| Preserving Critical Thinking in the Age of Large Language Models: The Paradox of Cognitive Load and Efficiency |
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Sangzin Ahn1,2,3 |
1Department of Pharmacology, Inje University College of Medicine, Busan, Korea
2Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
3Cardiovascular and Metabolic Diseases Medical Research Center, Inje University College of Medicine, Busan, Korea |
| 거대 언어 모델 시대 연구자의 비판적 사고 보존 전략: 효율성 뒤에 숨겨진 인지 부하의 역설 |
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안상진1,2,3 |
1인제대학교 의과대학 약리학교실
2인제대학교 의과대학 약물유전체연구센터
3인제대학교 의과대학 심혈관대사질환센터 |
Correspondence:
Sangzin Ahn, Tel: +82-51-890-5909, Fax: +82-893-1232, Email: sangzinahn@inje.ac.kr
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Received: 10 June 2025 • Revised: 25 June 2025 • Accepted: 27 June 2025 |
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| Abstract |
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Rapid advancements in large language models (LLMs) have fundamentally transformed research practices across academic disciplines, with considerable adoption rates among researchers. While empirical studies have demonstrated the substantial positive effects of LLMs on learning outcomes and cognitive performance, these technological advances present a paradoxical challenge in maintaining critical thinking capabilities. LLMs offer unprecedented efficiency in research tasks in literature reviews, analysis, and writing, by significantly reducing task completion time while improving output quality. However, this efficiency stems largely from cognitive offloading, which is the delegation of mental processes to external systems, raising concerns about the potential weakening of human analytical abilities. Cognitive load theory provides a framework for distinguishing between the beneficial reduction of unnecessary cognitive burden and problematic offloading of essential cognitive processes required for deep understanding and critical analysis. Experimental evidence suggests that while LLM users experience reduced cognitive load across multiple dimensions, their critical reasoning performance may suffer compared with traditional search methods. The fundamental challenge lies in balancing the efficiency gains of LLM integration while preserving rigorous analytical thinking. Medical researchers must develop strategic approaches that leverage LLM capabilities while maintaining active engagement with primary sources and complex reasoning tasks. Success lies in recognizing that traditional research methods may represent essential investments in preserving critical thinking skills, suggesting LLM integration involves selective application rather than wholesale cognitive offloading. |
| Key Words:
Large language models; Thinking; Learning; Artificial intelligence; Workload |
| 주제어:
거대 언어 모델; 사고; 학습; 인공지능; 작업 부하 |
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