## 4 Results
### The Proposed Architecture for the Learning System with Generative AI  
In this section, based on the results obtained from the questionnaire and the conclusions from the comparative analysis of learning platforms, the proposed system architecture is detailed along with a description of its components and how they interact with each other. Additionally, the development of the proposed architecture was guided by [PERSON]'s constructionist theory, which was placed at the forefront to ensure a learner-centered and exploratory approach to education [4]. From the results of the questionnaire answered by the students (presented in detail in Section 4.1), concrete information was extracted on which the learning system architecture proposal can be based. Thus, the score for the item \"ChatGPT can increase my study efficiency\" of almost 3.5 and the score of 3.48 for \"ChatGPT improve the quality of my assignment\" represented a foundation for adding a module with a learning assistant (AI Assistant Module). The purpose of this module is to provide support and guidance in the learning process. The certainty that personalized learning can be provided by generative AI through the score of 3.45 for the item \"ChatGPT can provide me personalized education\" determined the introduction of a smart learning interface module that allows accessing customized content and interacting with the learning assistant. The score of 3.14 recorded by the item \"I am satisfied with the quality of the information provided by ChatGPT\" led to the integration of a module that has a knowledge base from which the learning assistant can be fed with answers for users. As highlighted in Section 3.2.2 our comparative analysis of existing platforms revealed a lack of generative AI components, particularly LLMs, to address scenarios where lesson content does not fully meet learners' needs. These systems lack knowledge bases to answer user questions and features to solve problems or improve engagement. Our proposed architecture aims to bridge this gap by providing a knowledge base for each lesson, enabling the learning assistant to answer learners' questions. If the knowledge base lacks a suitable answer, an LLM can generate one, ensuring comprehensive support for learners. The proposed architecture centers around an adaptive learning platform that uses artificial intelligence (AI) to deliver intelligent learning lessons with the help of a virtual assistant. A key feature is the incorporation of generative AI, as demonstrated by research, to expand the range of answers that the learning assistant can provide. This supports both students and teachers throughout the learning process, with the assistant adapting content to each learner's unique needs [106]. The core of the architecture is the learning assistant, on which several modules depend. These modules support smart lessons that students can access through the dedicated learning interface. Smart lessons are designed to incorporate open educational resources, allowing students to progress through the material at their own pace, as also demonstrated by [107]. The assistant can also suggest additional lessons to supplement the student's knowledge. The smart learning interface delivers interactive educational content and grants access to AI-based assistants. It guides learners through the lessons, allowing them to navigate within a lesson, review content, and reread information from open resources. Importantly, the interface facilitates the interaction with the learning assistant. This approach is also highlighted by [108]. Students can pause the normal flow of the lesson to ask questions or seek clarification of challenging concepts. The assistant responds with explanations and directs the students to relevant resources. The answers to the student questions are initially drawn from a predefined knowledge base associated with each lesson. However, if the knowledge base is insufficient, the proposed architecture integrates a generative AI component based on large language models.