Rybakova, Anastasiya Choi, Jongsuk 2026-02-19T07:00:24Z 2026-02-19T07:00:24Z 2026-02-19 2026-01 1861-2776 https://pubs.kist.re.kr/handle/201004/154329 Traditional language instruction has been the dominant approach in early childhood education, yet integrating robotic systems presents new opportunities to enhance second language acquisition. This study introduces a multimodal robot-assisted learning framework featuring the OpenManipulator-X, an interactive robotic system designed to support English acquisition among Korean preschoolers. A comparative experiment was conducted in which children first participated in teacher-led English instruction, followed by robot-assisted learning using task-based interactions, including pick-and-place activities and collaborative drawing. The study evaluates engagement, vocabulary acquisition, and learning effectiveness across both methods. Results indicate that robot-assisted learning fosters greater engagement and interaction, while language retention remained comparable to traditional instruction. In addition, the robot's physical behaviors-such as object manipulation and drawing-contributed to increased attention and participation. The novelty of this work lies in its integration of multimodal interaction combining robotic embodiment, AR-based manipulation, and tablet-guided educational tasks within a unified instructional system. These findings contribute to child-robot interaction (CRI) research, offering a scalable hybrid learning model for early childhood education. Future work should explore AI-driven personalization and long-term developmental impact. English Springer Verlag From traditional to robot-assisted learning: a multimodal robot-assisted learning framework for enhancing english acquisition in korean preschoolers Article 10.1007/s11370-025-00685-z 1 Intelligent Service Robotics, v.19, no.2 Intelligent Service Robotics 19 2 Y scie scopus 001675322500001 2-s2.0-105029007905 Robotics Robotics Article Child-robot interaction Educational robotics Learning second language Multimodal framework