Personalized Learning in Action: Exploring AI and Robotics for Early Childhood Education

Abstract

The lack of personalization in early education can often leave students with weak foundational skills, causing said students to be behind in their studies. Personalized learning, the idea of tailoring a unique lesson plan to a student, has been shown to improve the understanding of content learned. Robots utilizing personalization techniques in educational settings, coined social robots, have been able to form a connection with students, thereby keeping them engaged while learning. This proposal seeks to study the effects of AI-driven social robotic tutors coupled with personalized learning on early childhood education. The study will consist of five groups of K-4 students: two groups learning while utilizing both a social robot and a tablet (one group with personalized learning and the other without), the two groups interacting with only the tablet (with and without personalization), and the last group learning utilizing both a non-personalized learning tablet and a non-social robot. This study aims to determine whether the combination of robotic interaction and personalized learning leads to better outcomes than solely tablet-based or non-personalized methods. This study will focus on teaching mathematics to the participants. Pre and post-tests will measure learning progress, and the influence of robot interaction on student engagement will also be evaluated. It is expected that the students with social robotic tutors and personalized learning tablets will show the greatest knowledge retention, outperforming all other categories. These findings could have significant implications for the integration of AI and robotics in early education, potentially revolutionizing how personalized learning is implemented therefore improving educational outcomes for young learners.