Model-Driven Engineering (MDE) supports the development of large-scale, complex systems through abstraction, automation, and traceability. The rise of Large Language Models (LLMs) has renewed interest in reshaping MDE, with recent studies exploring their use in tasks such as model generation, model comprehension, as well as model to code and test generation. While early results suggest improved productivity and lower barriers to modeling, their overall impact remains unclear.To address this, we conduct a Systematic Literature Review (SLR) and analyze 228 primary studies. We examine MDE tasks addressed, how LLMs were leveraged, evaluation practices, and the availability of tools and datasets. Results indicate a rapidly growing research trend, covering 24 types of MDE tasks, with model generation as the dominant focus. Domain-Specific Languages (DSL), Unified Modeling Language (UML), and Business Process Model and Notation (BPMN) are the most frequently targeted formalisms. GPT-family models and zero-shot prompting are most commonly used, with fine-tuning and retrieval-augmented generation (RAG) as typical enhancement techniques. Evaluations primarily emphasize correctness, with limited benchmarking in realistic settings and insufficiently comprehensive replication materials.
Zhang et al. (2026) studied this question.