Abstract
Troubleshooting complex consumer electronic products often overwhelms novice users due to limited technical knowledge and the complexity of diagnostic procedures. This study presents an AI-powered conversational assistant that guides users step-by-step through identifying and resolving product issues. Using the VanMoof S3 e-bike as a case study, the system integrates a Retrieval-Augmented Generation (RAG) framework trained on public data, including Reddit threads and user manuals. The interface supports natural dialogue to help users not only fix symptoms but also understand root causes. In a user study with 20 participants, the assistant achieved high usability (87/100 average satisfaction) and low cognitive load (NASA-TLX), enabling users to successfully repair components. Participants expressed strong interest in using the assistant for future troubleshooting tasks. These findings demonstrate the potential of AI-driven guidance tools to empower non-experts in complex repair scenarios and promote more sustainable product use.
Keywords
Human-AI interaction; Large language model; Chatbot design; Troubleshooting assistant
DOI
https://doi.org/10.21606/iasdr.2025.584
Citation
Huang, P.,and Chuang, Y.(2025) Empowering Novice Users with AI: A Conversational Interface for Complex Product Troubleshooting, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.584
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 4 - Human-Centered AI
Empowering Novice Users with AI: A Conversational Interface for Complex Product Troubleshooting
Troubleshooting complex consumer electronic products often overwhelms novice users due to limited technical knowledge and the complexity of diagnostic procedures. This study presents an AI-powered conversational assistant that guides users step-by-step through identifying and resolving product issues. Using the VanMoof S3 e-bike as a case study, the system integrates a Retrieval-Augmented Generation (RAG) framework trained on public data, including Reddit threads and user manuals. The interface supports natural dialogue to help users not only fix symptoms but also understand root causes. In a user study with 20 participants, the assistant achieved high usability (87/100 average satisfaction) and low cognitive load (NASA-TLX), enabling users to successfully repair components. Participants expressed strong interest in using the assistant for future troubleshooting tasks. These findings demonstrate the potential of AI-driven guidance tools to empower non-experts in complex repair scenarios and promote more sustainable product use.