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AI evidence extraction

Using Design Theory To Understand Human-Machine Collaborative Review

Runtao Ren, Antoine Bordas, Jian Ma, Jianxi Luo · CityU Scholars · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

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Methodology & findings

Study design

Planned methodology using design theory (specifically Concept-Knowledge theory) with three-step formal modeling approach: (1) formally model the review objective in C-K space, (2) separately model human and machine reviewer C-K spaces then define joint space, (3) employ taxonomy to codify decision patterns.

Primary method

Design science research using Concept-Knowledge (C-K) theory; formal modeling strategy validated in management and innovation disciplines

Main result

The paper is a research proposal rather than a completed study with results. The authors state: "The proposed model is expected to yield several key results. By formalizing the decision mechanisms, we anticipate explaining and predicting when and how human-machine collaborative review generates its decisions. The codified mechanisms are intended to form and reveal a taxonomy that covers the full space of review decisions."

Research paradigm

Design science research; formal modeling using Concept-Knowledge (C-K) theory

Author conclusions

The authors conclude that "This study is expected to contribute to existing knowledge in several ways. First, we seek to extend C-K theory to the domain of peer review, demonstrating that review is itself a generative process amenable to formal modelling. Second, we aim to develop the formal model that accounts for decision-making mechanism of human-machine review. Third, the model is intended to provide a predictive foundation that can inform the design of peer review systems by clarifying how human and machine capabilities should be combined for optimal review outcomes."

Open questions raised

  • The authors identify that there is "currently no mechanism that explains the micro-level decision process specific to human-machine collaborative review" and that "no existing research addresses when and why a manuscript should be accepted, rejected, or revised in a human-machine co-review setting."
  • The authors identify that there is "currently no mechanism that explains the micro-level decision process specific to human-machine collaborative review" and note the absence of research addressing "when and why a manuscript should be accepted, rejected, or revised in a human-machine co-review setting." They observe that existing collaborative review models are limited to human-human collaboration.
  • The authors identify that there is "currently no mechanism that explains the micro-level decision process specific to human-machine collaborative review" and that "no existing research addresses when and why a manuscript should be accepted, rejected, or revised in a human-machine co-review setting." They also note the need for "a theoretical framework that can explain and predict review decisions in human-machine collaboration" as AI-assisted peer review becomes more widespread.
Extracted from: pdfAgreement 78%

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