AI–Augmented Real‐Time Collaborative Blended Modeling Framework for Automotive Embedded Systems

Model‐driven engineering (MDE) has emerged as a foundational paradigm for the development of embedded systems in domains such as automotive, where stringent correctness and structural consistency must be maintained throughout the engineering process. However, traditional modeling practices often suffer from fragmented toolchains, siloed notations, and limited support for real‐time collaboration across distributed stakeholders. This paper presents an artificial intelligence (AI)–augmented and role‐aware collaborative framework that enables blended modeling through seamless synchronization of textual and tree‐based/graphical views, underpinned by a robust service‐oriented architecture. Rooted in automotive system engineering standards, specifically electronics architecture and software technology–architecture description language (EAST–ADL), and instantiated via an industrial‐grade domain‐specific modeling language (DSML) use case, the proposed framework facilitates stakeholder‐specific modeling via both textual and tree‐based/graphical editors. At the core of this infrastructure lies a real‐time coordination engine that governs session orchestration, conflict resolution, and model merging, ensuring semantic consistency across heterogeneous representations. A key innovation is the integration of an explainable AI (XAI) based log analysis engine, which leverages large language models (LLMs) to generate human‐readable summaries of user activity, model evolution, and design rationale. This not only enhances transparency and auditability but also introduces explainability as a first‐class concern in collaborative MDE workflows. Empirical validation confirms the framework’s effectiveness in enabling synchronized, traceable, and fault‐tolerant co‐modeling for automotive embedded systems. By fusing collaborative computing, XAI, and domain‐specific rigor, this research advances the state of the art in intelligent, scalable, and human‐centered system modeling environments.