## 1 Introduction
[PERSON] [17] studies specifically the suitability of the UCD framework in low-income countries. The author concludes that the empathic approach of the UCD framework is not ideal, whereas the usage of a technology probe is more suitable. [PERSON] et al. [18] identifies that due to the very different nature of the context and specificities of each use case or region, \"there is no single algorithmic best practice regarding participatory design in information systems which is applicable to all situations.\" This study details three interventions in three different regions and contexts and explains how the design team is able to include the end users in the design methodologies. [PERSON] [19] focuses on the adaptation of design tools to include cultural specificities of the target user group. For example, in a workshop involving users of Namibian culture, the authors highlighted that the Namibian culture is a high collectivistic culture, using [PERSON]'s [20] cultural model. Considering this characteristic, the users have tested the proposed service in small groups instead of one-on-one testing sessions. The session was also started by directly asking the users what their definition of a \"qualitative\" system was. It allows the authors to understand what is valuable to the users and then focus the subsequent evaluation on those aspects. While beneficial in their respective design projects, contexts of work, or specific research domains, existing methodologies have drawbacks and cannot be universally applied in low-income countries. The pertinent research question then arises: _Can we identify new patterns, trends, and paradigms that hold promise for application in interface design for low-income countries_? In response to this research question, this paper proposes the development of the Engineering design Research meta-model based Systematic Analysis (ERSA) process, which was developed in collaboration between the ERCOS research Pole of the ELLIADD laboratory at UTBM and Orange Innovation. ERSA is a new process for acquiring learnings to succeed in designing interfaces for low-income countries. An engineering design meta-model proposed by [PERSON] et al. [21] is used as a tool for analyzing a collection of research papers to extract valuable insights and identify emergingpatterns in design methodologies. A meta-model framework provides a structured and systematic approach to analyzing research papers. It allows researchers to organize and categorize information, making it easier to identify commonalities, differences, and gaps in existing design methodologies. It involves multiple interconnected models distributed in layers, providing a holistic understanding of the design process and its implications. Clustering algorithms within the meta-model framework enables the grouping of similar methodologies or approaches. This clustering facilitates the identification of trends and patterns across different studies, leading to a deeper understanding of the design of interfaces. It also streamlines data processing techniques, allowing researchers to extract relevant information efficiently and accurately. By employing a meta-model framework, we can identify and evaluate a wide range of design methodologies, enabling the development of more tailored and effective solutions. By understanding the strengths and limitations of different approaches, we can adapt and customize methodologies to suit specific contexts of low-income countries, ultimately leading to more impactful and sustainable solutions. It also can facilitate the transfer of knowledge and best practices across different multidisciplinary teams. By synthesizing and organizing information from diverse sources, it helps bridge the gap between theory and practice, fostering collaboration and innovation in the field of interface design. This article is organized as follows: Section 2 presents the engineering design meta-model for interface design. Section 3 outlines the construction of the proposed ERSA (Engineering design Research meta-model based Systematic Analysis).