# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 4 Conclusions  
This study investigated the role of AI in experimental design, aiming to explore whether and how AI can accelerate scientific research. A broad literature search was conducted across multiple databases, combining method-based, subject-specific, and general sources. A carefully developed search query and appropriate filtering narrowed the initial number of 234 results to 22 relevant contributions. This relatively small number of results reflects the emerging nature of AI-driven experimental design, highlighting a clear need for further research in this interdisciplinary field. A taxonomy-based analysis of the selected studies was used to address the defined research questions. The findings suggest that AI has significant potential to accelerate scientific research by optimizing the design, planning, and execution of experiments. In particular, AL, including optimization methodologies, stand out as key drivers, enabling iterative refinement of experiments using minimal data. These methods can effectively reduce the number of necessary trials by prioritizing the most informative experiments, thus saving time and resources. The use of surrogate models and uncertainty estimation allows AI systems to make informed decisions, even under data scarcity, demonstrating a practical advantage over traditional trial-and-error methods. BO is predominantly employed and shows great performance in handling scarce data and multiple targets (MOBO). To address scarce data and high-dimensional parameter spaces and enable long-term planning, combining RL with the data efficiency of AL is recommended. Apart from this, the majority of AI frameworks belong to chemistry or materials science. The generalizability of several frameworks offers the opportunity to apply them in other domains. However, the application of DL remains limited, primarily due to data requirements. Obtaining significant amounts of real data is often time-consuming and costly. Therefore, future research should investigate data-efficient frameworks and AI methods for dealing with scarce data. Where sufficient data is available, its integration could lead to more powerful, flexible, and accurate design tools. Data augmentation, transfer learning, and few-shot learning could help to overcome data scarcity. Furthermore, the lack of explainability and transparency in AI decision-making remains a barrier to broader acceptance within the scientific community. This highlights the need for the implementation of interpretable models. In terms of deployment, partially autonomous frameworks are most common, balancing AI-driven decision support with human oversight. Here, AI acts as a helping hand, without preventing human creativity. In contrast, fully autonomous systems offer transformative potential by enabling continuous, closed-loop experimentation without human intervention. These systems can drastically speed up the experimentation process by suggesting experimental designs, executing them, and analyzing the results to modify subsequent experiments. However, the implementation of fully autonomous frameworks is still rare and needs further research. In particular, regarding their explainability to address ethical issues. Overall, this study demonstrates that AI-driven experimental design can make scientific research more efficient, targeted, and reproducible. Researchers, especially in data-rich, high-throughput environments or with high parameter-search space complexity, stand to benefit most. In addition, domains with sparse data can also see improvements through smart sampling and model-guided experimentation. This research provides a foundation for future studies that aim to apply AI for experiment design in a certain domain. By offering a structured overview of existing frameworks, their implementation, and associated limitations, this study seeks to support researchers in selecting and developing suitable AI-driven approaches to accelerate their experimental processes. Conceptualization, L.N. ; methodology, L.N. ; data curation, L.N. ; investigation, L.N.