对比ChatGPT与DeepSeek在教育科研中的表现与体验。
Large Language Models for Education and Research: An Empirical and User Survey-based Analysis
- 通过实验与用户调研，评估两模型在教学科研中的表现
- ChatGPT文本理解更强，DeepSeek编程效率更优
- 两者均能准确诊断疾病并解复杂数学题，适合教育研究者使用
预训练大语言模型（LLMs）在多个领域取得显著进展，教育与科研成为其最具影响力的应用方向。当前先进模型如ChatGPT与DeepSeek在数学、科学、医学、文学及编程方面表现突出。本研究通过技术背景分析、实证实验与真实用户调研，全面评估这两款模型在教育与科研场景下的性能。评估涵盖模型准确性、计算效率与用户体验之间的权衡。实验对比了模型在文本生成、编程与专业问题求解方面的表现。结果表明，ChatGPT在通用语言理解与文本生成上表现更佳，而DeepSeek因面向效率设计，在编程任务中优势明显。此外，两款模型均能提供医学诊断的准确输出，并有效解决复杂数学问题。结合定量结果，对师生与研究人员的问卷调查揭示了模型的实际价值与局限，深化了对大模型在教育科研中作用的理解。
原文摘要 · Abstract (English)
Pretrained Large Language Models (LLMs) have achieved remarkable success across diverse domains, with education and research emerging as particularly impactful areas. Among current state-of-the-art LLMs, ChatGPT and DeepSeek exhibit strong capabilities in mathematics, science, medicine, literature, and programming. In this study, we present a comprehensive evaluation of these two LLMs through background technology analysis, empirical experiments, and a real-world user survey. The evaluation explores trade-offs among model accuracy, computational efficiency, and user experience in educational and research affairs. We benchmarked these LLMs performance in text generation, programming, and specialized problem-solving. Experimental results show that ChatGPT excels in general language understanding and text generation, while DeepSeek demonstrates superior performance in programming tasks due to its efficiency-focused design. Moreover, both models deliver medically accurate diagnostic outputs and effectively solve complex mathematical problems. Complementing these quantitative findings, a survey of students, educators, and researchers highlights the practical benefits and limitations of these models, offering deeper insights into their role in advancing education and research.
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