Artificial Intelligence and Scientific Reproducibility: Developing Reliable Frameworks for Data-Driven Research

Jeffrey Volenec

Artificial Intelligence and Scientific Reproducibility: Developing Reliable Frameworks for Data-Driven Research

Author(s)	Jeffrey Volenec
Country	United States
Abstract	The rapid integration of artificial intelligence (AI) into scientific research is transforming data analysis, computational modelling, hypothesis generation, simulation and knowledge discovery. At the same time, the increasing complexity of AI-driven research introduces new challenges for scientific reproducibility. Differences in datasets, preprocessing pipelines, software environments, model architectures, random initialisation, hyperparameters and computational infrastructure can produce substantially different results from nominally identical experiments. This paper examines the relationship between artificial intelligence and scientific reproducibility and proposes a structured framework for developing reliable, transparent and repeatable data-driven research workflows. The framework integrates data provenance, computational environments, model documentation, version control, experiment tracking, statistical validation, independent replication and transparent reporting. Particular attention is given to reproducibility challenges associated with machine-learning models, including data leakage, hidden preprocessing, stochastic training, model drift, benchmark dependence and insufficient documentation. The paper also examines the emerging role of reproducible AI practices such as containerisation, workflow automation, model cards, datasheets, versioned datasets and machine-readable research metadata. A human-centred approach is proposed in which AI systems support scientific discovery while maintaining researcher oversight and methodological accountability. The paper argues that reproducibility should be treated not as a final reporting requirement but as a design principle embedded throughout the research lifecycle. The proposed framework provides a basis for improving the reliability, transparency and auditability of AI-supported scientific research and can contribute to stronger scientific confidence in increasingly data-intensive research environments.
Keywords	Artificial Intelligence, Scientific Reproducibility, Reproducible Research, Machine Learning, Data Science, Research Transparency, Computational Science, Data Provenance, Model Validation, Open Science.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-14

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American Journal of Advanced Multidisciplinary Research and Innovation

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Artificial Intelligence and Scientific Reproducibility: Developing Reliable Frameworks for Data-Driven Research

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