## 3 Materials and Methods
### Methodology for Comparative Analysis of Platforms to Address Question 2
#### 3.2.1 The Systematic Approach That Our Analysis Followed Is as Follows:  
1. Database Search using Keywords and Queries: In the search for articles on architectures of adaptive learning platforms that also incorporate artificial intelligence, search engines and electronic databases in the field of education, which provide information on educational technology, adaptive learning platforms, and others, were used. These include the following: ERIC and JSTOR Open Access, DOAJ (Directory of Open Access Journals), and Google Scholar. To search databases and search engines, multiple keywords were used. To ensure relevant results, queries were also constructed using these keywords. Consequently, searches were conducted using the following terms: \"adaptive learning\", \"learning platforms with AI\", \"architecture of an adaptive learning platform\", \"architecture of an intelligent tutoring system\", and \"architecture for learning platforms with an AI assistant\";
2. Articles screening and eliminating duplicates: This stage involves removing duplicate articles and passing them through a screening process. As a result of this screening, only the articles that were relevant to the investigation remained. The filtering process was based on the following elements: (a) the article must refer to an adaptive learning platform used in the field of education; (b) the article must have been published between 2011 and 2023; and (c) the article must be written in English, and the full text must be publicly available;
3. Determining the eligibility of the articles: The articles that remained at this stage were subjected to a review process conducted by each author. During the review process, the authors considered the following: (a) whether the article presented and provided access to the architecture of a learning platform with artificial intelligence; (b) whether the main use cases of the platform were presented; and (c) whether the articles described the components of the architecture, the technology used, their purpose and roles, and how they interact. Importantly, criterion (a), specifically the availability of the platform's architectural schema, was particularly crucial in this review process;
4. Including the articles in review: As a result of the steps mentioned above, 4 articles were included, each referring to an AI-based learning platform architecture. In addition, a comparative analysis of their architectures was performed.  
A qualitative method was applied to analyze the platforms identified in the articles [72]. Furthermore, the 4 articles that were selected followed a process of extracting data from them. The purpose of these data is to aid in the comparative analysis that we need to conduct. Therefore, not all the data from the articles were extracted, but only the necessary data were extracted. We meticulously reviewed the materials collected for each platform, highlighting sections relevant to the established criteria. We documented the role and functionality of each architectural component, paying particular attention to how they interact to form a cohesive system. We also noted any commonalities or unique features across platforms. To ensure a complete understanding, we focused on parts of the articles that explicitly described the system components, their functions, and their interrelationships.  
We organized our findings into a comparative table, summarizing the key characteristics of each platform based on established criteria. This allowed us to identify strengths, weaknesses, and potential areas for improvement.  
On the basis of our evaluation, we synthesized our findings and proposed a new architecture that incorporates generative AI to address the limitations observed in existing platforms. This architecture aims to enhance personalization, expand the range of responses to learner queries, and ultimately improve learning outcomes.