<![CDATA[Background: Artificial intelligence (AI) can expand teachers’ capacity to develop visual, multimodal, adaptive, and interactive learning content, but tool mastery alone does not ensure pedagogical competence. Recent reviews also indicate that teacher professional development (TPD) for AI remains less developed than research on AI applications for learners. In early childhood education (ECE), AI-supported content development must additionally address child development, safety, simplicity, and the balance between digital interaction and direct pedagogical interaction.Aims: This study synthesizes evidence on teacher competence, professional development design, training activities, and ECE-relevant considerations to formulate an AI-based Activity-Based Training Design (ABTD-AI) framework.Methods: Following PRISMA 2020, we searched five databases for literature published mainly from 2016 to 2026. Of 180 records, 20 duplicates were removed; 160 were screened, 60 full texts assessed, and 40 studies included in a thematic synthesis guided by four research questions and predefined criteria.Results: The findings show that AI-based content development requires integrated technological, pedagogical, subject-specific, AI-literacy, evaluation, and child-appropriate design competencies. Effective TPD combines contextualized practice, collaboration, feedback, reflection, and ongoing mentoring, while activity-based training should produce observable outputs that transfer to classroom practice. These principles inform the seven-stage ABTD-AI framework, linking AI co-creation and verification with practical training, product development, feedback, and mentoring.Conclusion: ABTD-AI is presented as an evidence-informed conceptual framework for designing and mentoring kindergarten teachers in AI-based interactive content development. Its effectiveness has not yet been established empirically and requires subsequent implementation research.]]>
