Generative AI Readiness in Teacher Education: A Mixed-Methods Study of Pre-Service Teachers’ Competencies, Strategies, and Support Needs

As Generative AI tools become more embedded in educational practice, teacher education faces urgent questions about readiness, ethical use, and pedagogical integration. This demands both robust measurement tools and deeper understanding of pre-service teachers’ evolving relationships with GenAI technologies. This cross-sectional mixed-methods study, conducted between April 2024 to March 2025, investigated Finnish pre-service teachers’ (PST) GenAI readiness during the early stages of adoption. The study has three aims: (1) to validate the GenAI Readiness instrument by combining data from a previously published pilot study (April - June 2024; N = 56; Bui et al., 2025) and a subsequent data collection (October 2024- March 2025; N = 114), yielding a total sample of N = 170; (2) to examine participants’ experiences, challenges, coping strategies and support needs through thematic analysis of open-ended responses; and (3) to integrate quantitative and qualitative findings using joint displays and meta-inferences, through which qualitative insights helped explain and contextualize quantitative patterns. Findings suggest that GenAI readiness is both a measurable psychological construct and a developmental experience. Usage frequency emerged as the strongest predictor of readiness dimensions; perceived accuracy concerns persisted regardless of users’ confidence or experience level. Most importantly, PSTs demonstrated operational fluency with GenAI tools while showing limited metacognitive and self-regulated learning applications, indicating that hands-on experience alone does not cultivate the critical judgment required for a responsible use of GenAI. These patterns point to a broader reconceptualization of human-AI interaction in education: GenAI is increasingly positioned as a collaborative partner for cognitive exploration rather than an authoritative source, demanding ongoing critical evaluation rather than passive consumption. Implications highlight the need for teacher education programs to move beyond technical and procedural training toward cultivating evaluative judgment, structured verification practices, and the capacity to model critical, reflective GenAI engagement for students.