Journal of Engineering Design

Symbiotic Intelligence of Engineering Design for Lifecycle Innovations in Smart Products, Productions and Services

Aims and Scope

Engineering design is undergoing a profound transformation driven by the emergence of Symbiotic Intelligence, an evolving paradigm in which human expertise, artificial intelligence, autonomous systems, digital twins, and connected infrastructures collaborate to create capabilities and value beyond those achievable by any individual actor alone. Rather than viewing AI merely as a tool, this emerging paradigm positions intelligent technologies as collaborative partners that augment human creativity, decision-making, innovation, and lifecycle management across increasingly complex socio-technical systems.

This paradigm shift is transforming the design and lifecycle evolution of smart products, smart production systems, and smart services, spanning connected products, autonomous manufacturing, digital factories, smart healthcare, and AI-enabled service ecosystems. As traditional boundaries between design, production, operation, maintenance, and service delivery dissolve, engineering innovation is increasingly centered on lifecycle value creation from ideation and intellectual property generation to production, operation, upgrading, remanufacturing, and circular value recovery. Advances in generative AI, large language models, agentic systems, digital twins, cyber-physical systems, and autonomous operations are enabling symbiotic collaboration among human and machine stakeholders, creating new opportunities for engineering design, lifecycle innovation, and sustainable value creation.

This Special Issue aims to be a foundational event for establishing Symbiotic Intelligence in Engineering Design, establishing it as a unifying paradigm for lifecycle innovation in smart products, smart production systems, and smart services. Our goal is to move beyond incremental improvements and explore how the deep integration of human and artificial intelligence can fundamentally reshape design science and practice.

By bringing together researchers and practitioners from engineering design, manufacturing, service innovation, artificial intelligence, systems engineering, and innovation management, the Special Issue seeks to develop transferable design knowledge, lifecycle-oriented methodologies, and validated industrial solutions that contribute to resilient, adaptive, sustainable, and human-centric innovation ecosystems. The Special Issue welcomes original research articles, review papers, methodological contributions, industrial practice papers, perspective articles, and rigorously documented case studies that demonstrate how Symbiotic Intelligence in Engineering Design can enable lifecycle innovation, value co-creation, and sustainable system evolution across smart products, smart production systems, and smart services.

Topics of Interest

This Special Issue welcomes diverse perspectives from the domains of smart products (e.g., connected products, intelligent devices, healthcare technologies, and mobility systems), smart production systems (e.g., autonomous manufacturing, semiconductor fabrication, digital factories, robotic logistics, and cyber-physical systems), and smart services (e.g., service robots, autonomous transportation, digital healthcare, smart cities, platform ecosystems, and AI-enabled service delivery).

The Special Issue invites contributions that advance the theory, methods, and practice of Symbiotic Intelligence in Engineering Design, exploring how human and machine intelligence can be integrated to support lifecycle innovation, value co-creation, and sustainable system evolution across smart products, smart production systems, and smart services. Topics include, but are not limited to, the following:

Foundations of Symbiotic Intelligence:

·       Theoretical frameworks of symbiotic intelligence;

·       Human-AI collaboration and teaming in engineering design;

·       Hybrid intelligence for innovation and decision-making;

·       Collective intelligence and distributed engineering decision-making;

·        Computational creativity and AI-augmented design

·        Trust, ethics, and governance in human-AI design systems

·        Societal impacts and responsible AI design

 

Smart Products, Production Systems, and Services:

·        Smart product ecosystem design

·        Smart product innovation

·        Smart factories and autonomous manufacturing

·        Service robots design and development

·        Digital servitization and product-service systems

 

Lifecycle Innovation:

·       Lifecycle engineering methodologies;

·       Predictive maintenance and intelligent asset management;

·       Product, production, and service digital twins;

·       Twin-enabled lifecycle management and decision support;

·       Agent-based lifecycle management;

·       Engineering design for business model innovation;

·       Symbiotic value creation among stakeholders;

·       Sustainability assessment and lifecycle metrics.

·       Lifecycle optimization using AI analytics.

 

Industrial Applications and Case Studies: This SI explicitly welcomes industrial contributions and practice-oriented perspectives to strengthen the connection between research and real-world applications, including industry case studies documenting deployment challenges and solutions, practitioner reports on technology adoption and organizational change, benchmark datasets and evaluation protocols derived from real operational environments, and joint industry-academia studies that translate research findings into deployable systems.

 

Guest Editors

Ching-Hung Lee, Xi'an Jiaotong University, China; leechinghung@xjtu.edu.cn

Fan Li, The Hong Kong Polytechnic University, Hong Kong; fan-5.li@polyu.edu.hk

Amy Trappey, National Tsing Hua University, Taiwan; trappey@ie.nthu.edu.tw

Kendrik Yan Hong Lim, Massachusetts Institute of Technology, Cambridge, USA; Kendrik1@mit.edu

 

Important Date:

Submission deadline:  01/07/2027

Expected Date of Publication:  31/12/2027

References:

Yu, C., Yan, T., & Chen, Y. (2026). Meta-X design: from tools to partners in human–machine co–creation for industry 5.0. Journal of Engineering Design, 37(5), 2117-2144.

Huang, R., Yang, T., Feng, S., & Li, F. (2025). Design coco-pilot: QFD as decision logic for conflict-aware human–LLM collaborative product conceptual design. Journal of Engineering Design. Advance online publication. https://doi.org/10.1080/09544828.2025.2578826

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.

Lee, C.-H., Chen, C.-H., & Trappey, A. J. C. (2019). A structural service innovation approach for designing smart product service systems: Case study of smart beauty service. Advanced Engineering Informatics, 40, 154–167. https://doi.org/10.1016/j.aei.2019.04.006

Lee, C.-H., Li, L., Wang, D., & Wee, H. J. (2022). Strategic servitization design method for Industry 4.0-based smart intralogistics and production. Expert Systems with Applications, 204, Article 117480. https://doi.org/10.1016/j.eswa.2022.117480

Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020). A state-of-the-art survey of digital twin: Techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31(6), 1313–1337. https://doi.org/10.1007/s10845-019-01512-w

Lim, K. Y. H., Dang, L. V., & Chen, C.-H. (2024). Incorporating supply and production digital twins to mitigate demand disruptions in multi-echelon networks. International Journal of Production Economics, 273, Article 109258. https://doi.org/10.1016/j.ijpe.2024.109258

Shie, A.-J., Lin, X., Xu, E.-M., Zhang, Y., Ye, Z.-Z., Ruan, J.-Q., Fang, Z., & Lee, C.-H. (2026). Kansei-driven and TRIZ-informed product service systems design with abductive logic: Case study of children’s digital reading book. Journal of Engineering Design, 37(2), 372–405. https://doi.org/10.1080/09544828.2025.2502187

Trappey, A. J. C., Wu, C.-Y., Lee, Y.-C., & Hung, L.-P. (2025). Generative AI approach for inventive process visualisation—enhancing human-AI hybrid understanding and comparing of patents. Journal of Engineering Design. Advance online publication. https://doi.org/10.1080/09544828.2025.2518657