Date
Publisher
arXiv
In this study, we analyze 2,398 research articles published between 2020 and
2024 across eight core venues related to the field of Artificial Intelligence
in Education (AIED). Using a three-step knowledge co-occurrence network
analysis, we analyze the knowledge structure of the field, the evolving
knowledge clusters, and the emerging frontiers. Our findings reveal that AIED
research remains strongly technically focused, 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, we identify four emerging frontiers in AIED--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.The key
research interests and emerging frontiers in AIED reflect a growing emphasis on
co-adaptive, human-centered AI for education. This study provides the first
large-scale field-level mapping of AIED's transformation in the GenAI era and
sheds light on the future research development and educational practices.
Study at a glance
- Question: How has the knowledge structure of artificial intelligence in education evolved, and what are its emerging research frontiers?
- Setting and data: 2,398 research articles across eight core artificial intelligence in education publication venues, 2020 to 2024.
- Method: Keyword co-occurrence network analysis and qualitative content analysis.
Why it matters
Prior reviews often miss the full breadth of educational artificial intelligence research by using limited keyword searches, leaving a need for large-scale mapping of recent developments during the rapid rise of generative artificial intelligence.
Key findings
- Large language models, generative AI, multimodal learning analytics, and human-AI collaboration emerged as four key research frontiers bridging the field between 2020 and 2024.
- Artificial intelligence in education research maintained strong technical continuity, with sustained core clusters around natural language processing, intelligent tutoring systems, learning analytics, and online courses.
- The field's research structure is hierarchical and disassortative, meaning a few dominant technical hub topics connect frequently to diverse, specialized, and emerging educational themes.
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Study design
