ARTIFICIAL INTELLIGENCE AND DATA SCIENCE IN CONTEMPORARY RESEARCH

Abstract

Artificial intelligence (AI) and data science have emerged as transformative forces in contemporary research, reshaping methodologies across a wide range of academic disciplines. From early expert systems to modern deep learning architectures, AI technologies have expanded researchers’ capacity to analyze complex datasets and generate predictive insights (Russell and Norvig, 2020; Goodfellow et al., 2016). Concurrently, data science has provided robust frameworks for data acquisition, pre-processing, feature engineering and reproducible analysis (Hey et al., 2009; Wickham and Grolemund, 2017). This chapter examines the evolution, applications and methodological implications of AI and data science in contemporary research. It also addresses challenges related to interpretability, reproducibility, ethics and accessibility, emphasizing the importance of responsible and transparent research practices (Floridi and Cowls, 2019; Hutson, 2018). The chapter concludes by arguing that AI and data science should complement rather than replace traditional scientific reasoning, ensuring that technological advancement remains aligned with human values and societal benefit.