Artificial Intelligence as a Scientific Research Instrument: Productivity, Discovery, and the Risk of Illusions of Understanding

Abstract

Artificial intelligence is becoming embedded across the scientific research pipeline, from literature discovery and data analysis to hypothesis generation, simulation, and experimental design. This paper examines AI as a research instrument rather than merely a productivity application, with particular attention to the relationship between increased research throughput and scientific understanding. A structured literature synthesis was conducted using recent empirical and conceptual studies of AI in science together with the UNESCO Recommendation on Open Science. Evidence indicates that AI use and its potential benefits are widespread across scientific fields and have expanded rapidly since 2015, while a persistent mismatch remains between AI education and its application in research. At the same time, recent scholarship identifies an epistemic risk: researchers may produce more outputs while developing weaker causal, conceptual, or methodological understanding of the phenomena under study. The analysis therefore distinguishes operational productivity from epistemic quality. It proposes a research-governance framework in which AI-generated hypotheses, analyses, and interpretations remain traceable, reproducible, critically evaluated, and integrated with open scientific practices. The paper concludes that the scientific value of AI depends not simply on model capability but on the design of human–AI research workflows, verification procedures, and institutional norms for transparency and reproducibility.

Keywords

artificial intelligence; scientific discovery; research productivity; open science; epistemic risk

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