Generative AI in initial teacher education: Exploring the alignment of perceptions and experiences of pre-service teachers and their teacher educators

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Authors: Siv M. Gamlem, Samantha-Kaye Johnston, Synnøve Moltudal, Joshua A. McGrane, Therese N. Hopfenbeck
Cited by: 10
References: 27

Abstract

As generative artificial intelligence (GenAI) becomes increasingly embedded in education, teacher educators play a pivotal role in modelling its pedagogical use. This mixed-methods study explores how pre-service teachers' perceptions and experiences with GenAI align with or diverge from those of their teacher educators in a Norwegian teacher education programme. Survey and interview data from pre-service teachers (n = 209) and teacher educators (n = 17) reveal that both groups recognise GenAI's potential to enhance efficiency and support learning. However, concerns persist regarding trust, ethical use, and the erosion of core teaching competencies. While experienced GenAI users actively model GenAI integration, novice users express uncertainty and caution. The findings highlight differing perceptions and underscore the need for structured AI literacy development and institutional support. This study emphasises the importance of aligning teacher education practices with evolving technological landscapes to prepare future educators for critical, reflective, and pedagogically grounded GenAI use. • Explores GenAI perceptions among pre-service teachers and teacher educators. • Mixed methods study conducted in a Norwegian teacher education programme. • Finds divergence in GenAI use, confidence, and pedagogical integration. • Identifies need for structured AI literacy in teacher education curricula. • Highlights role of teacher educators in modelling ethical GenAI practices.

Fields of Study & Subjects

Ethics and Social Impacts of AI Artificial Intelligence in Healthcare and Education Explainable Artificial Intelligence (XAI)

Publication details

Journal / source
Teaching and Teacher Education
Publisher
Elsevier BV
Volume
172
Pages
105382–105382
Publication date
14 January 2026
Language
EN