Interaction-Rich instructional design in AI-Supported teacher education: Learning processes and educational implications for instructional readiness

As generative artificial intelligence (GAI) becomes embedded in educational practice, a central challenge for teacher education is no longer technical adoption but developing instructional readiness grounded in pedagogical reasoning and ethical judgment. Moving beyond outcome-oriented evaluations of technology competence, this study adopts a process-oriented perspective to examine how AI-integrated instructional activities shape pre-service teachers' learning processes and development of Intelligent-TPACK. Using a sequential explanatory mixed-methods design, pre-service teachers participated in a course integrating GAI into instructional design tasks. Quantitative results revealed uneven developmental patterns, while basic technological knowledge remained stable, substantial gains were observed in pedagogical integration, instructional coherence, and AI ethics literacy, indicating a shift in technological knowledge from a primary target to baseline. Qualitative findings showed that scaffolded human–AI interaction supported creative ideation, reduced cognitive barriers in lesson planning, and fostered reflective judgment. Persistent challenges in content evaluation and anticipated school constraints highlight the need for sustained pedagogical scaffolding. Rather than modeling psychological constructs as causal mechanisms, this study reconceptualizes instructional confidence and innovation readiness as outcomes of experience-based learning. By foregrounding learning trajectories and structural differentiation in knowledge development, this research offers design-oriented insights for AI-enhanced teacher education emphasizing authentic tasks, reflection, and ethical awareness.