## 4 Results  
### General Methodology  
Although the process of obtaining research results was made on specific components, based on the model implementation steps, this process has similar approaches and contained specific steps:  
1. The definition of objectives and purpose: The main purposes were related to model simulations in a laboratory or real environment using several methods (direct observation, comparison, etc.). 2. The implementation of the model 1. Application design: the application design comprised the choice of the optimal application development environment (web, mobile, desktop, etc.) and the instruments used (formal modeling languages and techniques, programming languages, methods, architectures, frameworks, etc. ); 2. Application development: the development consisted in the actual implementation of the model based on the blueprint design obtained at the previous step; 3. Testing and troubleshooting: the testing phase consisted in the calibration of the obtained instruments and the identification of specific errors or miscalculations. 4. Application integration and launching: the integration consisted of the connection action of the resulted implementation in a common learning framework. The launching aspects were related to the dissemination of implementation and its usage in data collection for various research contexts. 3. Data collection 1. The definition of objectives: the main purposes were related to the model validation using domain-specific methods, general (direct observation, comparison, etc.) or statistical; 2. Data collection: the data collection consisted of obtaining information based on the implementation behavior or specific research contexts; 3. Data pre-processing: In order to apply several instruments of methods, in several cases a pre-processing was necessary. 4. Data analysis: the collected data were analyzed to assess the achievement of the objectives and to test the formulated hypotheses. This analysis involved the use of statistical or analytical techniques to identify patterns, relationships, or trends in the collected data. * Data interpretation: The results of the analysis were interpreted in the context of previously established objectives and assumptions. The assessment was related to whether or not the data collected supported the hypotheses formulated and to identify the implications of these results for the implementation and the model theoretical assumptions. In the context of the DMAIR model, we address potential concerns related to data privacy, algorithmic transparency, and learner autonomy. All learner data used in simulations are anonymized, and the model is designed to operate within GDPR-compliant frameworks when deployed in real-world scenarios. To mitigate algorithmic bias, DMAIR supports teacher oversight and manual review of generated assessments. The personalization process remains interpretable, allowing educators to understand and intervene in the learning path when necessary. In the current experimental stage, the DMAIR model has been tested using synthetically generated datasets. These datasets were constructed to simulate realistic educational scenarios, including diverse learner profiles, varied item characteristics (datasets with items from various sources, human and machine-generated items and with various randomly-generated numerical indicators), and heterogeneous learning behaviors. The primary goal of using synthetic data was to validate the internal logic, adaptability, and optimization capacity of the model under controlled but meaningful conditions. While we acknowledge the limitations of simulation-based validation, the structure and parameterization of the datasets were designed to reflect the real world as closely as possible. For a better pedagogical assessment of the model, some indicators related to this will be studied in future directions. Table 3 summarizes the main indicators and their corresponding purposes.