Responsible AI in Non-Empirical Research: Guidance for Authors, Reviewers, and Editors in HRD

As generative artificial intelligence (AI) reshapes scholarly practice, non-empirical research in human resource development (HRD) faces distinct challenges that existing guidance has not addressed. Non-empirical scholarship—including systematic reviews, bibliometric analyses, conceptual articles, and theory articles—makes its contribution through the quality of the author’s intellectual engagement with ideas, a process that AI can support but cannot replace. This editorial articulates why non-empirical work demands careful attention to AI use, proposes a typology distinguishing legitimate, contested, and illegitimate applications across six research touchpoints, and establishes reporting expectations for non-empirical submissions. Grounded in the Academy of HRD Standards on Ethics and Integrity , we address AI’s current limitations for conceptual and review scholarship, its implications for scholarly equity, and the pragmatic-substantive distinction central to transparent disclosure. We offer eight reporting expectations and discuss implications for reviewers, editors, and the field.