PETMALU-AI: A Reporting Checklist for Transparent and Accountable Generative AI-Assisted Research Writing

The rapid integration of artificial intelligence (AI) tools into academic research and writing has introduced new challenges related to transparency, accountability, and reproducibility. While publishers increasingly permit AI use with disclosure, there is currently no standardized reporting framework guiding how such use should be documented. This study introduces PETMALU-AI (Principles for Ensuring Transparency in Machine-assisted Authorship, Logging, and Use of Artificial Intelligence), a reporting checklist designed to support more consistent disclosure and documentation of AI-assisted research practices. The development of PETMALU-AI followed a multi-phase design framework, including (1) a narrative synthesis of existing AI policies and reporting guidelines, (2) item generation grounded in methodological transparency principles, and (3) modified Delphi-based expert validation involving an interdisciplinary panel. The Delphi process produced a 33-item checklist organized across nine domains: disclosure, human accountability, system transparency, prompting and interaction processes, data provenance, validation, ethics, limitations, and reproducibility. All retained items achieved expert consensus, with item-level content validity indices ranging from 0.85 to 1.00 and final agreement rates ranging from 85% to 100%. Reliability analyses further supported the stability of expert judgments, with Fleiss’ kappa and the intraclass correlation coefficient indicating strong agreement and high consistency. The checklist emphasizes proportional reporting, human accountability, and transparent documentation of AI-assisted research practices. PETMALU-AI offers a framework for documenting AI involvement in ways that are visible, assessable, and accountable, providing a foundation for more transparent research reporting as scholarly writing becomes increasingly shaped by human–AI collaboration.