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<p>This study synthesizes generative AI policies from academic associations, publishers, and federal funding agencies to support researchers in navigating the ethical and practical implications of AI use in academic writing and proposal development. Focusing on the field of educational research, the study reviewed 27 policy documents from 20 entities, including 4 from academic associations, 21 from publishers, and 2 from federal funding agencies. The analysis identified seven key themes: Disclosure, Authorship, Information Verification, Scope of Generative AI, Peer Review Process, Intellectual Property, and Bias. Among these, disclosure and authorship were most frequently addressed, emphasizing transparency and human accountability. The findings also highlight that reviewers and editors who handle unpublished work and proprietary ideas have a critical responsibility to avoid uploading such materials into generative AI platforms, as doing so may violate confidentiality agreements and expose sensitive intellectual property to potential misuse or unauthorized redistribution. Based on these insights, the study provides practical recommendations to guide responsible AI use in research and publishing. Although the study’s scope centers on educational research, the findings may also offer valuable insights applicable to other social science disciplines and beyond.</p>

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Towards responsible generative AI in academia: a synthesis of AI policies on academic writing in the field of educational research

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Hsien-Yuan Hsu,
  • Abeer Hakouz,
  • Golnar Fotouhi

摘要

This study synthesizes generative AI policies from academic associations, publishers, and federal funding agencies to support researchers in navigating the ethical and practical implications of AI use in academic writing and proposal development. Focusing on the field of educational research, the study reviewed 27 policy documents from 20 entities, including 4 from academic associations, 21 from publishers, and 2 from federal funding agencies. The analysis identified seven key themes: Disclosure, Authorship, Information Verification, Scope of Generative AI, Peer Review Process, Intellectual Property, and Bias. Among these, disclosure and authorship were most frequently addressed, emphasizing transparency and human accountability. The findings also highlight that reviewers and editors who handle unpublished work and proprietary ideas have a critical responsibility to avoid uploading such materials into generative AI platforms, as doing so may violate confidentiality agreements and expose sensitive intellectual property to potential misuse or unauthorized redistribution. Based on these insights, the study provides practical recommendations to guide responsible AI use in research and publishing. Although the study’s scope centers on educational research, the findings may also offer valuable insights applicable to other social science disciplines and beyond.