Artificial Intelligence for Educational Research

Abstract

Educational research plays a vital role in understanding learning processes, teaching practices, institutional functioning, and policy effectiveness. However, the rapid digitalization of education has generated vast, complex, and heterogeneous datasets that challenge the capacity of traditional research methods. Artificial Intelligence (AI) has emerged as a powerful methodological enabler that expands the analytical scope, efficiency, and depth of educational research without displacing its theoretical and human-centered foundations. This chapter examines the role of AI in redefining educational research across the entire research lifecycle, including research design, data collection, data management, analysis, mixed-methods integration, and policy-oriented inquiry. It highlights how AI techniques such as machine learning, natural language processing, learning analytics, and predictive modeling support the systematic analysis of large-scale quantitative, qualitative, and multimodal data, enabling the identification of complex patterns, longitudinal trends, and actionable insights. The chapter emphasizes that AI functions as an augmentative research tool, enhancing methodological rigor, transparency, and scalability while remaining dependent on human judgment, theoretical grounding, and contextual interpretation. Ethical considerations—such as data privacy, informed consent, algorithmic bias, transparency, and equity—are critically examined to underscore the responsibilities associated with AI-driven research. By situating AI within established epistemological paradigms and educational values, the chapter argues that AI represents an evolutionary advancement rather than a departure from traditional educational research. Ultimately, the integration of AI offers significant potential to strengthen evidence-based practice and policy, provided it is applied critically, ethically, and in alignment with the humanistic purposes of education in the digital age.