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<p>The rapid proliferation of artificial intelligence (AI) across professional and educational domains has intensified ethical discourse, yet much of the existing literature remains anchored in abstract principles, policy prescriptions, or technical safeguards. Less is known about how AI ethics is articulated in relation to professional practice, contextual judgement, and interpretive decision-making across applied fields. Addressing this gap, the present study adopts a descriptive and evaluative bibliometric approach to examine the evolution, intellectual structure, and practice orientation of AI ethics research. Using bibliographic metadata retrieved from the Scopus database, 282 peer-reviewed journal articles and conference papers published between 2009 and 2025 were analysed. Citation indicators, publication trends, subject-area distributions, and keyword co-occurrence networks were generated using biblioMagika<sup>®</sup> and VOSviewer to map growth patterns, dominant sectors, and thematic emphases. The findings reveal a pronounced post-2021 expansion of AI ethics research, accompanied by a disciplinary shift toward applied domains such as social sciences, education, healthcare, and management. Keyword network analysis indicates that AI ethics is increasingly framed around professional judgement, trust, human–AI collaboration, and interpretive practice rather than solely around technical compliance or regulatory frameworks. Although healthcare accounts for the highest citation impact, education emerges as a conceptually important context where ethical questions of intelligence, agency, fairness, and professional responsibility are actively negotiated. Highly cited publications consistently foreground the role of human judgement, human-in-the-loop decision-making, and contextual reasoning, suggesting a reorientation of AI ethics toward practice-based and relational understandings. Overall, this study positions AI ethics research as an evolving practice-centred field and highlights the importance of interpretive and professional perspectives, particularly within education, thereby supporting ethically grounded AI integration.</p>

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From abstract ethics to situated practice: a bibliometric analysis of AI ethics and professional judgement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月23日

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  • Chamil Arkhasa Nikko Mazlan,
  • Muhammad Atiullah Othman,
  • Ahmad Rithaudin Md Noor,
  • Surasak Jamnongsarn,
  • Riyan Hidayatullah

摘要

The rapid proliferation of artificial intelligence (AI) across professional and educational domains has intensified ethical discourse, yet much of the existing literature remains anchored in abstract principles, policy prescriptions, or technical safeguards. Less is known about how AI ethics is articulated in relation to professional practice, contextual judgement, and interpretive decision-making across applied fields. Addressing this gap, the present study adopts a descriptive and evaluative bibliometric approach to examine the evolution, intellectual structure, and practice orientation of AI ethics research. Using bibliographic metadata retrieved from the Scopus database, 282 peer-reviewed journal articles and conference papers published between 2009 and 2025 were analysed. Citation indicators, publication trends, subject-area distributions, and keyword co-occurrence networks were generated using biblioMagika® and VOSviewer to map growth patterns, dominant sectors, and thematic emphases. The findings reveal a pronounced post-2021 expansion of AI ethics research, accompanied by a disciplinary shift toward applied domains such as social sciences, education, healthcare, and management. Keyword network analysis indicates that AI ethics is increasingly framed around professional judgement, trust, human–AI collaboration, and interpretive practice rather than solely around technical compliance or regulatory frameworks. Although healthcare accounts for the highest citation impact, education emerges as a conceptually important context where ethical questions of intelligence, agency, fairness, and professional responsibility are actively negotiated. Highly cited publications consistently foreground the role of human judgement, human-in-the-loop decision-making, and contextual reasoning, suggesting a reorientation of AI ethics toward practice-based and relational understandings. Overall, this study positions AI ethics research as an evolving practice-centred field and highlights the importance of interpretive and professional perspectives, particularly within education, thereby supporting ethically grounded AI integration.