. However, certification status explained only 0.8% of the variance in perceived AICo, indicating very limited explanatory value, while age was not significantly associated with the outcome. Thus, the moderate rank-based effect (e2 = 0.075) reflects a consistent distributional shift rather than meaningful individual-level prediction. The certification-group findings should therefore be interpreted as exploratory rather than as evidence of substantively meaningful or causal effects. Originality/value Future research should combine self-report and performance-based assessment, employ representative samples, and directly examine the individual and institutional factors associated with vocational teachers' AI competence.

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Publication Details

Journal
Asian Education and Development Studies
Published
2026-09-29
DOI
https://doi.org/10.1108/aeds-03-2026-0200
Primary Topic
Digital literacy in education
Type
article
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article

Assessing artificial intelligence competence in vocational education: validation of the AICo scale and certification-based differences

Sutirman Sutirman, Mar’atus Sholikah, Muhammad Irfan, Indra Febrianto et al.
Asian Education and Development Studies
Digital literacy in education
article

Assessing artificial intelligence competence in vocational education: validation of the AICo scale and certification-based differences

Sutirman Sutirman, Mar’atus Sholikah, Muhammad Irfan, Indra Febrianto, Yuliansah Yuliansah, Muhammad Hasan
article en

Abstract

Purpose This study aimed to validate the six-dimensional artificial intelligence competence (AICo) instrument among vocational teachers in Indonesia and explore differences in perceived AI competence across teacher certification groups. Design/methodology/approach Using a quantitative cross-sectional design, data were collected through convenience sampling from 622 Indonesian vocational teachers, comprising uncertified teachers (UT), certified teachers (CT), and teachers enrolled in the Professional Teacher Education programme (PT). Confirmatory factor analysis (CFA) was conducted to evaluate the higher-order measurement model, Cronbach's alpha and composite reliability were used to assess internal consistency, the Kruskal–Wallis test was employed to compare observed AICo scores across the three groups, and hierarchical regression examined certification-group differences after limited adjustment for age. Findings The findings yielded three main results. First, the CFA supported the satisfactory fit, reliability, convergent validity, and discriminant validity of the six-dimensional AICo model. Second, participants reported a relatively high overall level of perceived AI competence (M = 3.78). Third, UT and PT reported slightly higher observed AICo scores than CT. However, certification status explained only 0.8% of the variance in perceived AICo, indicating very limited explanatory value, while age was not significantly associated with the outcome. Thus, the moderate rank-based effect (e2 = 0.075) reflects a consistent distributional shift rather than meaningful individual-level prediction. The certification-group findings should therefore be interpreted as exploratory rather than as evidence of substantively meaningful or causal effects. Originality/value Future research should combine self-report and performance-based assessment, employ representative samples, and directly examine the individual and institutional factors associated with vocational teachers' AI competence.

Asian Education and Development Studies
Yogyakarta State University (ID), State University of Makassar (ID)
Reduced inequalities
Openalex Percentile: Top 4%
Digital literacy in education
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