Pedagogy First, Technology Second: Cross-Level Relationships Between Teacher Professional Knowledge and Student Learning

Overview

Shen and colleagues examine how teacher professional knowledge shapes student learning in K-12 AI education, drawing on social cognitive theory and the TPACK framework. Using a multilevel structural equation model, they analyzed data from 46 secondary school teachers and 2,832 students to test cross-level effects of two knowledge components: teacher AI knowledge (TAIK, the technological/technical side) and teacher pedagogical AI knowledge (TPAIK, the pedagogical side).

Key Findings

• Technical AI knowledge alone was insufficient — without pedagogical AI knowledge, TAIK could even slightly diminish students' perceptions of AI for social good.
• Pedagogical AI knowledge (TPAIK) played the fundamental role, fostering both students' perceptions of AI for social good and their behavioral intention to learn AI.
• Neither TAIK nor TPAIK was directly associated with students' AI knowledge gains — the teacher-knowledge effects operated through perceptions and intention rather than direct knowledge transfer.
• Findings support the "pedagogy first, technology second" guideline: pedagogical AI knowledge should be prioritized over technical AI knowledge for excellence in K-12 AI education.

Implications for Practice

• For teacher educators: professional learning in AI education should foreground pedagogical AI knowledge (how to teach with and about AI) over mere technical proficiency.
• For professional development: PD programs should treat pedagogical AI knowledge as the priority lever for influencing student perceptions and intentions.
• For school leaders and AI curriculum designers: building teachers' pedagogical capacity matters more than equipment or technical training for fostering student engagement with AI.

Connected Concepts

• TPACK
• Teacher AI Competency
• K 12
• Teacher Education
• Pedagogy
• AI Education

Connected Articles

• TPACK GenAI Inservice Teachers Mediation 2026 — in-service teachers' TPACK-GenAI and pedagogical mediation (Mohebi & ElSayary 2026)
• Preservice Teachers Responsible GenAI 2026 — pre-service teacher preparation for responsible GenAI use (Kohnke et al. 2026)
• Stanford Evidence Base AI K12 2026 — evidence base for K-12 AI education
• School AI Education Readiness Gaps Agency 2026 — school AI-education readiness gaps and teacher agency

Citation

Pedagogy first, technology second: Cross-level relationships between teacher professional knowledge and student learning in artificial intelligence (AI) education — Shen, W., Chai, C.-S., Chiu, T. K. F., Yau, K. W., Meng, H., King, I., Wong, S., & Yam, Y. (2026). Computers and Education: Artificial Intelligence, 10, 100564.