Rethinking digital competence for pre-service teachers in the GenAI era: a conceptual framework for pedagogical GenAI judgment
TL;DR
A digital-GenAI competence framework grounded on pedagogical GenAI judgment for pre-service teachers (PST) named DigiGen-PST 2030 is presented, which describes four layers of competence—namely foundational digital competence, pedagogical transformation, pedagogical GenAI judgment, and future-skills orientation.
Abstract
Abstract Generative artificial intelligence (GenAI) tools are disrupting how pre-service teachers plan lessons, produce feedback, assess learning, and evaluate professional practice. Current digital competence frameworks offer valuable starting points but lack specificity around how pre-service teachers should exercise judgment over AI-supported decisions. Through comparison of selected digital competence, teacher technology, AI competence, and future-skills frameworks, this conceptual article presents a digital-GenAI competence framework grounded on pedagogical GenAI judgment for pre-service teachers (PST). Updating our initial analysis, pedagogical GenAI judgment is identified as the key construct—a competency that requires teachers to decide when and how to use GenAI, frame pedagogically aligned prompts, evaluate/revise AI-generated output, contextualize the output for students and learning objectives, disclose AI use transparently, and take responsibility for final decisions. Named DigiGen-PST 2030, the framework describes four layers of competence—namely foundational digital competence, pedagogical transformation, pedagogical GenAI judgment, and future-skills orientation—and maps related constructs across TPACK, DigCompEdu, the ISTE Standards for Educators, UNESCO’s AI Competency Framework for Teachers, and OECD education guidance. We offer propositions and suggestions for teacher education programs, teacher educators, and future research validation.