When AI use becomes the norm: Researcher perspectives on AI disclosure policy and practice.
TL;DR
The study provides empirical evidence supporting the development of a structured AI contribution taxonomy as a more principled and practical alternative to existing disclosure practices and suggests that effective AI disclosure governance should incorporate field-sensitive adaptation rather than relying on uniform implementation across diverse research communities.
Abstract
Background
Despite the proliferation of AI disclosure requirements in academic publishing, recent research suggests a persistent gap between policy expectations and research practice. However, little is known about how researchers perceive and navigate these requirements or what limitations they identify in current disclosure practices.
Method
This study explored researchers' experiences with AI disclosure through semi-structured interviews with 14 researchers from two interdisciplinary fields, bioinformatics and computational social science. Data were analyzed using reflexive thematic analysis.
Results
Four thematic groupings emerged: fragmented and inconsistently enforced requirements; systemic limitations, including scope ambiguity, research integrity risks, and structural disincentives to honest reporting; researcher perspectives on more effective disclosure practices; and disciplinary variation as a cross-cutting dimension shaping how these issues are experienced across research communities. The findings suggest that the compliance gap reflects an interaction between structural conditions and ethical obligations. This gap is sustained by self-reporting mechanisms that lack verification capacity, a transparency paradox in which honest disclosure can invite professional penalization, and disciplinary norms that resist uniform governance approaches.
Conclusions
The study provides empirical evidence supporting the development of a structured AI contribution taxonomy as a more principled and practical alternative to existing disclosure practices. More broadly, the findings suggest that effective AI disclosure governance should incorporate field-sensitive adaptation rather than relying on uniform implementation across diverse research communities.