Modeling AI-TPACK in Practice Insights from Teachers Multi-Agent Workflow Design

2026-05-12

AI Summary

This study investigates the cognitive mechanisms underlying teachers’ design of multi-agent instructional workflows, reconceptualizing AI-TPACK (Artificial Intelligence–Technological Pedagogical Content Knowledge) as a dynamic cognitive practice rather than a static knowledge construct. Drawing on a mixed-methods approach that integrates cluster analysis and Markov chain modeling, the research analyzes behavioral logs from 61 teachers, 15 instructional artifacts, and 12 interviews to identify three distinct design archetypes: Systematic Optimizers, Prolific Creators, and Passive Observers. The findings elucidate how systems thinking, pedagogical beliefs, and self-efficacy dynamically shape the integration of AI-TPACK, offering empirical grounding for differentiated support strategies in professional development for intelligent educational technologies.

Abstract

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.

Problem

• AI-TPACK
• multi-agent workflow
• teacher design behavior
• cognitive underpinnings
• instructional design

Innovation

• AI-TPACK
• multi-agent workflow
• teacher design behavior
• cognitive-behavioral archetypes
• differentiated scaffolding