From play to practice: unpacking preschool teachers’ adoption of robotic toys through a multi-framework perspective
Year: 2026
Type: article
Study design: Observational Study
Abstract: The incorporation of robotic toys in early childhood education has the potential to enrich learning through play, foster cognitive and social development, and lay the groundwork for foundational STEM skills. Yet, despite increasing recognition of their educational value, the integration of robotic toys by preschool teachers remains limited and inconsistent. This study adopts an integrated approach, combining the Task Technology Fit (TTF) and Theory of Planned Behavior (TPB) frameworks, to systematically investigate the factors influencing preschool teachers’ intentions to adopt robotic toys in classroom practice. Drawing on survey data from 742 preschool teachers across diverse institutional contexts, the study employs structural equation modeling to evaluate both technological and behavioral predictors of teachers’ intention to adopt robotic toys. The results reveal that both task-technology alignment, reflecting the perceived fit of robotic toys with play based and collaborative learning goals, and psychological constructs such as attitude, subjective norm, and perceived behavioral control, significantly shape teachers’ adoption intentions. Task technology fit emerged as a particularly strong driver, highlighting the importance of designing robotic toys that resonate with early childhood pedagogies. In addition, social encouragement and teachers’ confidence in their technological abilities play vital roles in enabling adoption. The study advances theoretical understanding by illustrating the synergistic influence of technological and behavioral determinants on teachers’ intention to adopt robotic toys, and offers practical recommendations for policymakers, curriculum designers, and educational technology developers seeking to promote meaningful and sustainable integration of robotics in preschool education.
Author: Ayşegül Şakir
Institution: Ahi Evran University
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
Estimated APC paid: $3,590
Funder: Ahi Evran Üniversitesi
OpenAlex ID: W7128492946
