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Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study

Primary research

#1495

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-09 07:16:03
Last seen
2026-08-09 07:16:03

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  • Semantic Scholar2026-08-09 07:15:27
    Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study

    The rapid emergence of generative artificial intelligence (GenAI) necessitates a robust and validated framework for digital competency in higher education. This study developed and validated the artificial intelligence-augmented digital competency framework (AIDCF) to address the gap between general digital literacy and artificial intelligence (AI)-specific operational requirements. The research was conducted at a public technological university in Thailand. Employing a developmental mixed-methods design, the primary instrument, a 24-item questionnaire, was reviewed by nine experts, yielding a mean content validity ratio (CVR) of 0.861, with item-level CVRs ranging from 0.778 to 1.000. Exploratory factor analysis with a sample of 300 participants supported a five-factor structure: (1) AI awareness & ethics, (2) prompting & tool proficiency, (3) critical thinking with AI, (4) AI-integrated learning design, and (5) reflective digital practice. These factors collectively explained 65.12% of the total variance, with factor loadings ranging from 0.682 to 0.884 and an overall Cronbach’s alpha of 0.87. The framework’s pedagogical utility was examined through a five-week instructional intervention utilizing the collecting, reviewing, analyzing, framing, and tailoring (CRAFT) model with 36 graduate students. Results revealed high levels of perceived competency (mean = 4.51, standard deviation = 0.53) and qualitative evidence of metacognitive development. The AIDCF provides a theoretically grounded and empirically validated roadmap for fostering advanced AI literacy in the GenAI era.