A Digital Twin–Enabled Human-in-the-Loop Orchestration Protocol for AI Co-Teaching and Assessment in Engineering Capstone Courses

Authors

  • Yizhuo Song 1 https://orcid.org/0009-0007-6460-9578
    • Conceptualization
    • Data Curation
    • Investigation
    • Validation
    • Writing – Original Draft Preparation
  • Xiaomei Yuan 2,* https://orcid.org/0009-0003-4853-7798
    • Formal Analysis
    • Methodology
    • Project Administration
    • Supervision
    • Validation
    • Writing – Review & Editing
1 City Graduate School, City University Malaysia, Petaling Jaya, Malaysia 46100
2 Business School, Xi’an Innovation College of Yan’an University, Xi’an City, China 710100
Corresponding author: yuanmay@126.com

DOI:

https://doi.org/10.63646/SMLK8377

Keywords:

Engineering Capstone Education, Digital Twin, Human-AI Collaboration, Learning Orchestration, Formative Assessment, Engineering Design, Evidence Provenance

Abstract

Engineering capstone teams increasingly use instrumented prototypes, simulations, building information models, and AI assistants, but these resources are seldom coordinated as one inspectable learning workflow. This article specifies a technical design for a course project twin framework and a human-in-the-loop orchestration protocol for AI co-teaching and assessment in engineering capstones. The framework links an engineering-system representation, project-process events, and learning-evidence records while keeping their purposes distinct. It reserves “digital twin” for a bounded subsystem with a declared synchronization relationship: M0 is an instructional model, M1 a one-way shadow, and M2 a controlled synchronized subsystem. AI co-teaching is defined here as instructor-designed task allocation in which AI retrieves, compares, or drafts bounded support; learners test and explain engineering claims; and instructors retain authority over safety, assessment, and milestone decisions. The specification includes a service architecture, typed record contracts, a nine-state workflow with exception paths, an outcome-to-evidence map, governance controls, and a reproducible synthetic bridge-monitoring case. Proposed acceptance criteria distinguish deterministic safety and provenance gates from human-rated feedback quality and future learning outcomes. This is a design specification; no implementation, classroom deployment, or measured outcome is claimed.

How to Cite

Song, Y., & Yuan, X. (2026). A Digital Twin–Enabled Human-in-the-Loop Orchestration Protocol for AI Co-Teaching and Assessment in Engineering Capstone Courses. Trends and Reviews of Technological Engineering in Education, 2(3), 22-56. https://doi.org/10.63646/SMLK8377

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