The paper "XAI for All: Can Large Language Models Simplify Explainable AI?" by Philip Mavrepis, Georgios Makridis, Georgios Fatouros, Vasileios Koukos, Maria Margarita Separdani, and Dimosthenis Kyriazis explores the challenge of making Explainable Artificial Intelligence (XAI) more accessible to non-experts. The authors introduce "x-[pIAIn]," a Large Language Model (LLM) developed using ChatGPT Builder, designed to generate clear and concise summaries of various XAI methods tailored for different audiences, including business professionals and academics. The key feature of this model is its ability to adapt explanations to match each audience group's knowledge level and interests, enhancing user engagement and understanding across different sectors. The model's effectiveness is validated through use-case studies, demonstrating its capability to provide audience-specific explanations that are comprehensible and relevant. The paper highlights the importance of human-centric XAI, focusing on providing user-friendly interfaces and ensuring that the design of explanations and interfaces is user-centric. The findings suggest a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users, bridging the gap between complex AI technologies and their practical applications.The paper "XAI for All: Can Large Language Models Simplify Explainable AI?" by Philip Mavrepis, Georgios Makridis, Georgios Fatouros, Vasileios Koukos, Maria Margarita Separdani, and Dimosthenis Kyriazis explores the challenge of making Explainable Artificial Intelligence (XAI) more accessible to non-experts. The authors introduce "x-[pIAIn]," a Large Language Model (LLM) developed using ChatGPT Builder, designed to generate clear and concise summaries of various XAI methods tailored for different audiences, including business professionals and academics. The key feature of this model is its ability to adapt explanations to match each audience group's knowledge level and interests, enhancing user engagement and understanding across different sectors. The model's effectiveness is validated through use-case studies, demonstrating its capability to provide audience-specific explanations that are comprehensible and relevant. The paper highlights the importance of human-centric XAI, focusing on providing user-friendly interfaces and ensuring that the design of explanations and interfaces is user-centric. The findings suggest a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users, bridging the gap between complex AI technologies and their practical applications.