Version 1
: Received: 23 October 2024 / Approved: 25 October 2024 / Online: 28 October 2024 (07:20:20 CET)
How to cite:
Hijón-Neira, R.; Pizarro, C.; Borrás-Gené, O.; Cavero, S. AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education. Preprints2024, 2024102038. https://doi.org/10.20944/preprints202410.2038.v1
Hijón-Neira, R.; Pizarro, C.; Borrás-Gené, O.; Cavero, S. AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education. Preprints 2024, 2024102038. https://doi.org/10.20944/preprints202410.2038.v1
Hijón-Neira, R.; Pizarro, C.; Borrás-Gené, O.; Cavero, S. AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education. Preprints2024, 2024102038. https://doi.org/10.20944/preprints202410.2038.v1
APA Style
Hijón-Neira, R., Pizarro, C., Borrás-Gené, O., & Cavero, S. (2024). AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education. Preprints. https://doi.org/10.20944/preprints202410.2038.v1
Chicago/Turabian Style
Hijón-Neira, R., Oriol Borrás-Gené and Sergio Cavero. 2024 "AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education" Preprints. https://doi.org/10.20944/preprints202410.2038.v1
Abstract
This study investigates the impact of AI-generated contexts on preservice teachers' computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experience was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18-19 years, who were training in a Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key questions: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement of perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude towards technology integration in education. The results underscore the importance of context and support in fostering both confidence and acceptance of educational technologies among future educators. This research contributes to the growing body of literature at the intersection of AI, robotics, and education, advocating for the integration of AI and robotics into teacher training curricula. Future research should explore the long-term effects of these methodologies on CT skills and teaching practices across diverse educational settings.
Keywords
early childhood education; robotics; computational thinking; Generative AIs; preservice teachers
Subject
Social Sciences, Education
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
