Orchestrating the TPACK of University Professors,in Order to generate AI Prompts that Improve theInitial Training

Abstract

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This article presents a study focused on the articulation of technological, pedagogical, and content knowledge (TPACK) among university faculty members, with the aim of generating contextualized pedagogical proposals using generative artificial intelligence (AI) tools. Through the analysis of concept maps and interviews with twenty professors from diverse disciplines, key variables in the teaching-learning process were identified, as well as the interactions between different types of TPACK knowledge. The novelty of this study lies in the proposed design of prompts aimed at IAG systems, which allow the generation of contextualized pedagogical proposals. The cross-analysis between types of TPACK knowledge and contextual variables revealed combinations with high training potential. The study proposes new lines of research that integrate the TPACK model and the validation of prompts as training tools. Overall, the extracted variables have been used as AI indicators to improve initial teacher training. However, a solid framework is offered for integrating emerging technologies to improve the pedagogical and digital competence of university faculty members, helping to reduce pedagogical frailty and improve technological integration in educational practice.