Purpose This study addresses a gap in understanding technology adoption across different teacher education pathways. It comparatively analyzes the use, perceptions, and challenges of integrating generative artificial intelligence (GenAI) and emerging technologies between in-service early childhood education (ECE) teachers studying via open and distance education (ODE) and their pre-service, full-time counterparts. Design/methodology/approach A cross-sectional, mixed-methods design was employed. Data were collected from 288 Bachelor of Education in Early Childhood Education students in Hong Kong, comprising 118 in-service distance learners and 170 pre-service full-time students, via an online survey with quantitative and qualitative items. Findings In-service distance learners demonstrated a pragmatic, intensive use of GenAI for immediate professional tasks, contrasting with the broader, more exploratory use by pre-service students. Self-selected, optional information technology (IT) training was significantly associated with technology application among the distance learning cohort, whereas mandatory training showed no such effect among the full-time cohort. The cohorts also reported distinct primary barriers: lack of time for in-service learners versus technology complexity for pre-service students. Originality/value This study provides the first direct comparative analysis of GenAI integration between in-service ODE and pre-service traditional ECE teacher candidates in Hong Kong. It offers empirical evidence for the value of self-directed, need-based professional development within flexible learning models and highlights the moderating role of professional context and pedagogical beliefs on technology acceptance, challenging one-size-fits-all training approaches. This study underscores the unique potential of ODE platforms not merely as alternative delivery methods, but as primary drivers for immediate, context-driven technological innovation among in-service teachers.
Wong et al. (Mon,) studied this question.