TECH-DQ Pathway: Modeling Digital Literacy of Industrial Technology Students through Digital Quotient, AI Competence, and Technology Attitudes
Year: 2026
Type: conference-paper
Study design: Observational Study
Abstract: The increasing integration of artificial intelligence (AI) in higher education has heightened the importance of digital literacy as a core graduate competency. This study examined the predictive roles of digital quotient, AI competence, and attitudes toward technology in explaining the digital literacy of Industrial Technology students. Using a quantitative, cross-sectional design, data were collected from 345 undergraduate students enrolled in Industrial Technology programs in two state universities in the Philippines. A validated questionnaire measured digital quotient (digital citizenship, digital creativity, digital safety, and digital emotional intelligence), AI competence (conceptual knowledge, tool use, critical evaluation, and ethical use), technology attitudes (perceived usefulness, perceived ease of use, and technology self-efficacy), and digital literacy (information and data literacy, communication and collaboration, digital content creation, safety, and problem solving). Data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicated strong reliability and validity of the measurement model. Structural model analysis revealed that AI competence (β = 0.38), digital quotient (β = 0.31), and technology attitudes (β = 0.27) significantly and positively predicted digital literacy, explaining 63% of the variance. AI competence emerged as the strongest predictor. The findings suggest that digital literacy development is shaped by an interplay of multidimensional digital competencies, emerging AI capabilities, and positive technology dispositions. Integrating AI literacy, digital citizenship, and attitude-enhancing strategies into Industrial Technology curricula may strengthen students’ digital and AI readiness.
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7204567200