Meta-synthesis of global cases for the adaption stages of artificial intelligence systems into university practices

Abstract The transformative potential of Artificial Intelligence (AI) in universities is simply extending beyond instructional applications to include administrative and strategic domains in university management. This study aims to explore how AI could be integrated into university practices by addressing three core questions: to what AI technologies are being employed, which integration stages support their implementation, and what kind of ethical risks need to be considered for AI usage in universities. To respond to these questions, the researchers a meta-synthesis approach to qualitatively analyse selected studies. Based on a systematic search protocol, they selected 49 empirical cases from different parts of the world; then, analysed them using thematic analysis through data reduction, data display, and conclusion drawing/verification. The findings were categorised into a three-layered framework capturing various purposes of AI usage, technical and institutional integration steps, and the components of ethical AI regulations in universities. The analysis shows that higher education authorities, university leaders, and techno-experts initially decide the purpose(s) of AI usage, regarding teaching, research, engagement, and administration. Then, as a structured roadmap, they can follow eight steps identified for the technical and institutional AI integration: 1. AI System Design, 2. AI Algorithm Development, 3. Data Management-Preprocessing, 4. AI System Integration, 5. Strategic Management Policies, 6. Operational Management Roles, 7. Curriculum-Pedagogical Design, and 8. Evaluation-Performance Monitoring. Lastly, ethical regulations could be organised combining sub-themes of Data Ethics, Algorithmic Ethics, Human-AI Interaction and Social Impact, Academic Integrity and Responsible Use, and Institutional and Managerial Ethics.