Triadic Instructional Design: The Impact of Structured AI Training on Pre-Service Teachers’ Intelligent-TPACK, Attitudes, and Lesson Planning Skills

Artificial Intelligence (AI) holds transformative potential to revolutionize teaching and learning, yet its rapid integration poses significant challenges for teacher preparation. While AI competencies—encompassing knowledge, skills, and attitudes—are critical for effective integration, limited research has holistically addressed these three interconnected domains. To bridge this gap, this quasi-experimental study (N = 259) evaluated a triadic instructional design synergizing the intelligent technological, pedagogical, and content knowledge (Intelligent-TPACK) framework, Synthesis of Qualitative Data model, and curated AI tools. Pre-service English as a foreign language (EFL) teachers were assigned to an experimental group (n = 137) receiving the structured intervention or a control group (n = 122) engaging in self-directed AI exploration. Results reveal that the experimental group achieved greater gains across all Intelligent-TPACK dimensions and demonstrated higher-order AI applications in lesson planning. Furthermore, the experimental group experienced a significant reduction in perceived pressure and reported higher perceived usefulness regarding AI integration. Qualitative data revealed that hands-on AI tasks enhanced participants’ confidence, yet challenges with prompts and critical adaptation persisted. The findings demonstrate that systematic training is essential for transforming pre-service teachers’ passive awareness into competent AI integration. Finally, this paper proposes practical implications for integrating this triadic framework into teacher education curricula to facilitate sustainable AI adoption.