Holstein, JoshuaMüller, PatrickVössing, MichaelFromm, Hansjoerg2024-12-262024-12-262025-01-07978-0-9981331-8-8b61cd247-6100-4f2e-938c-e38f9f5c4d76https://hdl.handle.net/10125/109300The rapid advancements of artificial intelligence (AI) have led to its widespread adoption in enhancing productivity across various domains, including both personal productivity and workplace efficiency. Despite these advancements, the effective onboarding of novices, particularly in complex environments with extensive documentation, remains a significant challenge. Therefore, this paper explores the design of AI assistants to support novices. Utilizing a design science research approach, we collaborate with a leading pharmaceutical manufacturer to develop and evaluate an AI assistant to support novices during their onboarding. Grounded in scaffolding theory, we identify two design requirements and propose three design principles: Metadata Filtering, Graduated Complexity, and Sequential Query Generation. The evaluation of these principles demonstrates the AI assistant's effectiveness in generating accurate and contextually relevant responses, facilitating the onboarding of novices. This study provides valuable insights into the design of AI assistants, contributing to the theoretical understanding of AI-driven scaffolding and practical applications in complex industrial settings.10Attribution-NonCommercial-NoDerivatives 4.0 InternationalArtificial Intelligence-based Assistants and Platformsai assistants, human-ai collaboration, large language model, onboardingConference Paper10.24251/HICSS.2025.456