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

◐ medium · 2026-05-17ai-literacy, faculty-development, generative-ai, multi-agent, scaffolding, teacher-role, teacher-ai-competency, agentic-ai-ecosystems-higher-education, faculty-development-genai, rag

This study investigates how teachers design multi-agent instructional workflows and identifies three distinct teacher archetypes that emerge from behavioral log analysis of 61 in-service teachers:

  • Systematic Optimizers: Iteratively refine complex multi-agent architectures with a methodical, architectural focus. These teachers treat AI agent orchestration as a systems design problem.
  • Prolific Creators: Rapidly prototype pragmatic tools, leveraging scaffolding to quickly produce usable instructional aids. Efficiency-oriented and output-focused.
  • Passive Observers: Exhibit polarized expert-novice profiles with minimal proactive modification of the AI workflows presented to them.

    • AI-TPACK Beyond Discrete Knowledge

    The core theoretical contribution is that effective AI-TPACK integration — the fusion of Technological, Pedagogical, and Content Knowledge in an AI context — emerges not from possessing separate knowledge domains, but from a dynamic interplay of three factors:

    1. Systems thinking: The ability to conceptualize interactions among multiple AI agents and map them to instructional goals

    2. Pedagogical beliefs: Underlying assumptions about teaching and learning that fundamentally shape design choices

    3. Self-efficacy: Confidence in one's ability to orchestrate complex AI-enhanced workflows

    This challenges static, checklist-based models of Teacher AI Competency and aligns with broader research showing that Faculty Development must address cognitive-behavioral diversity, not just technology training.

    Implications for Teacher Professional Development

    The findings call for differentiated scaffolding:

  • Systematic Optimizers benefit from advanced system-design frameworks and revision opportunities
  • Prolific Creators thrive with rapid-feedback cycles and modular, reusable components
  • Passive Observers need explicit modeling, guided practice, and confidence-building exercises

    • This connects to the Teacher AI Adoption Confidence finding that teacher confidence fully mediates institutional support effects on AI adoption. It also extends the Teachingcoach Chatbot Instructor Guidance paradigm by suggesting that coaching scaffolds must be personalized to teacher archetypes, not just content domains.

    Connection to Agentic AI in Education

    The multi-agent workflow framing positions this work at the intersection of Agentic AI and teacher professional learning. As Eduagentbench Agent Teaching Benchmark establishes benchmarks for what agent tutors should do, this study addresses the complementary question: how should teachers learn to design and orchestrate those agents?

    Connected Concepts

  • Teacher AI Competency
  • Faculty Development
  • Agentic AI

    • Connected Articles

  • Teacher AI Adoption Confidence
  • Teachingcoach Chatbot Instructor Guidance
  • Eduagentbench Agent Teaching Benchmark

    • Citation

    Sun, Y., Xin, H., Li, S., Niu, Q., Chai, C. S., Huang, L., & Chen, G. (2026). Modeling AI-TPACK in practice: Insights from teachers' multi-agent workflow design. arXiv:2605.13906 [cs.CY].