# Configurable Intelligent Design Based on Hierarchical Imitation Models
## 4 Preliminary Results
### Validity and Significance  
The validity of a configurable design is founded on its inherent qualitative property of determinism. This approach is implemented by excluding the use of unverified external data (such as databases or knowledge) and instead employing working models that exhibit high levels of adequacy and reliability. The utilization of data arrays is minimized, typically limited to system-generated data for specific purposes or a restricted set of external data derived from the system's experiment planning feature. The quality of model adequacy is guaranteed by their fundamental nature, which is based on established scientific and specialized methodologies, relevant laws, axioms, theorems, and so forth. Additionally, hierarchical modeling facilitates a quasi-dynamic effect as an alternative to neural dynamics. Furthermore, system performance is enhanced through reference learning, which modifies simulation models during online interactions with users. The significance of configuring systems is determined by both their specific methodological and technological characteristics and their practical relevance, as indicated by demand indicators. The sphere of education, particularly in the natural and technical domains, is characterized by high quality and standardization of methods and procedures, built upon a long-standing history and extensive expertise. This provides a solid foundation for the development of deterministic AI educational systems. The positive reputation of online educational resources, such as [89; 91], can also be attributed to their utilization of educational materials directly aligned with US educational standards, which serve as a reference for standards in many states and regions. It is worth mentioning that government bodies are showing interest in supporting the creation of AI education systems, as evidenced by their funding of relevant projects and their implementation in schools and universities [68; 91]. For instance, the development of an automated educational content generation system [68] resulted in the creation of a new university course on theoretical mechanics, complete with next generation educational materials. Additionally, a specialized consulting group was established to provide support for AI content generation in educational institutions. The practical application of content generators [68; 69] has revealed the significant impact of new content on the didactic aspects and learning outcomes. Firstly, studying a particular theoretical material in multiple distinct configurations, as opposed to a typical book format, greatly enhances the quality of content perception. This is evident in the effective \"recognition\" of the material not only within related fields of knowledge but also in unexpected practical applications. The diverse range of educational tasks and theorems, characterized by methodological unity and even external similarities, fosters user interaction and collaboration, increasing their engagement and the amount of information assimilated. Furthermore, through joint training activities, leaders emerge, and users can be structured based on their level of training and intellectual capabilities, allowing for tailored provision of training materials across different subjects and levels of complexity. Materials such as challenging tasks for independent work or unique theoretical content for training seminars are offered on a competitive basis, encouraging healthy competition among users. The experience gained from AI education can be effectively applied to the realm of scientific research, where rigorous verification and testing of fundamental theoretical models are conducted. By employing robust initial models with a high degree of generality, configurable design enables the construction of a networked hierarchical system of models, yielding substantial amounts of valid specialized information. The potential of the configuration approach is exemplified in the study of wind turbines [93; 94].