중등영재 물리 사사과정에서 생성형 AI를 활용한 교수-학습 방법 고찰

초록

The recent educational application of generative AI is fundamentally transforming traditional teaching-learning methods, particularly offering an innovative turning point in cultivating scientific thinking and creative problem-solving skills, core objectives of science education for the gifted. This study explored how generative AI can be utilized explicitly in secondary gifted education mentorship programs and empirically demonstrated that this can facilitate learners' thinking and redefine the role of instructors. To this end, this study examined the research project "Analysis of Generational Acoustic Characteristics in K-pop Idol Music," conducted within the secondary physics mentorship program at G University's Science Gifted Education Center. The study employed an analytical framework adapted from the ‘AI co-scientist’ system, aligning its theoretical stages with the scientific inquiry process to analyze student activities. The research findings indicated that gifted students strategically utilized various generative AIs in each stage of literature search, inquiry design, customized tool development, data analysis, and result synthesis, thereby implementing a practical ‘AI co-scientist’ agent through functional differentiation and integration. Notably, the prominent features were eliciting expert-level AI responses through structured agentic workflow techniques and developing customized analytical tools through vibe coding. This demonstrates that generative AI is an effective educational tool for enhancing gifted students' higher-order thinking skills and inquiry abilities as a cognitive partner, and we expect it to provide a primary guideline for developing learner-AI-instructor collaboration models and scientific inquiry AI agents in the future.

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