The rapid rise of generative AI (GenAI) presents a critical challenge for teacher education, which must now determine how to effectively prepare educators from diverse professional backgrounds to integrate these new tools. This study addresses this challenge by investigating the contrasting mindsets and integration patterns of in-service and pre-service teachers. Using a mixed-methods survey, we collected data on technology usage, self-perceived competence, and perceived barriers from 288 participants pursuing early childhood education (ECE) degrees in Hong Kong, comparing 118 experienced in-service teachers studying via distance learning with 170 pre-service students in a full-time programme. Findings reveal two distinct adoption mindsets. The in-service cohort was characterised by a pragmatic approach, with users in this group engaging in more intensive and frequent use of GenAI to solve immediate classroom problems, such as generating lesson plans and defining learning objectives. In contrast, the pre-service group exhibited a pattern of broader, more exploratory, but less frequent use. A key paradox emerged: for in-service teachers, optional, need-driven IT training was significantly associated with higher self-perceived competence and subsequent technology application. Conversely, the mandatory course for pre-service students showed no such significant association. The groups also cited different primary barriers: a lack of time for practising teachers versus the perceived complexity of the technology for students. The study concludes that effective technology integration hinges not on standardised mandates, but on context-aware, problem-centred support tailored to the distinct professional realities of teachers.

Authors:
Jessie Ming Sin Wong, Hong Kong Metropolitan University, Hong Kong

About the Presenter(s)
Dr. Jessie Wong leads a range of Early Childhood Education programs at Hong Kong Metropolitan University (HKMU).

Connect on Linkedin
https://www.linkedin.com/in/jessie-ming-sin-wong/

Connect on ResearchGate
https://www.researchgate.net/profile/Jessie-Ming-Sin-Wong

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