ENTERPRISE AI ANALYSIS
Research status, hotspots and trends of educational artificial intelligence research
This paper presents a visual analysis of 1,398 English documents from the Web of Science core collection to review the research status, hotspots, and trends of educational Artificial Intelligence (AI) from 2015 to 2025. It highlights significant growth in publications since 2019, with China, the US, and Australia leading research output. Key hotspots include educational AI theory, generative AI applications in higher education, and machine learning models. Future trends emphasize deep integration of learning motivation, quality education, AI chatbots, and information literacy.
Key Metrics & Impact
Explore the core statistics driving the conversation around educational AI.
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Total Publications (2015-2025)
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Avg Annual Growth Since 2019
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Top Publishing Country (China)
Deep Analysis & Enterprise Applications
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Research Status
The research on educational AI started late but has seen significant growth, especially after 2019. China, the United States, and Australia are leading in publication volume, with Chinese institutions showing concentrated research output, unlike the more scattered efforts in the U.S. The number of annual publications dramatically increased from 9 in 2015 to 700 in 2024, indicating growing attention to the field.
Initial research was sparse, but a sharp increase in publications is observed post-2019. The Web of Science core collection shows 1398 articles analyzed. Major journals like COMPUT EDUC and EDUC INF TECHNOL are key publication venues. China leads with 426 articles (30.472%), followed by the United States with 332 (23.748%), and Australia with 127 (9.084%). Key Chinese institutions like Chinese University of Hong Kong (37 articles) and Education University of Hong Kong (35 articles) are prominent.
Hotspots
Current hotspots include theoretical and conceptual research in educational AI, applications of generative AI in higher education, and machine learning models for collaborative design. Ethical risks of AI in education are also a growing concern. Emphasis is placed on optimizing educational environments, promoting reform in teaching methods, and enhancing student learning experiences.
Keywords like 'artificial intelligence' and 'education' consistently rank high. 'Higher education' is a major focus, with researchers exploring AI's role in various disciplines and innovative talent training. 'Machine learning' and 'performance' highlight the interest in AI models, deep learning, natural language processing, and emotional computing. Generative AI, exemplified by ChatGPT, is rapidly emerging as a frontier issue, particularly in its implications for learning motivation and quality education.
Trends
Early research (before 2019) focused on professional technical fields such as engineering education and big data. The second phase (2019-2022) shifted to educational application of technology, including learning strategies and intelligent tutoring systems. Since 2023, the emergence of generative AI has intensified research, making large language models and information literacy central to future directions.
The evolution shows three phases: Phase 1 (before 2019): Scattered research focusing on specific technologies like engineering education and big data; 'machine learning' appeared in 2019, indicating early conceptualization. Phase 2 (2019-2022): Broader application focus, with keywords like 'performance,' 'analysis,' and 'systems' gaining prominence, driven by reports like UNESCO's 'AI in Education.' Phase 3 (2023-present): Rapid growth driven by generative AI (ChatGPT), large language models, and AI literacy becoming frontier issues, emphasizing deep integration into learning motivation and quality education.
Key Insight: Publication Growth
700+ Articles Published in 2024 (Highest Annual Count)Educational AI Research Evolution Timeline
Early Applications (Before 2019)
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Educational Tech Integration (2019-2022)
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Generative AI & LLM Focus (2023-Present)
| Aspect | Traditional Education | AI-Enhanced Education |
|---|---|---|
| Personalization | One-size-fits-all |
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| Efficiency | Manual, time-consuming |
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| Insights | Limited, subjective |
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| Accessibility | Resource-dependent |
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Impact of Generative AI in Higher Education
The emergence of tools like ChatGPT has rapidly reshaped higher education. This research highlights its profound impact, enabling more personalized learning experiences and efficient content generation. However, it also brings challenges regarding academic integrity and the need for new pedagogical approaches. Institutions are now focusing on integrating these tools ethically and effectively to enhance student outcomes and prepare them for an AI-driven future.
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