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.

研究发现 MDE 对 AI 的使用仍处于早期阶段，并且没有广泛应用的单一工具或方法，当前的方法主要注重具体的开发阶段而非整个开发过程，因此，该研究提出了几个研究方向，以进一步改进 MDE 在 AI 方面的应用，并引导未来的研究。