From proficiency to pedagogy: A mixed-methods study of in-service teachers’ TPACK-GenAI and the mediating role of pedagogical knowledge

Document Type

Article

Source of Publication

Computers and Education Artificial Intelligence

Publication Date

6-1-2026

Abstract

This study investigated in-service teachers' Technological Pedagogical Content Knowledge for Generative Artificial Intelligence (TPACK-GenAI) and examined the factors influencing their integration of generative AI in classroom practice. Using an explanatory sequential mixed-methods design, data were collected from 325 in-service teachers across 26 countries, followed by in-depth interviews with seven teachers. Quantitative results revealed that Technological Knowledge (TK), Pedagogical Knowledge (PK), and Pedagogical Content Knowledge (PCK) were significantly associated with teachers' overall TPACK-GenAI. Crucially, mediation analysis confirmed that Technological Pedagogical Knowledge (TPK) serves as the indirect association through which TK is related to overall TPACK-GenAI competence. Qualitative findings enriched this result, illustrating that teachers’ confidence stems not from technical skill alone, but from their ability to apply AI tools to specific pedagogical challenges. The qualitative data also provided context for the non-significant effects of demographic variables, suggesting that barriers such as lack of access and training are more influential than experience or school level. These findings emphasize the need for professional development that is pedagogically focused, context-sensitive, and moves beyond mere technical training. This study contributes a detailed, multinational perspective on teacher preparedness in the age of AI and validates the critical role of pedagogical thinking in effective technology integration.

ISSN

2666-920X

Publisher

Elsevier BV

Volume

10

Disciplines

Education

Keywords

AI literacy, Generative artificial intelligence, Professional development, Teacher competencies, Technology integration, TPACK-GenAI

Scopus ID

105038441923

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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