A Study on the Framework Design of Artificial Intelligence Thinking for Artificial Intelligence Education
Seungki Shin
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.