This study aims to develop a training module for lesson planning using the Technological Pedagogical Content Knowledge (TPACK) approach integrated with Artificial Intelligence (AI) to enhance the pedagogical competence of junior high school art teachers in Salatiga. The research employed a Research and Development (R&D) method based on the Borg & Gall model, limited to the first five stages. The development process involved expert validation and limited field testing with 21 MGMP members. The research instruments included validation sheets, questionnaires, and pre-test/post-test assessments, while data were analyzed using descriptive statistics and paired-sample t-tests. The validation results indicated a high level of feasibility (average 90% for material and 83.6% for module). The effectiveness tests revealed a statistically significant improvement in teachers' pedagogical competence, with higher post-test scores (77.86) compared to pre-test scores (61.43), and a significance value (Sig. 0.000 < 0.05). The module, designed to be self-instructional, self-contained, adaptive, and user-friendly, demonstrates the potential of AI-enhanced TPACK training to improve the quality of art education. These findings imply that integrating AI into teacher professional development can effectively enhance pedagogical competence, with broader implications for designing innovative training models across various subject areas.
Sari et al. (Tue,) studied this question.