Exploring Challenges in Simulation Environments with Socially Assistive Robots for Early Learners

Simulation-based learning has become an essential tool for education, offering interactive and scalable experiences that replicate real-world scenarios. From engineering and Human-Robot Interaction (HRI) to early childhood education, simulations provide cost-effective alternatives to physical environments while enabling real-time adaptation of learning experiences. However, despite their advantages, significant challenges remain in implementing simulations as effective teaching tools. These include limitations in simulation fidelity, engagement retention, adaptability, usability for educators and learners, and computational performance constraints. This paper explores these challenges and discusses solutions for improving simulation-based learning environments. A case study involving a simulated Socially Assistive Robot (SAR) for early childhood learning is presented to highlight these issues, examining engagement levels, task completion rates, and speech recognition limitations. The findings offer insights into optimizing simulation-based learning for better accessibility, adaptability, and educational outcomes.