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<p>Artificial Intelligence (AI) is rapidly reshaping higher education. Yet much empirical research still treats ethical governance and pedagogical change as separate issues. This leaves limited understanding of how faculty members bring principles of transparency, fairness, privacy, and accountability into teaching decisions, and how institutional conditions support or restrict deeper AI use. This study examines faculty AI adoption through an ethical, pedagogical, and institutional lens, combining ethical preparedness, SAMR-based pedagogical readiness, and institutional support within a sequential explanatory mixed-methods design. In the quantitative phase, survey data from 460 faculty members were analysed using Latent Profile Analysis (LPA) across ethical preparedness, SAMR-based pedagogical readiness, institutional support, teacher readiness, practical barriers, and future vision. A five-profile solution demonstrated strong classification quality (entropy = .994), with patterns ranging from faculty members with limited engagement to more ethically and pedagogically integrated adopters. The qualitative phase drew on semi-structured interviews with 20 faculty members, analysed through reflexive thematic analysis, to examine why the profiles differed. The findings show that ethical preparedness develops through experience, reflection, and institutional guidance. It also shapes how faculty members judge the pedagogical legitimacy of AI use. More transformative AI integration was most evident when ethical clarity was supported by coherent governance, professional development, and assessment guidance. An aspiration–capacity gap became evident when optimism about AI’s future role and ethical intent were not accompanied by sufficient institutional support, pedagogical capacity, and practical feasibility. The study offers a theoretically grounded account of responsible AI adoption as ethically situated and institutionally mediated. It also highlights the need for ethical governance linked to teaching and assessment, alongside clearer pathways for developing faculty capacity.</p>

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Ethically situated AI integration in higher education: A mixed-methods latent profile analysis of faculty adoption

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Mohammed S. Alwaqdani

摘要

Artificial Intelligence (AI) is rapidly reshaping higher education. Yet much empirical research still treats ethical governance and pedagogical change as separate issues. This leaves limited understanding of how faculty members bring principles of transparency, fairness, privacy, and accountability into teaching decisions, and how institutional conditions support or restrict deeper AI use. This study examines faculty AI adoption through an ethical, pedagogical, and institutional lens, combining ethical preparedness, SAMR-based pedagogical readiness, and institutional support within a sequential explanatory mixed-methods design. In the quantitative phase, survey data from 460 faculty members were analysed using Latent Profile Analysis (LPA) across ethical preparedness, SAMR-based pedagogical readiness, institutional support, teacher readiness, practical barriers, and future vision. A five-profile solution demonstrated strong classification quality (entropy = .994), with patterns ranging from faculty members with limited engagement to more ethically and pedagogically integrated adopters. The qualitative phase drew on semi-structured interviews with 20 faculty members, analysed through reflexive thematic analysis, to examine why the profiles differed. The findings show that ethical preparedness develops through experience, reflection, and institutional guidance. It also shapes how faculty members judge the pedagogical legitimacy of AI use. More transformative AI integration was most evident when ethical clarity was supported by coherent governance, professional development, and assessment guidance. An aspiration–capacity gap became evident when optimism about AI’s future role and ethical intent were not accompanied by sufficient institutional support, pedagogical capacity, and practical feasibility. The study offers a theoretically grounded account of responsible AI adoption as ethically situated and institutionally mediated. It also highlights the need for ethical governance linked to teaching and assessment, alongside clearer pathways for developing faculty capacity.