Core Insights - The article discusses the challenges faced by educational robots, including limited availability, restricted interaction time, and lack of personalization, which leads to a significant decline in student interest within 1-2 months [1]. Group 1: Challenges in Educational Robotics - Educational robots enhance classroom engagement but are often expensive and limited in number, leading to students sharing them [1]. - The interaction with these robots is confined to classroom hours, resulting in a lack of continuous learning opportunities [1]. - A study indicates that approximately 60% of students lose interest in educational robots after 1-2 months, highlighting the issue of short-term interest decay [1]. Group 2: Proposed Solutions - A research team from Taiwan has introduced an "AI Personalized Robot Framework" that pairs each robot with an AI digital partner to enhance student learning outcomes and engagement [2]. - The framework is based on digital twin technology and large language models (LLMs) to ensure continuous connection and dynamic responses [3]. Group 3: Framework Architecture - The framework consists of three layers: - Infrastructure layer with modular design connecting physical robots to cloud LLM services for scalability [4]. - Data interaction layer that records and analyzes student learning behaviors and preferences to create personalized digital profiles [4]. - Application performance layer allowing students to interact with digital partners via mobile devices, with physical robots serving as their tangible representation [4]. Group 4: Implementation of Personalized Learning Mechanism - The learning model includes two interconnected phases: - An extracurricular preparation phase where students interact with digital partners, earning virtual currency to customize their partners [5]. - A classroom presentation phase where the digital partner's "soul" is transferred to a shared physical robot, enhancing the learning experience [8]. Group 5: Empirical Research and Results - A quasi-experimental study was conducted with 90 students divided into three groups to evaluate the effectiveness of the AI personalized robot system [9]. - After ten weeks, results showed that the experimental group using the AI personalized robot system had significantly better post-test scores, with an effect size of 0.21, indicating substantial educational value [11]. - The experimental group also demonstrated higher levels of ownership and engagement, with increased participation in extracurricular activities compared to the other groups [12][14]. Group 6: Practical Implications - The research provides a feasible path for the large-scale application of educational robots, allowing schools to implement personalized education within limited budgets [14]. - The modular design of the framework allows for adaptability across various subjects, making it applicable in language learning, STEM education, and vocational training [14].