불교 교양교육의 자기성찰 역량과 AI시대 인성교육의 정렬

초록

Contemporary learning environments shaped by digital overstimulation and generative AI demand that students develop not only self-regulation,‌empathy,‌ and responsibility,‌ but also critical AI literacy―including the ability to examine evidence,‌bias,‌and limitations in AI-mediated outputs.‌Yet Seon/ Buddhist practice (e.g.,‌mindfulness,‌observation,‌compassion),‌character education (e.g.,‌ self-management,‌empathy,‌responsibility),‌ and AI literacy (e.g.,‌metacognition,‌ critical evaluation,‌digital empathy) are often discussed and assessed in fragmented frameworks,‌making it difficult to design coherent classroom experiences and evaluative criteria.‌This study proposes the SCA (Seon Character AI) model,‌which aligns these domains through a shared procedural cycle of Attention Reflection Care Responsibility.‌ The model articulates three prototype alignment pathways―mindfulness self-management metacognition;‌ observation empathy critical AI literacy;‌and compassion responsibility digital empathy while explicitly allowing cross-links among components in actual learning contexts. To enhance instructional usability, SCA is operationalized as (1) a four-verb design language that integrates goals, activities, and assessment; (2) a repeatable in-class micro-cycle of learning activities; and (3) an analytic rubric with observable indicators for performance, character competencies, and AI use. As an initial plausibility check for the model’s alignment logic, the study references a cross-sectional K-SRIS survey (n = 278) reported in prior research on Buddhist liberal arts courses, where students who perceived the courses as helpful exhibited higher mean scores across self-reflection subscales (self-awareness, self-design, self-regulation, and self-examination). Given the limitations of cross-sectional data, these findings are interpreted as associative evidence rather than causal effects. Overall, the study provides a conceptual and methodological bridge that translates integrative discussions into actionable classroom design and assessment in the AI era.

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