Artificial intelligence in higher education (AIHE): implications for personalized learning and institutional practices
Jeferson Freitas, Marjorie Mariana de Abreu, Luiz Pereira Pinheiro JuniorPurpose
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.