Purpose This article critically examines the enduring problems and emerging possibilities of educational research in light of rapid advances in artificial intelligence (AI). It seeks to understand why educational research has struggled to influence practice and policy meaningfully and explores how AI necessitates a fundamental rethinking of research purposes, methods, and epistemologies. Design/Approach/Methods The article adopts a conceptual and critical review approach, drawing on historical, philosophical, and methodological literature. It identifies and analyzes seven major problems in traditional educational research, including flaws in peer review, quantification bias, methodological fragmentation, overgeneralization, neglect of individual learner diversity, limited educational imagination, and narrow outcome measures. It then explores how AI technologies challenge and reshape core assumptions about knowledge production and educational inquiry. Findings Traditional educational research is constrained by outdated paradigms that emphasize generalizability, stability, and typicality at the expense of contextual sensitivity, individual variability, and imaginative possibilities. The rapid evolution of AI further undermines assumptions of stable treatments, linear causality, and human-centered cognition. AI opens new opportunities for participatory, iterative, and systems-oriented research, while also raising ethical and epistemological concerns that demand critical reflection. Originality/Value This article offers a timely and provocative analysis of the limitations of traditional educational research and articulates a vision for its rebirth in the age of AI. It contributes to the growing discourse on paradigm shifts in education by integrating critiques of research orthodoxy with emerging insights into AI-enabled learning. The article calls for methodological pluralism, ethical vigilance, and epistemological innovation, positioning researchers to better respond to the complex and evolving landscape of education in a post-AI world.
Zhao et al. (Tue,) studied this question.