Exploring the AI Topic Composition of K-12 Using NMF-based Topic Modeling

HoSung Woo, JaeHong Kim, JaMee Kim, WonGyu Lee


Recently, artificial intelligence has become more prevalent due to the combination of more data, faster processing power, and more powerful algorithms. AI technology has been introduced into almost all industries and is also affecting the education sector. The objective of this study was to explore AI topics through an analysis of literature related to AI education for grades K-12 and provide implications for the composition of a system for AI education. For this purpose, 27 materials released at the 2018 and 2019 AI4K12 Symposiums were collected. Besides, artificial intelligence integration across subjects and artificial intelligence curriculum published by CBSE of India were collected for analysis. The frequency of words, word cloud, and topic modeling was performed for each collected document. According to the analysis, content on the necessary future direction for AI education and introductions to educational tools were extracted from the 2018 symposium, whereas the 2019 symposium contained more concrete discussions on how to conduct AI education in schools. Meanwhile, content involving the principles of integration for how to integrate AI with other subjects and AI-based teaching and learning methods were extracted from Artificial Intelligence Integration Across Subjects. Finally, Artificial Intelligence Curriculum covered the theories and principles of AI. This study has significance in that it analyzed how much discussion about AI education is being conducted in K-12 based on topic modelling and suggested future directions for AI education.


K-12 AI curriculum; AI curriculum; topic model; topic analysis.

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DOI: http://dx.doi.org/10.18517/ijaseit.10.4.12787


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