A Multi-Stakeholder Vision for Designing AI-Empowered Teacher Education: Exploring Key Components for Sustainable Institutional Change

This research investigates various stakeholder perspectives on AI-powered teacher education, focusing on its potential benefits, strengths, and limitations for integrating this promising technology into a sustainable educational future. It was designed as an exploratory mixed-methods study. It involved five distinct groups: curriculum developers in teacher-training institutions, artificial intelligence experts, department heads and deans in education faculties, private sector managers in teacher-training companies, and over 500 pre-service teachers. The findings reveal promising smart opportunities that AI offers for reimagining teacher training, contributing to the social and long-term institutional sustainability of teacher education. Key components of AI-powered teacher education identified include Intended use of AI in teacher education context, Machine learning with data monitoring, AI-human interaction in teacher training, AI-powered feedback for better faculty management, and critically, Digital vision, risks, and AI ethics for responsible and sustainable implementation. Prominently stressed codes within these themes include AI readiness, automated teacher education curriculums, a new recruitment system, designing AI-guided smart faculties, measuring on-entry skills, identifying risky pre-service teachers, improving teachers' assessment capacity, creating smart content, and criticisms over its value. The results of the multiple regression analysis demonstrate that curiosity about AI use has the strongest impact on pre-service teachers' openness and readiness for AI-empowered teacher education, followed by institutional AI support. The research concludes by implicitly calling for a holistic and ethical strategy for leveraging AI to prepare educators to successfully navigate the demands of a sustainable future.