Artificial Intelligence in Teacher Professional Development: A Systematic Review

• In Event: Educators and AI: Perspective, Practice, and Professional Growth

Abstract

This review examines how artificial intelligence (AI) is applied in in-service teacher professional development (TPD), analyzing 12 empirical studies published up to 2024. Using theories of AI in education and effective TPD, it distinguishes between AI as a learning objective, aimed at building teachers’ AI related competencies, and AI as a pedagogical tool, designed to enhance teacher learning. The findings indicate that adaptive learning and feedback via natural language processing and machine learning are predominant. While studies generally report positive outcomes for teacher knowledge, AI literacy, and self-efficacy, effects on student outcomes remain understudied. The review highlights the potential for AI to personalize teacher learning experiences and calls for future research on scalability, ethical considerations, and long-term effectiveness.

Authors

• Nick Erhardt, University of Potsdam

• Eric Richter, KU Eichstätt-Ingolstadt

• Yizhen Huang, University of Potsdam

• Dirk Richter, University of Potsdam

• Katharina Scheiter, University of Potsdam