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

Abstract

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.