# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
###### Abstract  
Designing and conducting experiments is a fundamental process across various scientific disciplines, such as materials science, biology, medicine, and chemistry. However, experimental research still predominantly relies on traditional, time-consuming, resource-intensive, and costly trial-and-error experimentation approaches that hinder rapid discovery, reproducibility, and scalability. Recent advances in artificial intelligence (AI) and machine learning (ML) offer promising alternatives, but a comprehensive overview of their implementations in experimental design is lacking. This research fills this gap by providing a structured overview and analysis of existing frameworks for AI-driven experimental design, supporting researchers in selecting and developing suitable AI-driven approaches to automate and accelerate their experimental research. Moreover, it discusses the current limitations and challenges of AI techniques and ethical issues related to AI-driven experimental design frameworks. learning from previous results. A sketch of the AI-driven experimental design workflow can be found in Figure 1.  
As a result, experiments can be performed precisely and efficiently, saving time and material by avoiding unnecessary trials. Consequently, there are reductions in cost for both experiments and potentially environmentally hazardous waste. In combination with an autonomous platform incorporating an experimentation and analysis stage, experiments can be performed autonomously. This concept is referred to in the literature as a \"self-driven laboratory\" (SDL) [4]. The autonomous execution and analysis of experiments further enhance the reproducibility of experiments and continuous experimentation, ideally operating without the need for human intervention. SDLs for different applications have been reported [2; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. The literature incorporates various reviews highlighting the role of AI in accelerating scientific research across diverse fields with a particular focus on AI-driven experimental design [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 15; 16; 17]. Some reviews related to AI-driven experimental design target a specific domain, such as organic/inorganic semiconductors (materials science) or drug formulation (medicine), providing architectures or frameworks that are intended to be applied in the addressed field. Others concentrate on a specific technique, such as LLMs. The majority of reviews focus on self-driving laboratories rather than AI-human frameworks. Currently, the literature lacks a cross-domain perspective and merely provides general overviews and analyses of existing frameworks for AI-driven experimental design across different domains. Thus, an outline of applied AI methodologies in experimental design is missing. Furthermore, previous reviews overlook key aspects such as the degree of automation, online capability, and generalizability of AI frameworks. In contrast, this study highlights the current state of AI-driven experimental design by analyzing existing approaches and frameworks. This encompasses both the AI techniques used in experimental design and the applied frameworks in general. In terms of a generic analysis of the framework, different aspects are considered. This includes the scientific domain, the degree of automation, the kind of data, and online and generalization capabilities. Additionally, the limitations and challenges of current AI methodologies, as well as explainability and ethical issues, are considered and discussed. By analyzing the existing literature accordingly, this research aims to guide researchers in identifying an ideal AI-driven experimental design system to accelerate their experimentation processes and, thus, their scientific research. Figure 1: Schematic illustration of the AI-driven experimental design workflow. Available experimental data are used to train AI. The scientist defines an experimental objective, and AI suggests experimental designs to the scientist accordingly or performs them autonomously.