Purpose: The present study explores how Artificial Intelligence (AI) is reshaping scientific research by accelerating discovery, improving data analysis, and further transforming research methodologies. It aims to identify the roles AI plays in research, highlight methodological changes, and examine associated ethical concerns. Design/Methodology/Approach: The research utilises secondary data from academic journals, editorial reviews, and case studies across multiple disciplines. The current paper opted content analysis to identify common patterns, benefits, and limitations in AI integration. Findings: The research efficacy today based on AI which uses automation of routine tasks such as hypothesis testing, literature reviews, and simulation modelling. It also supports the peer review process. Machine learning enables predictive modelling, particularly in biomedical and environmental sciences, while large language models assist in summarization and question-answering within academic databases. Although we see, challenges persist, including algorithmic bias, data privacy risks, and limited interpretability of AI systems. Research Limitations/Implications: The present study is literature-based, it lacks primary empirical data. Future research could focus on specific AI applications in real-world research settings and assess long-term impacts. Practical Implications: Research institutions are encouraged to promote AI literacy, invest in transparent and explainable AI systems, and implement ethical frameworks to guide responsible AI usage in research. Originality/Value: This study offers a cross-disciplinary synthesis of AI’s impact on research, highlighting both its transformative potential and the importance of ethical integration for sustainable innovation.
Jain et al. (Wed,) studied this question.