Where is AIED Headed? Key Topics and Emerging Frontiers (2020-2024)

TL;DR

This study maps the evolution of AI in Education from 2020 to 2024, highlighting core themes, emerging frontiers like LLMs and GenAI, and the field's shift towards human-centered, co-adaptive AI applications.

Contribution

It provides the first large-scale field-level mapping of AIED's transformation in the GenAI era using knowledge network analysis.

Findings

AIED remains technically focused with key themes like tutoring systems and NLP.

Emerging frontiers include LLMs, GenAI, multimodal analytics, and human-AI collaboration.

Research interests focus on personalization, self-regulation, feedback, motivation, and ethics.

Abstract

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.