Abstract The application of Large Language Models (LLMs) in Model-Driven Engineering (MDE) has emerged as a rapidly evolving research area. While existing systematic literature reviews have examined specific technical approaches, a comprehensive mapping of the broader research landscape (e.g., development trends) remains lacking. This study presents a systematic mapping study of LLM applications in MDE, analyzing 86 primary studies collected from five databases, covering publications from 2022 to early 2026. Guided by five research questions, we characterize the field across five dimensions: MDE task distribution and research contribution types, LLM technologies and interaction strategies, artifact representation and processing, validation practices, and publication landscape. Our findings reveal that current LLM4MDE research is heavily concentrated on Model Generation, while tasks such as Model Migration, DSL Engineering, and Metamodeling remain marginal. Most approaches rely on black-box OpenAI models accessed via remote APIs and adapted through prompt engineering, with fine-tuning and retrieval-augmented generation rarely employed. Inputs are predominantly natural-language artifacts, while outputs are model-oriented but usually expressed in lightweight textual formats rather than native MDE exchange formats. Validation is centered on quantitative experimentation, with 42% of studies reporting no baseline and cost efficiency reported in fewer than one quarter of studies. The field has grown rapidly, from one paper in 2022 to 42 in 2025, with research concentrated in Europe and Canada and limited industry involvement. Based on these findings, we identify gaps and opportunities across task coverage, technical configuration, and evaluation practice, offering a knowledge map to guide future work in this cross-disciplinary field.

ABSTRACT Introduction Requirements engineering (RE) faces challenges due to the handling of increasingly complex software systems. These challenges can be addressed using generative artificial intelligence (GenAI). Given that GenAI‐based RE has not been systematically analyzed in detail, this review examines the related research, focusing on trends, methodologies, challenges, and future work directions. Methods A systematic methodology for paper selection, data extraction, and feature analysis is used to comprehensively review 238 articles published from 2019 to 2025 and available from major academic databases. Results Although generative pretrained transformer models dominate current applications (67.3% of studies), the research focus remains unevenly distributed across RE phases, with analysis (30.0%) and elicitation (22.1%) receiving the most attention and management (6.8%) remaining underexplored. Three core challenges—reproducibility (66.8%), hallucinations (63.4%), and interpretability (57.1%)—form a tightly interlinked triad affecting trust and consistency, and strong correlations ( co‐occurrence) indicate that these challenges must be addressed holistically. Industrial adoption remains nascent, with > 90% of studies corresponding to early‐stage development and only 1.3% reaching production‐level integration. Evaluation practices show maturity gaps, limited tool/dataset availability, and fragmented benchmarking approaches. Conclusions Despite the transformative potential of GenAI‐based RE, several barriers hinder its practical adoption. The strong correlations among core challenges demand specialized architectures targeting interdependencies rather than isolated solutions. The limited real‐world deployment reflects systemic bottlenecks in generalizability, data quality, and scalable evaluation methods. Successful adoption requires coordinated development across technical robustness, methodological maturity, and governance integration. A multiphase research roadmap emphasizing evaluation infrastructure strengthening, governance‐aware development, and industrial‐scale standardization is proposed.