배색 이미지 스케일 교육에서 있어서 챗지피티 활용도 연구

초록

This study aims to explain what the image color scheme scale, which has been covered in traditional color education, is and to examine how to explain it using AI, and then to explore methods that can be utilized in future color classes. When utilizing AI for instruction on these scales, a reference color system is required to evaluate the utility of the color palettes generated by the AI; the I.R.I. adjective-based color scheme image scale serves as a key standard for this purpose. Images generated by AI prioritize the intended adjective-based image; notably, the overall visual impression remains consistent even when the composition of the three colors is altered. One of AI's strengths is its ability to generate images based on specific prompts; by generating images with uniform color ratios (as done in this study) and analyzing the results, one can simultaneously observe the imagery and colors that align with a given adjective. Consequently, this approach facilitates understanding not only through color palettes but also through the actual color schemes of the generated images. Through AI-integrated color education, students will engage in practical color (matching exercises based on the concept of “adjective-based color schemes”. gaining knowledge that will serve as a valuable foundation for both their daily lives and future learning. Incorporating methods and validated findings from AI research into the curriculum) moving beyond traditional classes that relied on paper printouts and colored paper can enhance learning outcomes. Furthermore, compiling these results will provide students with high-quality educational resources. Ultimately, this research is expected to help university-level color education evolve beyond its traditional, theory-centric roots; by integrating smart media and innovative content, it aims to broaden the educational horizons of the digital era.

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