Abstract:

The rapid advancement of generative artificial intelligence is reshaping the foundations of educational research, exposing longstanding methodological and epistemological limitations while introducing new complexities. Traditional educational research has been constrained by inconsistent peer review, quantitative bias and false precision, paradigm wars between qualitative and quantitative traditions, overgeneralization across diverse contexts, neglect of learner individuality, a dominant focus on typical rather than possible educational futures, and an overly narrow definition of educational outcomes. These issues have limited the field’s relevance, validity, and impact. AI amplifies these challenges by accelerating the obsolescence of educational interventions, fundamentally altering the question of what knowledge is of most worth, and transforming learning environments through human–AI collaboration. These developments demand research approaches informed by complexity science, distributed cognition, and sociotechnical critique, particularly in relation to ethics, equity, algorithmic bias, and surveillance. At the same time, AI is transforming literature reviews, research design, and epistemic assumptions, shifting educational research from static causal models toward dynamic, co-evolving systems, and from human-centered interpretation to hybrid human–machine knowledge production. This paper argues that educational research must move beyond existing paradigmatic limitations and embrace more adaptive, participatory, pluralistic, and future-oriented methodologies. Such a transformation represents not merely an improvement but a rebirth of educational research necessary for maintaining scientific rigor, ethical responsibility, and societal relevance in the age of AI.

Key words: artificial intelligence, educational research, distributed cognition, complexity, methodological pluralism, paradigm shift