## 3 DMAIR Description  
### General Considerations and Purpose  
The model presented in this paper shows innovation aspects related to a complete overview of the learning process, starting from the teaching phase and continuing to the learning and assessment phases. In this matter, the Dynamic Model for Assessment and Interpretation of Results (DMAIR), developed sequentially and continually in [54; 55; 56; 57], with various variations, such as in [58], shows a perspective of integrating automated tasks in the educational process. This initiative would reduce the effort generated by human-made educational tasks. At the same time, its mechanisms should replicate the human expertise related to process quality in teaching, learning and assessment (TLA) as close as possible. The model responses to the specific research gaps identified earlier by the following:  
* _Dynamic personalization_: The learning process, emphasized by the learning path generation, and the assessment component, related to assessment test generation, carry a high potential for learning personalization. * _An integrated model for adaptive learning_: The proposed model offers a unified framework that connects the teaching, learning, and assessment processes. * _Usage of genetic algorithms_: Based on specific elements of Classical Test Theory and Item Response Theory, among other theoretical frameworks, for a valid scientific foundation of the model design, the advantages of the solutions of the genetic algorithms are explored. * _scalable architecture_: the model is designed with flexibility in mind, following a scalability-driven structure and taking into account the future integration into existing and developing Learning Management Systems. These contributions address the first three research gaps directly. While real-world validations are only partially addressed in this stage, the proposed architecture establishes a solid foundation for future experimental studies in authentic educational environments. The main innovative aspects that show novelty related to existent approaches are related to the following:  
* The interaction between the learning and teaching component and the assessment component, both using dynamic evolutionary-based methods to obtain the desired results;* the traditional approaches related to learning path generation are related to rule-based approaches or static branching logic. In this matter, the current model uses genetic algorithms to optimize the structure and sequence of learning blocks for each learner;
* the integration of a closed feedback loop by integrating automated generation, real-time analysis, and interpretation of results related to assessment;
* the accent is put on the optimization of the learning and assessment process with a direct influence on learning progression; the model adapts both _what_ is taught and _how_ learners are assessed;
* the architecture of the model is a modular one, with the scalability and integration of the implementation in specific learning platforms in mind. To ensure computational feasibility when applying genetic algorithms in large-scale environments, the model supports parallel processing strategies and can be integrated with distributed computing infrastructures such as cloud-based platforms. Also, optimization techniques such as genetic parameter tuning and population variation are studied to address scalability related to high volumes of data. An educational scenario may take into account an online platform designed to teach high school biology. A student logs in to begin a new unit on genetics. The model first generates a diagnostic assessment with varied difficulty levels and topics. Based on the student's responses, a personalized sequence of learning blocks is generated, including video lessons, interactive quizzes, simulations, and reading assignments, arranged to address weak points and reinforce core concepts. As the student progresses, the model continuously monitors performance through embedded micro-assessments.