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Supporting Preschool Emotional Development with AI-Powered Robots

Santiago Berrezueta-Guzman, María Dolón-Poza, Stefan Wagner

TL;DR

This paper investigates the use of AI-powered robots to support preschool emotional development, focusing on emotional self-regulation, engagement, and collaboration. Using a ten-week, two-group design, the study combines an open-source robot architecture with real-time emotion analytics and a cloud-backed data pipeline to deliver adaptive interactions and collect multi-stakeholder data. Results show early improvements in emotion recognition for immediate robot exposure, with sustained robotic use yielding stronger collaborative skills and engagement; parental and teacher feedback further validate acceptance and feasibility. The work highlights the potential for scalable, inclusive adoption of AI-assisted robotics in early education, while addressing ethical, equity, and training considerations and outlining directions for broader, long-term studies.

Abstract

This study evaluates the integration of AI-powered robots in early childhood education, focusing on their impact on emotional self-regulation, engagement, and collaborative skills. A ten-week experimental design involving two groups of children assessed the robot's effectiveness through progress assessments, parental surveys, and teacher feedback. Results demonstrated that early exposure to the robot significantly enhanced emotional recognition, while sustained interaction further improved collaborative and social engagement. Parental and teacher feedback highlighted high acceptance levels, emphasizing the robot's ease of integration and positive influence on classroom dynamics. This research underscores the transformative potential of AI and robotics in education. The findings advocate for the broader adoption of AI-powered interventions, carefully examining equitable access, ethical considerations, and sustainable implementation. This work sets a foundation for exploring long-term impacts and expanding applications of AI in inclusive and impactful educational settings.

Supporting Preschool Emotional Development with AI-Powered Robots

TL;DR

This paper investigates the use of AI-powered robots to support preschool emotional development, focusing on emotional self-regulation, engagement, and collaboration. Using a ten-week, two-group design, the study combines an open-source robot architecture with real-time emotion analytics and a cloud-backed data pipeline to deliver adaptive interactions and collect multi-stakeholder data. Results show early improvements in emotion recognition for immediate robot exposure, with sustained robotic use yielding stronger collaborative skills and engagement; parental and teacher feedback further validate acceptance and feasibility. The work highlights the potential for scalable, inclusive adoption of AI-assisted robotics in early education, while addressing ethical, equity, and training considerations and outlining directions for broader, long-term studies.

Abstract

This study evaluates the integration of AI-powered robots in early childhood education, focusing on their impact on emotional self-regulation, engagement, and collaborative skills. A ten-week experimental design involving two groups of children assessed the robot's effectiveness through progress assessments, parental surveys, and teacher feedback. Results demonstrated that early exposure to the robot significantly enhanced emotional recognition, while sustained interaction further improved collaborative and social engagement. Parental and teacher feedback highlighted high acceptance levels, emphasizing the robot's ease of integration and positive influence on classroom dynamics. This research underscores the transformative potential of AI and robotics in education. The findings advocate for the broader adoption of AI-powered interventions, carefully examining equitable access, ethical considerations, and sustainable implementation. This work sets a foundation for exploring long-term impacts and expanding applications of AI in inclusive and impactful educational settings.

Paper Structure

This paper contains 18 sections, 2 figures.

Figures (2)

  • Figure 1: Architecture of the AI-powered robotic assistant, featuring interactive peripherals (left) and a Raspberry Pi-based system (right).
  • Figure 2: Activity diagram illustrating the data flow process for the AI-powered robot.