The AIR Framework for Research Transparency: A Critical Analysis of Stage-Specific AI Disclosure in the Context of Accessibility and Research Integrity

The rapid adoption of generative AI tools across disciplines has created an urgent need for robust disclosure practices that can keep pace with evolving research behaviours (Electv Training, 2026a). Existing guidance from bodies such as COPE and ICMJE tends to be principle-based, tool-specific, or stage-agnostic, leaving researchers without a shared vocabulary for describing how AI is actually used across the research lifecycle (Electv Training, 2026a). The AIR (AI in Research) framework, developed by Electv Training in 2026, addresses this gap by offering a stage-specific matrix that categorizes AI involvement across seven research phases and five engagement bands, from no use to substantial use (Electv Training, 2026a). In this article, I provide a critical analysis of AIR’s theoretical foundations, practical viability and ethical limitations, with particular attention to accessibility and neurodiversity. Drawing on virtue epistemology (Zagzebski, 1996), I argue that transparency should be understood as a constitutive epistemic virtue rather than a merely procedural requirement, and I examine how AIR operationalizes this claim. I then report findings from an inter-rater reliability pilot study (n=15 raters, nine scenarios, Cohen’s κ=0.72), which suggests that trained evaluators can apply AIR with substantial agreement while also revealing systematic boundary ambiguities (Star & Griesemer, 1989). Building on these empirical and conceptual insights, I identify five major limitations of the framework, including false precision, inadequate treatment of accessibility-related AI use and the risk of stigmatizing legitimate high-band practices, and I propose a set of refinements designed to preserve AIR’s strengths while mitigating its most serious risks.