# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 2 Methodology
### Search Strategy  
To efficiently guide this study's research toward the desired search space and exclude non-relevant publications, the subsequent search strategy is applied. It follows [PERSON] and [PERSON] [1] and encompasses the following steps:  
Figure 2: Schematic illustration of the applied methodology. Define research questions for guiding the systematic research. Create an appropriate search query by defining meaningful search terms and assessing relevant databases. Determine filter criteria to exclude non-relevant studies. Apply the filter criteria through appropriate procedures. To guide the literature research of this study, the following research questions are defined:  
In which research fields and kinds of applications are AI techniques used to automate experimental design? Which quantitative methods are used to implement AI in experimental design? Which tasks in the experimental design process are addressed by these techniques? What kind of data are used? Are the proposed frameworks online-capable, and can they generalize well to new data? The first question aims to identify the fields and applications in which AI-driven experimental design is applied. The second question aims to investigate which AI methodology is employed to ensure that the scope of the research lies within AI-driven experimental design. The objective of the third question is to examine which experimental design tasks within the experimental design process are addressed using AI methodologies. The fourth question addresses the data utilized, while the fifth question aims to analyze the online and generalization capabilities of the proposed frameworks. The publications included in this research are intended to answer these questions in order to provide a comprehensive overview of AI-driven experimental design. Thus, this study investigates various databases to ensure a comprehensive and balanced review of the existing literature, covering both method-specific and subject-specific resources. Databases typically accepted in the field of AI, such as the ACM Digital Library and IEEE Xplore, are included. Additionally, subject-specific and experimental design-related resources, like ACS Publications and RSC Publishing, which focus on biology, chemistry, and materials science, are examined. To incorporate medicine-related publications, PubMed is also considered. Furthermore, general databases, including ScienceDirect and Springer-Link, are utilized to broaden the scope of the research. This selection of databases--two focused on AI, two on materials science, biology, and chemistry, one on medicine and biology, and two general repositories--ensures a broad and diverse overview of the relevant literature regarding AI-driven experimental design, balancing method-specific resources of AI research with subject-specific studies in experimental design while disregarding those with a very limited application focus or low popularity. The assessed databases are queried by applying an appropriate search query. It combines various meaningful search terms. Using a search query ensures the reproducibility of our research. For this work, a search query is experimentally derived, and the following combinations of search terms were found to yield the best results for relevant studies: _\"artificial intelligence\" AND \"driven\" AND (\"experiment planning\" OR \"experiment selection\" OR \"experiment parameter adaption\")_. The first part of the query (\"artificial intelligence\" and \"driven\") narrowed the search to publications that apply AI methodologies. Thus, the authors of this research could focus on intelligent, automated solutions and omit manual, hand-optimized approaches. During the development of the search query, it was found that using the term \"artificial intelligence\" consistently included all relevant publications identified in this study.