AI Competence Frameworks for STEAM Educators: A Knowledge Codification Perspective
TL;DR
This paper examines how eight EU and international competence frameworks codify AI-related knowledge for STEAM educators, using a dual analytical lens comprising AI-TPACK and STEAMCompEdu and proposes three emerging theoretical extensions to AI-TPACK to address the identified lacunae.
Abstract
Competence frameworks function as structured knowledge codification systems: authoritative, domain-wide representations of professional knowledge that define what counts as competence within a field and how it should be organised. As such, they constitute a form of professional knowledge encyclopaedia, structured to comprehensively map an emerging or established knowledge domain. This paper examines how eight EU and international competence frameworks codify AI-related knowledge for STEAM educators, using a dual analytical lens comprising AI-TPACK and STEAMCompEdu. Read through this dual lens, the cross-framework analysis identifies four knowledge dimensions insufficiently addressed in the existing landscape: interdisciplinary pedagogical knowledge, creative-aesthetic AI knowledge, ethics as embedded disciplinary practice, and inquiry-oriented AI pedagogy. Based on these findings, the paper proposes three emerging theoretical extensions to AI-TPACK to address the identified lacunae and discusses implications for the design of structured professional knowledge codification systems that better represent complex, interdisciplinary, and rapidly evolving knowledge domains.