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Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education

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  • Seungsu Paek

    (Graduate School of Education, Gachon University, 1342 Seongnam-daero, Sujung-gu, Seongnam-si 13120, Gyeonggi-do, Korea)

  • Namhyoung Kim

    (Department of Applied Statistics, Gachon University, 1342 Seongnam-daero, Sujung-gu, Seongnam-si 13120, Gyeonggi-do, Korea)

Abstract

In today’s world, artificial intelligence (AI) and human intelligence coexist, and no field is free from the impact of AI. At present, education cannot be discussed without mentioning AI, which has an omnidirectional impact on all its areas, including the purpose, content, method, and evaluation system. This study aimed to explore the future direction of education by examining the current impact and predicting future impacts of AI. It also examined research trends and collaboration status by country through network analysis, topic modeling and global research trends in AI in education (AIED), by applying the Latent Dirichlet Allocation algorithm. Over the past 20 years, the number of papers on AIED has steadily increased, with a dramatic rise since 2015. The research can be broadly classified into eight topics, including “changes in the content of teaching and learning.” Using a linear regression model, three hot topics, two cold topics and trend changes for each research topic were identified. The study found that AIED research should be more thematically diversified and in-depth; this directly applies AI algorithms and technologies to education, which should be further promoted. This study provides a reference for exploring the direction of future AIED research.

Suggested Citation

  • Seungsu Paek & Namhyoung Kim, 2021. "Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7941-:d:595337
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    References listed on IDEAS

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    1. Jee Hoon Lee & Jacob Wood & Jungsuk Kim, 2021. "Tracing the Trends in Sustainability and Social Media Research Using Topic Modeling," Sustainability, MDPI, vol. 13(3), pages 1-19, January.
    2. Seongyoun Hong & Taejung Park & Jaewon Choi, 2020. "Analyzing Research Trends in University Student Experience Based on Topic Modeling," Sustainability, MDPI, vol. 12(9), pages 1-11, April.
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    5. Hakyeon Lee & Pilsung Kang, 2018. "Identifying core topics in technology and innovation management studies: a topic model approach," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1291-1317, October.
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    Cited by:

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    2. I-Cheng Chang & Tai-Kuei Yu & Yu-Jie Chang & Tai-Yi Yu, 2021. "Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    3. Hsin-Yu Kuo & Su-Yen Chen & Yu-Ting Lai, 2021. "Investigating COVID-19 News before and after the Soft Lockdown: An Example from Taiwan," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    4. Sunghwan Hwang, 2022. "Examining the Effects of Artificial Intelligence on Elementary Students’ Mathematics Achievement: A Meta-Analysis," Sustainability, MDPI, vol. 14(20), pages 1-18, October.

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