Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors

Youngseok Lee - KNU College of Liberal Arts and Sciences, Kangnam University, 40 Gangnam‑ro, Giheung‑gu, Yongin‑si, Gyeonggi‑do, 16979, South Korea
Jungwon Cho - Department of Computer Education, Jeju National University, 102 Jejudaehak‑ro, Jeju‑si, Jeju‑do 63243, South Korea

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In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. AI is being rapidly applied across industries and is developing as a core technology to enable a society led by knowledge and information. An AI education focused on problem solving and learning is efficient for computer science education. Thus, the time has come to prepare for AI education along with existing software education so that they can adapt to the social and job changes enabled by AI. In this paper, we explain a classification method for AI machine learning models and propose an AI education model using teachable machines. Non-computer majors can understand the importance of data and the AI model concept based on specific cases using AI education tools to understand and experiment with AI even without the knowledge of mathematics, and use languages such as Python, if necessary. Through the application of the machine learning model, AI can be smoothly utilized in their field of interest. If such an AI education model is activated, it will be possible to suggest the direction of AI education for collaboration with AI experts through the application of AI technology.


Artificial intelligence; education model; classification techniques; learning strategy; teachable machine.

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