## 4 Application and Results
### Phase 6-Construction of the Learnings  
The developed process has led to the generation of the following key learnings.  
* **Learning n\({}^{\circ}\)1 (Core models inclusion)**-The analysis reveals that conceptual and experimental models are prominently featured across most clusters. Notably, the 9 th cluster diverges by excluding both models. Additionally, despite the widespread prioritization of the conceptual and experimental models, the utilization of the mathematical model remained surprisingly low.
* **Learning n\({}^{\circ}\)2 (Adapting iterative strategies)**--The analysis demonstrates that the most common strategies include iterative processes, especially involving the physical user interface design and subsequent testing. This adaptation enables continuous user feedback when initial models are incomplete. Despite the potential benefits of iterative methodologies, the results highlight that research articles from low-income countries exhibit a low rate of iterations. This may be explained by the challenges associated with field testing and distance from end users.
* **Learning n\({}^{\circ}\)3 (Incomplete modeling)**--The analysis indicates that the methodologies examined in this study fail to fully incorporate all models of the engineering design meta-model. Indeed, none of the 15 clusters included all the models. This result underscores the incompleteness within the field and a gap in current research methodologies.
* **Learning n\({}^{\circ}\)4 (Mathematical Modeling Gap)**--The analysis exposes a reluctance in adopting mathematical modeling. Among the clusters analyzed, only clusters 2 and 13 employ the mathematical model. The results suggest that designers may be limited in their ability to take advantage of the enhanced precision, predictive capabilities, optimization potential, and validation opportunities afforded by mathematical models.
* **Learning n\({}^{\circ}\)5 (Community-based Design)**--The analysis suggests a transition from user-based design to community-based design. To better addresses local cultural needs, the results show that it is preferable to focus on, or conduct, studies on community-wide studies rather than individual users.
* **Learning n\({}^{\circ}\)6 (Real-time dynamic solutions)**--The analysis outlines the importance in incorporating cultural sensitivity across multiple cultures. The physical models should not exclude users due to socio-economic and literacy disparities. The results reveal that designs should be tested in real-time using dynamic solutions to ensure they meet the varied capacities and computer literacy levels of users.  
* **Learning n\({}^{0}\)7 (Versatility)--**The analysis allowing for identification of 15 clusters, demonstrating the diversity of the methodologies employed. The results show that including varying levels of literacy, computer literacy, and potential future literacy is crucial to make designs of user interface versatile. More than in any other context, the results suggest that designers should correctly include users in all phases.
* **Learning n\({}^{0}\)8 (Model-based Interface design)--**The design of an interface can be seen as a process of building engineering models from the user domain to the process domain.