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

2026-05-22

AI Summary

Current AI contribution disclosure mechanisms focus solely on final outputs, failing to accurately capture the cognitive interplay and division of decision-making between humans and AI throughout the research process, thereby leading to ambiguous attribution. This work proposes the PAIRED framework, which introduces a process-oriented disclosure mechanism centered on “decision points” as the fundamental unit, enabling prospective author logs to automatically generate structured contribution records. Grounded in four core design principles—decision-point granularity, dual-sided output, artifact-triggered logging, and process-anchored modeling—the framework is embedded within AI-enabled research platforms to facilitate automated, compliant disclosure. Empirical validation demonstrates that PAIRED effectively distinguishes human originality from the degree of AI suggestion adoption and offers a viable pathway for platform integration, substantially enhancing transparency and traceability in AI-augmented scientific collaboration.

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.

Problem

• AI contribution
• scientific research
• transparency
• research process
• academic disclosure

Innovation

• process-oriented reporting
• AI research transparency
• decision-point logging
• dual-facing disclosure
• artifact-triggered auditing