Enabling Interactive AI in Industry 5.0 with RAG-Enhanced GenAI Chatbots

Industry 5.0 advances sustainable development through human-machine collaboration and personalised manufacturing. The increase in intelligent industrial equipment creates data scalability challenges for human workers who face difficulties in making decisions based on relevant data sources. Advanced interactive AI systems, capable of integrating diverse data sources and delivering real-time, context-aware insights, present promising solutions to the challenges of the industrial environment. This research introduces a retrieval-augmented generation (RAG)-enhanced Generative artificial intelligence (GenAI) chatbot to address these challenges. The system integrates a variety of information sources, including government reports, news websites, academic studies, and industry reports. This industry 5.0 chatbot aims to offer users extensive knowledge of the industrial sector through a Question-and-Answer interface. It provides relevant and accurate information through intuitive, context-aware interactions to reduce cognitive load for users, which improves decision-making efficiency and user experience. Through experimental evaluation, the RAG-enhanced GenAI chatbot significantly improves accuracy, relevance and user satisfaction, outperforming models like ChatGPT-4o. This system presents an innovative practical solution to tackle Industry 5.0 core issues particularly in enhancing human-machine collaboration and decision-making efficiency. This research contributes to the theoretical and practical development of RAG-enhanced AI systems, laying a foundation for future investigations of industrial AI interaction.

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Publication Details

Published
2025-06-16
DOI
https://doi.org/10.1109/ice/itmc65658.2025.11106634
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48
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article

Enabling Interactive AI in Industry 5.0 with RAG-Enhanced GenAI Chatbots

Yuwei Wan, Maneesh Kumar, Tianyu Zhou, Liu Ying
1 citations
AI in Service Interactions
2.48
article

Enabling Interactive AI in Industry 5.0 with RAG-Enhanced GenAI Chatbots

Yuwei Wan, Maneesh Kumar, Tianyu Zhou, Liu Ying
article en
1 citations

Abstract

Industry 5.0 advances sustainable development through human-machine collaboration and personalised manufacturing. The increase in intelligent industrial equipment creates data scalability challenges for human workers who face difficulties in making decisions based on relevant data sources. Advanced interactive AI systems, capable of integrating diverse data sources and delivering real-time, context-aware insights, present promising solutions to the challenges of the industrial environment. This research introduces a retrieval-augmented generation (RAG)-enhanced Generative artificial intelligence (GenAI) chatbot to address these challenges. The system integrates a variety of information sources, including government reports, news websites, academic studies, and industry reports. This industry 5.0 chatbot aims to offer users extensive knowledge of the industrial sector through a Question-and-Answer interface. It provides relevant and accurate information through intuitive, context-aware interactions to reduce cognitive load for users, which improves decision-making efficiency and user experience. Through experimental evaluation, the RAG-enhanced GenAI chatbot significantly improves accuracy, relevance and user satisfaction, outperforming models like ChatGPT-4o. This system presents an innovative practical solution to tackle Industry 5.0 core issues particularly in enhancing human-machine collaboration and decision-making efficiency. This research contributes to the theoretical and practical development of RAG-enhanced AI systems, laying a foundation for future investigations of industrial AI interaction.

Cardiff University (GB)
Industry, innovation and infrastructure
Openalex Percentile: Top 9%
AI in Service Interactions
2.48
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Enabling Interactive AI in Industry 5.0 with RAG-Enhanced GenAI Chatbots — Yuwei Wan, Maneesh Kumar, et al. · (2025) | TGRS Research Map | TGRS