AI 시대, 느린 번역의 교육적 가치: 학습자의 비효율적 성찰 과정과 주체적 성숙의 필요성
Beyond efficiency: The educational value of slow, reflective translation in the age of AI
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The rise of efficient AI systems, including NMT and LLMs, has challenged the traditional value of human translation education. This study contests the prevailing output-efficiency paradigm and argues for the educational significance of the slow, reflective cognitive processes inherent in human translation. Using Piagetian and Vygotskian frameworks, the study analyses screen recordings of undergraduate students translating without machine assistance. Specifically, it examines behavioral indicators, such as pause duration, search frequency, and revision patterns, to identify moments of cognitive disequilibrium and scaffolding attempts. The analysis reveals that these seemingly inefficient behaviors function as critical 'reflective hotspots' where schema restructuring and internalization occur. The findings suggest that translation pedagogy must shift its focus from competing with machine efficiency to cultivating this unique reflective capacity, which underpins high-level MT literacy and critical thinking in the AI era.
Abstract
The rise of efficient AI systems, including NMT and LLMs, has challenged the traditional value of human translation education. This study contests the prevailing output-efficiency paradigm and argues for the educational significance of the slow, reflective cognitive processes inherent in human translation. Using Piagetian and Vygotskian frameworks, the study analyses screen recordings of undergraduate students translating without machine assistance. Specifically, it examines behavioral indicators, such as pause duration, search frequency, and revision patterns, to identify moments of cognitive disequilibrium and scaffolding attempts. The analysis reveals that these seemingly inefficient behaviors function as critical 'reflective hotspots' where schema restructuring and internalization occur. The findings suggest that translation pedagogy must shift its focus from competing with machine efficiency to cultivating this unique reflective capacity, which underpins high-level MT literacy and critical thinking in the AI era.