PAIRED: A Process-Anchored Framework for Transparent Reporting of AI Contributions in Scientific Research
For researchers and publishers, this framework addresses the need for honest and detailed reporting of AI contributions, distinguishing between different levels of human involvement in the research process.
The paper identifies a critical gap in existing frameworks for reporting AI contributions in scientific research, which are output-oriented and fail to capture the cognitive dynamics of the research process. The authors propose PAIRED, a process-anchored framework with four design principles to enable transparent reporting of AI collaboration, demonstrated through worked examples.
The rapid integration of generative AI into scientific research has exposed a critical gap in academic disclosure practice. Existing frameworks for reporting AI contributions are uniformly output-oriented -- they document what AI produced, not how the research unfolded. As a result, researchers who wish to report their AI collaboration honestly lack the tools to do so: no current framework can distinguish between a researcher who originated a research direction and one who adopted a direction proposed by AI, or between a researcher who critically evaluated AI-generated alternatives and one who accepted AI output without independent assessment. This gap is not a matter of compliance detail; it is a failure to capture the cognitive dynamics that determine what kind of intellectual contribution a paper actually represents. We propose PAIRED -- Process-Anchored Interaction Reporting for AI-Enabled Discovery -- a dual-facing framework that addresses this gap through four design principles: process orientation, which takes the decision point rather than the research product as the fundamental unit of documentation; dual-facing output, which derives a structured publisher disclosure from a prospective author log without double work; decision-point granularity, which operates between session-level coarseness and message-level impracticality; and artifact-triggered logging, which provides an auditable rule against selective omission. We demonstrate PAIRED through worked examples, discuss its limitations openly, and propose a model-assisted adoption pathway that embeds the framework's logging discipline directly into AI research platforms.