Enhancing Teacher Competence in Coding and Artificial Intelligence Through an Interdisciplinary Plugged-Unplugged

The integration of coding and artificial intelligence (AI) in early childhood education has become a global priority, yet many Indonesian teachers remain underprepared to implement these competencies. This study examines the effectiveness of a professional development programme aimed at enhancing teacher competence in coding and AI through an interdisciplinary plugged-unplugged approach. Using a pre-experimental One-Group Pretest-Posttest design, the study involved 14 teachers from seven early childhood schools in Makassar, South Sulawesi. The intervention combined screen-based (plugged) and screen-free (unplugged) activities within an interdisciplinary framework. Teacher competence was measured using an observational rubric covering conceptual understanding, pedagogical integration, practical implementation, and reflective practice. The results showed a substantial increase in competence from pre-test (M = 1.43) to post-test (M = 2.89). The Wilcoxon Signed-Rank Test confirmed a significant improvement (p < .05) with a very large effect size (r ≥ .80). These findings indicate that the programme effectively enhances teachers’ competence and has strong potential as a scalable model for teacher training in Indonesia.