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

2025-06-25

AI Summary

This study investigates the evolutionary trajectory of knowledge structures and emerging frontiers in Artificial Intelligence in Education (AIED) from 2020 to 2024. Method: Leveraging 2,398 core journal articles, we employ a three-stage knowledge co-occurrence network analysis integrating bibliometrics and thematic clustering to construct dynamic knowledge maps. Contribution/Results: We present the first domain-level characterization of AIED’s paradigm shift in the generative AI era, identifying four key frontiers: large language models, generative AI, multimodal learning analytics, and human–AI collaboration. We uncover a thematic cluster centered on personalized learning, self-regulation, intelligent feedback, assessment, motivation, and ethics. Further, we propose a novel “human-centered, co-adaptive” paradigm for AIED development and trace longitudinal frontier evolution through cross-year bridge keywords. The findings offer theoretical guidance and methodological support for both educational practice and future research.

Technology Category

• Humans and AI: Understanding People, Theories, Concepts and Methods
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Analyze AIED knowledge structure and emerging trends (2020-2024).
• Identify rising frontiers like LLMs and GenAI in education.
• Map AIED's transformation in the era of generative AI.

Innovation

• Knowledge co-occurrence network analysis
• Large language models in education
• Generative AI-driven personalization