Objective: This study aims to investigate the existing body of knowledge in the field of Model-Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and define the current state of the art. Method: We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 703 candidate studies, eventually retaining 15 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results: The study's findings show that the pillar concepts of MDE (metamodel, concrete syntax and model transformation), are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. Different MDE technologies are used, leveraging different language workbenches. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisciplinary communication of requirements, such as the CRISP-DM \textit{Business Understanding} phase, are rarely reflected. Conclusion: The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process. As a result, the study suggests several research directions to further improve the use of MDE for AI and to guide future research in this area.

翻译：目的：本研究旨在探索模型驱动工程（MDE）支持人工智能（MDE4AI）领域的现有知识体系，以进一步明确未来研究方向并界定当前技术发展水平。方法：我们采用系统文献综述（SLR）方法，从五个主要数据库中收集文献，获得703篇候选研究，最终保留15篇主要研究。每项主要研究将从以下两方面进行评估与讨论：（1）MDE原则与实践的应用；（2）与CRISP-DM方法论各阶段相对应的AI开发支持阶段。结果：研究结果表明，MDE的核心概念（元模型、具体语法和模型转换）被用于定义明确针对AI问题的领域特定语言（DSL）。研究者采用了不同语言工作台支撑的多种MDE技术。最受关注的AI相关问题是AI算法的训练与建模，而耗时耗力的数据集准备工作受到的关注较少。支持需求跨学科沟通的早期项目阶段（如CRISP-DM的"业务理解"阶段）很少被体现。结论：研究发现，MDE在AI领域的应用仍处于早期阶段，尚未出现广泛使用的统一工具或方法。此外，当前方法往往聚焦于特定开发阶段，而非提供全流程支持。基于此，本研究提出了若干研究方向，以进一步改进MDE在AI领域的应用并指导该领域的未来研究。