Quantitative analysis identifies cognition as key to enhancing digital competence and job satisfaction in educators, indicating the need for focused training.
Integrating Artificial Intelligence (AI) into education poses new challenges and opportunities, particularly in the training of university professors, where Teaching Digital Competence (TDC) emerges as a key factor to leverage its potential. The aim of this study was to evaluate a structural model designed to measure TDC in relation to the educational use of AI. A quantitative methodology was applied using a validated questionnaire distributed through Google Forms between March and May 2024. The sample consisted of 368 university professors. The model examined relationships among key dimensions, including cognition, capacity, vision, ethics, perceived threats, ai-powered innovation, and job satisfaction. The results indicate that cognition is the strongest predictor of capacity, which in turn significantly influences vision and ethics. AI-powered innovation presented limited explained variance, while perceived threats from AI negatively affected capacity. Additionally, job satisfaction was mainly influenced by external factors beyond the model. The overall model fit confirmed its reliability in explaining the proposed relationships. This study highlights the critical role of cognitive training in AI for teachers and the importance of designing targeted professional development programs to enhance TDC. Although a generally positive attitude towards AI was identified, perceptions of threats remained low.
No takes yet. Share an insight, caveat, or question.
Almenara et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: