In scientific applications, integrating artificial intelligence (AI) and machine learning (ML) has revolutionized research methodologies and workflows. This study delves into an innovative application of OpenAI's Large Language Models (LLMs) in developing a conversational AI chatbot, drawing exclusively from the culturally significant Legacy of Slavery (LoS) datasets maintained by the Maryland State Archives. This initiative deviates from conventional chatbots that rely on a vast, generalized corpus for training. Instead, it focuses on harnessing the LoS datasets as the sole source for responses, thereby ensuring the authenticity and contextual relevance of the historical content. Expanding on Cloud-hosted interactive digital computer notebooks (iDCNs) to design and create a new Learning-as-a-Service (LaaS) solutions are at the heart of this research. These notebooks are designed to elucidate the methodology behind employing OpenAI's LLMs to engineer a chatbot that engages in meaningful dialogues and is also constrained to using verified data from the LoS collection. The intention is to create a chatbot that supports educational and research-focused interactions, offering users insights rooted directly in the archival material. The project also integrates LangChain agents, such as CSV agents, to empower the chatbot with data aggregation and analytical tasks capabilities, extending its functionality beyond standard conversational interfaces. A pivotal aspect of this study is the comparative analysis between the outcomes produced by the LLM-based chatbot and those obtained using traditional data analysis and visualization tools like Tableau. This comparative study is essential to assess the effectiveness and accuracy of AI-driven analysis compared to conventional data analysis methods. It aims to illuminate the potential benefits and drawbacks of employing LLMs in scientific and research settings, particularly in the context of historical and cultural data analysis. This project's cloud computing and AI convergence exemplify an innovative approach to digital humanities and archival research. The cloud-based digital notebooks serve as a model for LaaS solutions, showcasing how AI can transform the access, analysis, and dissemination of cultural and historical data. This research contributes significantly to the ongoing discourse on AI-enabled scientific workflows, offering new perspectives on applying ML and Deep Learning techniques in data-rich domains of humanities research. Through its unique use of AI, this project opens new pathways for interacting with, analyzing, and learning from historical datasets. It demonstrates the transformative potential of AI in reshaping educational and scholarly approaches to digital humanities. Another aspect of this study is to enable access to the hidden information buried in the physical archives of culturally rich dataset collections. The insights gleaned from this study are poised to influence a range of disciplines, promoting a deeper understanding of how AI can be tailored to respect and amplify the nuances of cultural and historical datasets in the digital era.