Mapping the evolution of AI in education: Toward a co-adaptive and human-centered paradigm

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This study analyzes 2398 research articles published between 2020 and 2024 across eight core venues related to the field of Artificial Intelligence in Education (AIED). Using a three-level knowledge co-occurrence network analysis, this study analyzes the knowledge structure of the field, the evolving knowledge clusters, and the emerging frontiers. The findings reveal that AIED research is centered on developing AI-assisted systems and using AI to support educational analysis, with sustained themes such as intelligent tutoring systems, learning analytics, and natural language processing, alongside rising interest in large language models (LLMs) and generative artificial intelligence (GenAI). By tracking the bridging keywords over the past five years, this study identifies four emerging frontiers in AIED, including LLMs, GenAI, multimodal learning analytics, and human-AI collaboration. The current research interests in GenAI are centered around GAI-driven personalization, self-regulated learning, feedback, assessment, motivation, and ethics. Our findings underscore the need to consciously shape these technical pursuits through co-adaptive and human-centered principles. It is essential to proactively bridge these advanced technical capabilities with core educational values and purposes to ensure that technological development is guided by educational goals, ethics, and a commitment to human agency and education equity. This study provides a large-scale field-level mapping of AIED's transformation in the GenAI era and sheds light on the future research development and educational practices.