Artificial intelligence in higher education (AIHE): implications for personalized learning and institutional practices

Purpose This study addresses the following research question: What are the implications of artificial intelligence (AI) for Personalized Learning and Online Higher Education (OHE)? Design/methodology/approach A qualitative study was conducted using semi-structured interviews including faculty members and higher education administrators. The data were analyzed to identify the adoption factors, challenges, opportunities, and ethical considerations. An Integrative Model of Adopting AI in Online Higher Education (IMAOHE) was developed based on the empirical findings. Findings AI supports personalized learning through adaptive assessment, real-time feedback, learning analytics, and tailored content. The reported challenges include ethical dilemmas, institutional barriers, and the need to preserve teacher autonomy. Research limitations/implications Limited respondents and lack of student perspectives suggest future research with larger, more diverse samples and mixed-methods or longitudinal designs. Practical implications The model guides institutions, educators, and developers to integrate AI ethically and strategically, ensuring meaningful, context-aware learning. Social implications AI can enhance equity, inclusion, and student-centered learning while maintaining human mediation and cultural sensitivity. Originality/value This study provides an empirically grounded model linking AI adoption to pedagogical personalization in OHE, offering actionable insights for ethical and effective AI integration.