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<p>This study explores the policies and regulations governing the use of Generative Artificial Intelligence (GenAI) in New Zealand higher education institutions (HEIs) and provides recommendations for developing policies and guidelines for teaching and learning with GenAI. Using Policy Discourse Analysis (PDA), official GenAI-related policy documents from eight universities in New Zealand were thematically analysed through a combination of inductive and deductive coding. The findings reveal that while most universities adopt an open but cautious approach to GenAI, the completeness of policies varies across institutions. Despite a generally positive sentiment toward GenAI, challenges remain in aligning policies with institutional goals. Thematic analysis identified three key subject positions: students, lecturers, and university learning support, each with distinctive responsibilities and challenges. Policy recommendations for these positions, drawn from the literature, are also presented. Additionally, this study highlights two critical directions for policymaking: fostering collaboration among stakeholders and adopting an adaptable AI integration framework. These findings provide valuable insights into the evolving role of GenAI in HEIs and offer practical guidance for its ethical and effective integration in academic settings.</p>

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Generative Artificial Intelligence in Higher Education: Review of Institutional Policies and Practices across New Zealand

  • 期刊论文

  • 发表日期：

    2025年11月17日

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  • Iqbal Ainur Rizki,
  • Rana Daoud

摘要

This study explores the policies and regulations governing the use of Generative Artificial Intelligence (GenAI) in New Zealand higher education institutions (HEIs) and provides recommendations for developing policies and guidelines for teaching and learning with GenAI. Using Policy Discourse Analysis (PDA), official GenAI-related policy documents from eight universities in New Zealand were thematically analysed through a combination of inductive and deductive coding. The findings reveal that while most universities adopt an open but cautious approach to GenAI, the completeness of policies varies across institutions. Despite a generally positive sentiment toward GenAI, challenges remain in aligning policies with institutional goals. Thematic analysis identified three key subject positions: students, lecturers, and university learning support, each with distinctive responsibilities and challenges. Policy recommendations for these positions, drawn from the literature, are also presented. Additionally, this study highlights two critical directions for policymaking: fostering collaboration among stakeholders and adopting an adaptable AI integration framework. These findings provide valuable insights into the evolving role of GenAI in HEIs and offer practical guidance for its ethical and effective integration in academic settings.