12,445 papers · continuously updated · last export: 10 Aug 2026livingmeta.ai
← Browse all papers
Research theme

Reproducibility with AI

The Reproducibility with AI theme comprises 109 papers in this corpus published between 2007 and 2026. Work here is dominated by Empirical Study, Design Science, Case Study. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Empirical Study19 (17%)
  • Design Science15 (14%)
  • Case Study13 (12%)
  • Literature Review11 (10%)
  • Content Analysis9 (8%)
  • Conceptual9 (8%)

Research domains

  • Research Integrity96 (88%)
  • Research Productivity7 (6%)
  • Data Analysis3 (3%)
  • Peer Review1 (1%)
  • Scholarly Infrastructure1 (1%)

Frequent sub-topics

Data Availability Statements in mega journals; data sharing practices and transparency · 1AI and radiomics reproducibility crisis in clinical research · 1transparency, open science, privacy, and algorithmic bias in digital research · 1open science adoption in communication research · 1quantum software defect dataset reproducibility and maintenance · 1stochastic dynamics of AI assistance on code quality; methodological caution in causal inference · 1validation of machine learning prediction models with clustered repeated-measures data, clinical prediction model standards, digital twins · 1LLM-assisted computational reproducibility on testbeds · 1

Open research gaps

  • The paper identifies tensions between enablers and barriers perceived by diverse stakeholders and proposes recommendations for addressing these tensions, suggesting further work needed to reconcile di
  • The study identifies the need for stricter enforcement of reproducibility guidelines by reviewers and editors, more comprehensive documentation standards for AI research, and improved accessibility of
  • Future work should address: (1) Generalization to non-computational and non-quantitative research domains, (2) Integration of external information sources while maintaining document-level assessment f
  • Whether LLMs can reliably carry out computational reproducibility assessments (addressed in this study)
  • Generalizability across different LLM models and architectures

Representative papers

← Browse all papersPriority research agenda →