Faculty knowledge of Generative Artificial Intelligence. TPACK-based predictive model for the ethical integration of Generative Artificial Intelligence in Higher Education
Abstract
Generative Artificial Intelligence (GAI) has been rapidly incorporated into education, providing personalisation, automated tutoring and more efficient assessments, but also generating ethical challenges such as bias, privacy and academic integrity, which require informed teacher training. This study sought to validate a predictive model that explains the relationship between the dimensions of the TPACK model adapted to GAI and Technological Ethical Assessment (TEAK). Using a quantitative approach, a 25-item questionnaire was administered to 240 teachers at the National Open and Distance University of Colombia. The analysis with Structural Equation Modelling (PLS-SEM) showed high reliability and validity (Cronbach's Alpha: 0.935–0.976). Teachers presented attitudes ranging from ‘neutral’ to ‘agree’, highlighting Technological Knowledge as the strongest dimension. The results indicate that knowledge of the tool does not guarantee appropriate pedagogical use, making it essential to integrate ethical and didactic aspects into teacher training. The validated model is proposed as a useful guide for promoting ethical and reflective use of IAGen in university education.
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References
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