The Utility of AI Tools in Auditing Adherence to Pre-Analysis Plans

Jeffrey Clemens and Anwita Mahajan

No 35719, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Pre-analysis plans (PAPs) can improve research reproducibility by reducing researchers’ degrees of freedom, but their value depends on adherence. We argue that large language models (LLMs) can provide an efficient, scalable, and systematic way for authors and reviewers to assess adherence to PAPs. In an application to our own research, an LLM systematically identifies precommitted design choices, evaluates deviations, and diagnoses gaps in pre-specification, substantially reducing the human labor required for these tasks. However, variability in audit output across LLMs underscores the continued importance of human judgment. We discuss implications for best practices in AI-assisted PAP auditing.

JEL-codes: C18 C80 D82 (search for similar items in EconPapers)
Date: 2026-09
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