Modeling AI-TPACK in Practice Insights from Teachers Multi-Agent Workflow Design
For teacher educators and AI curriculum designers, it reveals the need for differentiated scaffolding based on teachers' cognitive-behavioral diversity.
This study identified three teacher archetypes (Systematic Optimizers, Prolific Creators, Passive Observers) in designing multi-agent AI workflows, showing that AI-TPACK integration emerges from dynamic cognitive-behavioral factors rather than discrete knowledge.
This study investigates teachers design behaviors and cognitive underpinnings when designing multi-agent instructional workflows. Analyzing behavioral logs (N=61), cluster and Markov analyses identified three archetypes: Systematic Optimizers iteratively refining complex architectures; Prolific Creators rapidly prototyping pragmatic tools via scaffolding; and Passive Observers exhibiting polarized expert-novice profiles. Subsequent artifact (n=15) and interview (n=12) analyses reveal AI-TPACK integration emerges from a dynamic interplay of systems thinking, pedagogical beliefs, and self-efficacy, not merely from the possession of discrete knowledge. These findings call for differentiated scaffolding responsive to teachers cognitive-behavioral diversity.