Establishing ethical standards for GenAI in university education: a roadmap for academic integrity and fairness

Abstract

Purpose The increasing adoption of generative artificial intelligence (GenAI) tools in university education has raised significant ethical concerns regarding academic integrity and fairness. This study aims to address these concerns by reviewing existing models and frameworks for ethical GenAI use and proposing a preliminary roadmap to establish ethical standards for GenAI use in higher education. Design/methodology/approach This study reviews current models and frameworks for ethical GenAI use, identifying their strengths and limitations. Based on this literature review and an approach combining interpretative phenomenological analysis and a hybrid phenomenological qualitative method, a six-phase roadmap is proposed, consisting of awareness and understanding, policy development, curriculum integration, technology and infrastructure, continuous evaluation and adaptation and collaboration and outreach. Findings This paper emphasizes the need for clear policies, interdisciplinary curriculum integration, robust technological infrastructure and ongoing stakeholder collaboration. Practical recommendations are provided for each phase of the roadmap, offering strategic guidance for universities to navigate the ethical complexities of GenAI implementation. Originality/value The proposed roadmap serves as a foundational step for developing policies and guidelines that ensure GenAI supports academic integrity and fosters innovative learning environments. Future research will focus on empirical validation and refinement of the roadmap to enhance its applicability and effectiveness in diverse educational contexts.