## 4 Results  
### Analysis of the Architectures of Identified Platforms  
This section presents the architectures of the systems analyzed, specifically those with publicly available architectures. Existing educational platforms based on artificial intelligence offer significant advantages in adapting to the needs of students and teachers. However, there is room for improvement in how virtual assistants or intelligent tutoring systems interact with students, particularly in deep understanding user intent and providing assistance for topics not explicitly covered in the curriculum. In particular, there is a lack of components built using large language models (LLMs) to answer user questions. Our proposed architecture addresses these limitations by incorporating a generative AI component. The Squirrel AI architecture is designed around a student-centric AI-based adaptive learning platform. This solution aims to make learning both efficient and engaging [80]. Students using the platform interact with an AI-powered virtual teacher that guides their learning while also receiving support from a human instructor [81]. At its core, the platform features an AI engine comprising AI algorithms, content, data, and pedagogical techniques. Squirrel AI emphasizes its large adaptive model, which combines adaptive methods with large models trained on 10 billion data points of student learning patterns. This model seeks to better understand how learners learn and how content relates to their learning capacity, enabling a more personalized learning experience [82; 83]. The Squirrel AI architecture is organized into three main categories:  
Learning Management System Core: As presented in [80], this module includes real-time event collectors that track student progress and generate learning maps that illustrate their mastery of theoretical concepts. Learning Content: This includes a learning map, goals, and a content map. API Services: These services provide real-time recommendations based on learning content and student interactions. Figure 2 provides a visual representation of the Squirrel AI architecture, which is based on [80]. The Squirrel AI solution covers various STEM subjects (science, technology, engineering, and math) and language learning content. It serves students from kindergarten through grade 12 [84]. Knewton is another adaptive learning platform that we analyzed. It provides customized learning content tailored to the individual needs and level of the learner. This personalization is achieved through continuous data analysis and processing, which assesses the learner's current knowledge state and guides future performance improvements [85]. Figure 3 illustrates the complete architecture of the Kenwton solution, which also integrates Amazon services. This figure is adapted from the original architecture presented in [85]. Figure 2: Squirrel AI architecture (based on [80]). A significant aspect of the Knewton platform is its emphasis on managing vast amounts of data related to student activity both inside and outside of the classroom. These data are analyzed in real time to update recommendations for users, facilitated by the system's built-in services [85]. Knewton employs artificial intelligence (AI) algorithms as a core element of its platform to personalize content [86]. These algorithms recommend personalized lessons and, on the basis of the learner's performance, identify areas for improvement within the content [87]. Knewton covers subjects such as mathematics, English, science, and history for students from kindergarten to grade 12 [88]. The architecture of the Knewton platform is based on modules with cloud services from Amazon Web Services (AWS) that are used to personalize live learning. The platform features components for collecting user data, analyzing their behavior in the platform, and rendering personalized content.