Artificial Intelligence in Research Methodology: Methodological Advances, Opportunities, Risks, and Responsible AI Integration Framework

Abstract

This article discusses the integration of artificial intelligence (AI) as part of the research methodology and studies its methodological implications, potential opportunities, advancements, and threats. The study quantifies how useful AI can be in automating complex, broad, and long chain syllogistic operations and computations, as well as accelerating the predictions and projections in data-intensive domains. The study, which uses a mixed-method design that combines empirical assessments of the literature and practitioner-based case studies of four varied applications of AI in research, namely text analytics, sentiment analysis, climate modelling, and health-care research, finds that the AI-based workflows evaluated by the study are able to achieve significant gains in efficiencies and accuracies with remarkably similar patterns. The results also pose potential concerns, such as algorithmic bias, limited explainability and reproducibility, and other ethical and data-privacy risks. Our research outlines a framework and governance mechanisms to enable the responsible integration of AI to overcome challenges. The research cycle benefits from these mechanisms through the injection of bias mitigation and transparency. This article summarizes some of the key guidelines on the ethical use of AI that ensure transparency and rigor.