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<p>This study adapts and validates the Artificial Intelligence Literacy Scale (AILS) for higher education faculty in Ethiopia. After expert review and a pilot test with 40 participants identified four items with weak factor loadings, the research team revised those items to improve clarity and contextual relevance. The final 12-item English version was administered to 376 faculty members from four public universities. Exploratory factor analysis using principal axis factoring with oblique rotation confirmed a four-factor structure—Awareness, Usage, Evaluation, and Ethics—that explained 72.89% of the total variance. Confirmatory factor analysis showed good model fit (CFI = 0.977; TLI = 0.969; RMSEA = 0.061; SRMR = 0.049). Reliability and validity tests produced strong results: Cronbach’s α ranged from 0.856 to 0.930, composite reliability (CR) from 0.856 to 0.930, and average variance extracted (AVE) from 0.665 to 0.816. Multi-group confirmatory factor analysis supported configural, metric, and scalar invariance across both data collection methods (online and paper-based) and gender, confirming stable measurement properties. The adapted AILS provides a reliable, concise, and contextually sensitive tool for measuring AI literacy among faculty in low-resource higher education environments. The study highlights implications for curriculum design, faculty professional development, and cross-cultural adaptation of AI literacy assessments.</p>

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Adapting and validating an artificial intelligence literacy scale for Ethiopian higher education using structural equation modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月12日

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  • Ermiyas Tsehay Birhanu,
  • Getachew Worku Tefera,
  • Shouket Ahmad Tilwani,
  • Bekalu Terefe,
  • Yimer Gobezie Shiferaw

摘要

This study adapts and validates the Artificial Intelligence Literacy Scale (AILS) for higher education faculty in Ethiopia. After expert review and a pilot test with 40 participants identified four items with weak factor loadings, the research team revised those items to improve clarity and contextual relevance. The final 12-item English version was administered to 376 faculty members from four public universities. Exploratory factor analysis using principal axis factoring with oblique rotation confirmed a four-factor structure—Awareness, Usage, Evaluation, and Ethics—that explained 72.89% of the total variance. Confirmatory factor analysis showed good model fit (CFI = 0.977; TLI = 0.969; RMSEA = 0.061; SRMR = 0.049). Reliability and validity tests produced strong results: Cronbach’s α ranged from 0.856 to 0.930, composite reliability (CR) from 0.856 to 0.930, and average variance extracted (AVE) from 0.665 to 0.816. Multi-group confirmatory factor analysis supported configural, metric, and scalar invariance across both data collection methods (online and paper-based) and gender, confirming stable measurement properties. The adapted AILS provides a reliable, concise, and contextually sensitive tool for measuring AI literacy among faculty in low-resource higher education environments. The study highlights implications for curriculum design, faculty professional development, and cross-cultural adaptation of AI literacy assessments.