人工智能用于分析化学的学术文本生成:当前风险,指标和对更绿色和更可持续方法的前景
Adrián Fuente-Ballesteros1, Vânia Zuin Zeidler1
1Institute of Sustainable Chemistry, Leuphana University Lüneburg, Lüneburg 21335, Germany.
Analytical chemistry
|February 16, 2026
概括
大型语言模型 (LLM) 在科学写作中提出了新的挑战. 本研究检查了分析化学中人工智能生成的文本,并提出了负责任使用指南,以保持科学完整性.
科学领域:
- 分析化学 分析化学
- 科学写作 科研写作
- 人工智能的人工智能
背景情况:
- 像ChatGPT这样的生成人工智能模型可以产生学术文本,这引发了对著作权和科学记录完整性的担忧.
- 人工智能工具传统上帮助研究人员完成语法检查和数据处理等任务.
研究的目的:
- 研究在分析化学出版物中使用人工智能生成和人工智能辅助文本.
- 识别语言,结构和文献图案模式,表明自动化起草.
- 在科学写作中反思与人工智能相关的风险,包括潜在的误解和偏见的推理.
主要方法:
- 对与人工智能产生的内容相关的模式的期刊提交的分析.
- 对风险和伦理考虑的定性评估.
- 开发人工智能使用的实用指标和最佳实践.
主要成果:
- 经常出现的语言,结构和文献图案可能会在分析化学提交中显示人工智能生成的内容.
- 潜在的风险包括肤浅的内容,科学误解,偏见的推理和完整性失败.
- 该研究强调了在科学出版物中对人工智能的批判性监督的必要性.
结论:
- 对人工智能的负责任,透明和严格监督的使用在分析化学出版物中至关重要.
- 提出了指导方针和指标,以支持研究人员在导航人工智能集成方面.
- 保持科学记录的完整性需要仔细考虑人工智能的作用.
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