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

Gurol Yokus ()
Additional contact information
Gurol Yokus: Department of Educational Sciences, Sinop University, 57000 Sinop, Türkiye

Sustainability, 2025, vol. 17, issue 21, 1-35

Abstract: 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.

Keywords: artificial intelligence in teacher education; social and institutional sustainability; automated curriculum; smart content; AI-guided faculty; intelligent tutoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/21/9815/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/21/9815/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:21:p:9815-:d:1787129

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().