Issues and Challenges of AI Education in Secondary Education

This study aims to systematically analyze the educational issues and challenges arising from the rapid diffusion of artificial intelligence (AI) technologies in secondary education and to derive implications for their future application. To this end, the study synthesizes findings from international and domestic research literature, policy documents, and empirical studies to examine the impacts of AI adoption across multiple dimensions of secondary education, including curricular structure, instructional practices, performance-based assessment, student guidance and counseling, teachers’ professionalism and identity, teachers’ work structure, educational inequality, school facilities and infrastructure, and educational governance. The analysis indicates that AI can serve as an effective cognitive trigger during the exploratory and expansion phases of learning by supporting information access and idea generation. However, when AI intervenes in core stages such as concept formation, value judgment, and meaning construction, it poses a significant risk of weakening students’ cognitive agency and undermining teachers’ educational judgment. In particular, in the domain of performance-based assessment, AI use tends to reinforce outcome-oriented learning, thereby distorting the fundamental purpose of process-oriented evaluation. In student guidance and counseling, the findings further demonstrate that AI technologies cannot replace relational, empathetic, and trust-based educational interventions. Moreover, while AI holds the potential to enhance teachers’ professional capacity by supporting instructional design and learning diagnosis, the analysis reveals a parallel risk: under technology-driven operational models, teachers may be reduced from educational decision-makers to system operators. Based on these findings, this study argues for a paradigm shift in AI integration—from technology-centered implementation to education-centered judgment. It emphasizes the principle that AI should function as a tool for triggering learning processes, while educational judgment and responsibility must remain firmly with teachers. This principle should be institutionally embedded across curriculum design, assessment systems, school operations, and educational governance.