PAIRED: A Process-Anchored Framework for Transparent Reporting of AI Contributions in Scientific Research

• URL: http://arxiv.org/abs/2605.24325v1
• Date: Sat, 23 May 2026 01:10:56 GMT
• Title: PAIRED: A Process-Anchored Framework for Transparent Reporting of AI Contributions in Scientific Research
• Abstract summary: Existing frameworks for reporting AI contributions are uniformly output-oriented.<n>No current framework can distinguish between a researcher who originated a research direction and one who adopted a direction proposed by AI.
• Score: 0.7614628596146601
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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