Utilization of Generative AI in Research and Academic Writing in Medical Education: A Practical Approach from Research Planning to Manuscript Preparation

  • Kasai Hajime
    Department of Medical Education, Graduate School of Medicine, Chiba University, Japan Health Professional Development Center, Chiba University Hospital, Chiba, Japan Department of Respirology, Graduate School of Medicine, Chiba University, Chiba, Japan Research Field of Health Professions Education, Graduate School of Medicine, Gifu University

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  • 医学教育の論文執筆における生成AIの活用 : 研究計画立案から論文執筆までの実践的アプローチ
  • イガク キョウイク ノ ロンブン シッピツ ニ オケル セイセイ AI ノ カツヨウ : ケンキュウ ケイカク リツアン カラ ロンブン シッピツ マデ ノ ジッセンテキ アプローチ

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In academic research and writing, generative artificial intelligence (gAI) is becoming an increasingly useful tool. gAI can help researchers work more efficiently, think creatively, and manage different tasks throughout the research process. This review presents the basic functions of gAI and how it can be used in various types of research, including medical education research-from developing research questions and reviewing previous studies to analyzing data, writing drafts, proofreading, and translating texts. Understanding how these tools work and using them carefully can be greatly beneficial for researchers. For example, better results can be derived by using well-designed prompts and critically checking the gAI's output. However, important concerns also exist. gAI can sometimes produce information that sounds correct but is, in fact, wrong. Excessive reliance on gAI may also weaken important writing and thinking skills. In addition, questions concerning fairness, responsibility, and proper use need to be addressed. Clear rules and responsible use of gAI tools are essential. gAI should not replace human judgment or academic standards. However, if used thoughtfully, it can help improve the quality of writing and make research work more productive. It may also support researchers in Japan in sharing more of their work internationally. As medical education continues to develop, gAI has the potential to support better research practices-if it is used with care and a strong sense of responsibility.

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