Journal of Engineering Design

Human-AI Collaboration for Generative Intelligent Design

With the increasingly prominent requirements for low-cost, high-agility, and variable-flexibility research and development, engineering design has progressively evolved from a traditional paradigm relying on expert rules/experience to a Generative Intelligent Design (GID) mode driven by the integration of domain-specific intellectual resources (i.e., data, models, and knowledge) and artificial intelligence (AI) [1, 2]. Notably, with the rapid and profound development of generative-AI technologies typified by large language models (LLM) [3], these technologies are gradually subverting the traditional intelligent design paradigms and design cognitions represented by Expert Systems, Knowledge-Based Engineering (KBE), and Intelligent Computer-Aided Design (ICAD). The GID driven by human-AI collaborative intelligence achieves the automation and intelligence of the entire design process for complex equipment and systems—encompassing requirement comprehension, design scheme generation, optimization, and evaluation—through the deep collaboration between human designers and AI systems, integrating domain knowledge, multi-modal data, and generative AI algorithms [3, 4]. As the American scholar J.C.R. Licklider first proposed the concept of "Human-Machine Symbiosis" in 1960 [5], a cognitive complementarity exists between humans and AI-integrated machines: humans possess strengths in creative decision-making and value judgment, whereas AI machines excel in data storage and rapid computational reasoning.

The GID of complex engineering equipments underpinned by human-AI collaborative intelligent computing has engendered substantial innovative impetus for academic research and engineering applications, particularly in the realm of design process logical reconfiguration. Specifically:

●       In conceptual design, it involves generating multi-scheme architectures that fulfill functional requirements, implementing modular-parametric collaborative optimization, and achieving cross-domain integrative global innovation through the qualitative leap from "local optimality" to "global emergence," thereby enabling disruptive design outcomes across disciplines.

●       In detailed design, progression from parametric optimization to human-AI co-creation facilitates breakthroughs in performance ceilings within predefined architectural frameworks.

●       In manufacturing process design, it entails intelligent planning of multi-stage processes by incorporating manufacturability constraint analysis, while exploring the feasibility of novel combinations of manufacturing processes and materials.

●       In flexible production line design, it enables the rapid generation of integrated equipment modules and process flows, thus realizing multi-variety mixed-model production and personalized customized manufacturing.

Notwithstanding the technological impetus from LLM, generative AI models, and multi-agent systems, research and applications of human-AI collaborative generative intelligent design still confront substantial challenges. These challenges entail, for example: the principles of dynamic two-way feedback between humans and AI in complex generative design task scenarios [6], and how such feedback mechanisms act on domain-specific design tasks [7]; the embedding representation, transfer, and evolution of multi-modal domain knowledge to better facilitate the generation and iteration of design schemes [8, 9]; and the deep integration of LLM and multi-agentic AI technologies within design verticals to enhance the generalization, adaptability, and trustworthiness of design generation [10].

To this end, as an emerging and promising research topic, this special issue calls for papers about the state-of-the-art reviews, methodologies, tools, systems, and practical applications of human-AI collaborative generative intelligent for engineering design.

Topic Areas:

●       Conceptual frameworks/theories of human-AI collaborative intelligence for GID

●       Algorithms/Models/Methods for achieving generative intelligent design

●       Human and AI capabilities analysis for enhancing collaborative intelligence in GID

●       Human-AI intelligence-driven GID systems design and development

●       Survey and review paper about human-AI hybrid intelligence in GID

●       Practical studies/applications for human-AI collaborative intelligence

References:

[1]        A. Bordas, P. Le Masson, M. Thomas, and B. Weil, "What is generative in generative artificial intelligence? A design-based perspective," Research in Engineering Design, vol. 35, no. 4, pp. 427-443, 2024/10/01 2024.

[2]        F. Wu et al., "Towards a new generation of artificial intelligence in China," Nature Machine Intelligence, vol. 2, pp. 312-316, 06/01 2020.

[3]        M. Mariani and Y. K. Dwivedi, "Generative artificial intelligence in innovation management: A preview of future research developments," Journal of Business Research, vol. 175, p. 114542, 2024/03/01/ 2024.

[4]        L. Regenwetter, A. H. Nobari, and F. Ahmed, "Deep Generative Models in Engineering Design: A Review," ArXiv, vol. abs/2110.10863, 2021.

[5]        J. C. R. Licklider, "Man-Computer Symbiosis," IRE Transactions on Human Factors in Electronics, vol. HFE-1, no. 1, pp. 4-11, 1960.

[6]        J. I. Saadi and M. C. Yang, "Generative Design: Reframing the Role of the Designer in Early-Stage Design Process," Journal of Mechanical Design, vol. 145, no. 4, 2023.

[7]        V. Singh and N. Gu, "Towards an integrated generative design framework," Design Studies, vol. 33, no. 2, pp. 185-207, 2012/03/01/ 2012.

[8]        Z. Jiang, H. Wen, F. Han, Y. Tang, and Y. Xiong, "Data-driven generative design for mass customization: A case study," Advanced Engineering Informatics, vol. 54, p. 101786, 2022/10/01/ 2022.

[9]        A. Fitriawijaya and T. Jeng, "Integrating Multimodal Generative AI and Blockchain for Enhancing Generative Design in the Early Phase of Architectural Design Process," Buildings, vol. 14, no. 8. doi: 10.3390/buildings14082533

[10]      S. Xu, Y. Wei, P. Zheng, J. Zhang, and C. Yu, "LLM enabled generative collaborative design in a mixed reality environment," Journal of Manufacturing Systems, vol. 74, pp. 703-715, 2024/06/01/ 2024.