A historical and ethical inquiry into the transformative role of artificial intelligence in scientific research methodologies

Josephat Nairutia Kemei; Kizito Muchanga Lusambili; Pontian Godfrey Okoth

doi:10.51867/AQSSR.2.4.55

Authors

Josephat Nairutia Kemei Masinde Muliro University of Science and Technology, Kenya Author https://orcid.org/0000-0002-4911-3378
Kizito Muchanga Lusambili Masinde Muliro University of Science and Technology, Kenya Author https://orcid.org/0000-0002-5430-4558
Pontian Godfrey Okoth Masinde Muliro University of Science and Technology, Kenya Author https://orcid.org/0000-0001-8369-9029

DOI:

https://doi.org/10.51867/AQSSR.2.4.55

Keywords:

Artificial-Intelligence, Epistemology, Ethics, Methodology

Abstract

The evolution of scientific research methodologies has closely mirrored the technological transformations that define human progress—from manual record keeping and statistical modeling to computational and machine-assisted analysis. This paper conducts a historical and ethical inquiry into the transformative role of Artificial Intelligence (AI) in reshaping scientific research methodologies across time and contexts. Anchored in Actor-Network Theory (ANT), the study conceptualizes AI not merely as a technical tool but as a dynamic non-human actor that co-produces knowledge in socio-technical research networks. Using a qualitative historical research design, the study draws on secondary literature, archival data, and oral histories to trace the progression of AI from early computational devices to contemporary deep learning and natural language processing systems. Thematic and content analyses reveal that AI enhances data accuracy, accelerates predictive modeling, and fosters interdisciplinary collaborations, thus redefining epistemic practices and expanding the scope of scientific inquiry. However, the findings also expose critical ethical challenges, including algorithmic bias, data privacy violations, and epistemological opacity that threaten research integrity. To mitigate these risks, the paper proposes a hybrid governance framework that integrates AI literacy, algorithmic transparency, and co-produced ethical accountability between human and machine actors. The study concludes that sustainable integration of AI in scientific research requires balancing innovation with moral responsibility, ensuring that AI serves as a partner in the advancement of credible and ethically grounded knowledge.

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