ScholarWorks@경상국립대학교

초록

Platforms such as YouTube have emerged as crucial tools for information acquisition and learning, but they simultaneously generate various social problems, including the spread of misinformation, deepening social polarization, and the distortion of values. To address these issues, enhancing individual users' critical thinking skills, strengthening media and algorithm literacy education, and cultivating fact-checking capabilities have traditionally been emphasized. However, the inherent characteristics of recommendation algorithm systems raise fundamental questions about the effectiveness of these individual-level response strategies. Accordingly, this study deeply analyzed and critically examined the fundamental limitations faced by three key response strategies — critical thinking, literacy, and fact-checking — in the information environment dominated by recommendation algorithms. The analysis revealed that recommendation algorithms make it structurally difficult for individuals to set algorithms as objects of critical thinking. Furthermore, literacy education focused on enhancing individual capabilities tends to overlook the problems of the structural environment in which algorithms operate, and the environment of hyper-personalized and rapidly distributed information was found to inherently constrain the execution and effectiveness of fact-checking. In conclusion, while individual-level response strategies like critical thinking, literacy, and fact-checking are important in themselves, they cannot be sufficient solutions due to the structural constraints of the recommendation algorithm environment. This study argues that for fundamental problem- solving, structural and systemic approaches — such as ensuring algorithm transparency, strengthening platform accountability, and establishing public regulation and governance — must necessarily be pursued concurrently. The findings of this research are expected to predict potential problems when designing and implementing algorithm-based educational systems like AI Digital Textbooks in the future, and provide important foundational data for seeking more effective educational policy directions and future research tasks that go beyond cultivating individual capabilities.

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