Abstract—This study aims to examine the definition and
attributes of artificial intelligence (AI) thinking to support AI
education, so educators can determine how such education
should be conducted in grades K–12. The text mining method
was conducted using text crawling and co-word analysis to
design and define AI thinking using the Python programming
language. The cosine similarity and word2vec techniques were
used to perform co-word analysis. Cosine similarity extracts
paired words by assigning a weight according to the frequency
of appearance. The skip-gram of word2Vec examines the
surrounding words and predicts the paired words. According to
the co-word analysis results, AI thinking is using an integrated
thinking process to solve decision problems by discussing,
providing, demonstrating, and proving processes. Moreover, AI
thinking must be considered in future research on AI education.
This study aims to serve as the foundational research to move
forward in AI education.
Index Terms—Artificial intelligence (AI) thinking, co-word
analysis, computer science, text crawling, cosine similarity,
skip-gram.
The author is with the Department of Computer Education, Seoul
National University of Education, South Korea (e-mail: skshin@snue.ac.kr).
Cite: Seungki Shin, "A Study on the Framework Design of Artificial Intelligence Thinking for Artificial Intelligence Education," International Journal of Information and Education Technology vol. 11, no. 9, pp. 392-397, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).