Towards ai-driven socially assistive virtual robots for personalized early childhood education: an investigation of feasibility, usability, and parent-supervised configuration

Abstract

This paper presents an extensive investigation into the adoption of AI-driven Socially Assistive Virtual Robots (SAVRs) in simulation-based environments for early childhood education. By integrating child-focused feasibility data (engagement, speech recognition, success rates) with parent-focused usability data (task configuration challenges, speech misinterpretations, AI clarity), we analyze how eight children (ages 3–4) and ten parents used a ChatGPT-powered simulation framework to practice counting and letter recognition at home.Child results show that 4-year-olds achieved near-perfect task completion with minimal frustration, while 3-year-olds faced more difficulties due to incomplete articulation, shorter attention spans, and repeated speech recognition errors. Parent findings reveal moderate setup complexity (40 described it as “tedious”), frequent manual overrides for speech errors (70 cited speech recognition as a major frustration), and a strong preference (80) for cost-effective simulation over expensive physical SARs, provided usability improvements are made. We explore literature on Socially Assistive Robots (SARs), child-specific speech recognition challenges, AI-based adaptive learning, and prior human-robot interaction (HRI) studies, situating our work in the context of both physically embodied SARs and simulation-based agents that increasingly fulfill similar pedagogical roles [1], [2], [3], [4]. The methodology details the CoppeliaSim environment, ChatGPT-based real-time adaptation, multi-threaded speech orchestration, and the parent configuration interface. The results section includes tables (I–III) summarizing child performance, supplemented with in-depth analysis. We then present a discussion linking child engagement patterns to parent usability concerns, culminating in recommendations for child-specific ASR, multimodal input, short session durations, wizard-style interfaces, and context-aware AI prompts. Future directions explore hybrid physical-virtual usage, specialized acoustic modeling for 3-year-olds, and scaling to larger, more diverse samples. Overall, the study addresses child feasibility and parent usability in a unified manner, underscoring how speech recognition and user-friendly design can support wide-scale implementation of AI-driven SAVRs at home.