For current K-12 AI education, understanding how teacher professional knowledge influences student learning is essential. This study employed social cognitive theory to conceptualize teachers’ instructional practice and conducted a multilevel structural equation model analysis to examine the cross-level effects of teacher AI knowledge (TAIK) and pedagogical AI knowledge (TPAIK) on student AI knowledge (SAIK), perceptions of AI for social good (SAISG), and behavioral intention to learn AI (SBIAI). Data from 46 secondary school teachers and 2832 students revealed intricate mechanisms. TAIK alone was insufficient and could slightly diminish SAISG without TPAIK. Instead, TPAIK played a fundamental role in fostering SAISG and SBIAI. Neither TAIK nor TPAIK was directly associated with SAIK. Our findings distinguished the roles of TAIK and TPAIK, which demonstrated the “Pedagogy first, technology second” guideline: TPAIK should be prioritized over TAIK for teachers’ and students’ excellence in K-12 AI education.
Shen et al. (Sun,) studied this question.