Abstract
In an era characterized by the relentless emergence of big data and the continuous evolution of artificial intelligence, the traditional design chain is undergoing a significant reconstruction. The performance of existing general-purpose large language models (LLMs) in specific domains falls considerably short of expectations. This study aims to invigorate the utilization of a vast amount of high-quality design data, integrating multimodal methods to deeply embed AI into the design process. Taking industrial design as a case study, this research selected databases of entries from three internationally recognized design awards, encompassing key data fields such as names of works, award statuses, and design descriptions, totaling 84,773 design data entries. Through extracting and analyzing design data, this research fully exploits the data resources of the design and intelligent manufacturing industry, constructing a heuristic design innovation method that incorporates information from award-winning works. By fusing design data with LLM, this study developed DIABot, a heuristic design innovation tool based on LLMs, inspired by the ReAct method. Combining extensive design data, DIABot offers design guidance through data retrieval integration. Furthermore, an evaluation test involving nine participants was organized to compare DIABot with human designers and ChatGPT in terms of design assistance, with the results affirming DIABot’s effectiveness in supporting design tasks. This research provides valuable insights and explorations into the application of AI in the design field, offering designers more comprehensive, refined, and precise support, thereby promoting the deeper development and application of artificial intelligence in industrial design.
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References
Zhang, F.: Design and implementation of industrial design and transformation system based on artificial intelligence technology. Math. Probl. Eng. 2022, 1–9 (2022)
Schimpf, C., Goldstein, M.H.: Large data for design research: an educational technology framework for studying design activity using a big data approach. Front. Manuf. Technol. 2, 971410 (2022)
Göpfert, J., Weinand, J.M., Kuckertz, P., Stolten, D.: Opportunities for large language models and discourse in engineering design. arXiv preprint arXiv:2306.09169 (2023)
Zhong, M., et al.: Towards a unified multi-dimensional evaluator for text generation. arXiv preprint arXiv:2210.07197 (2022)
Taylor, R., et al.: Galactica: a large language model for science. arXiv preprint arXiv:2211.09085 (2022)
Yager, K.G.: Domain-specific chatbots for science using embeddings. Digital Disc. 2(6), 1850–1861 (2023)
Ling, C., et al.: Beyond one-model-fits-all: a survey of domain specialization for large language models. arXiv preprint arXiv:2305.18703 (2023)
Luo, Z., et al.: Augmented large language models with parametric knowledge guiding. arXiv preprint arXiv:2305.04757 (2023)
Karabacak, M., Margetis, K.: Embracing large language models for medical applications: opportunities and challenges. Cureus 15(5), 1–5 (2023)
Ge, Y., Hua, W., Ji, J., Tan, J., Xu, S., Zhang, Y.: Openagi: when LLM meets domain experts. arXiv preprint arXiv:2304.04370 (2023)
de la Rosa, J., Pozo, Á.P., Ros, S., González-Blanco, E.: Alberti, a multilingual domain specific language model for poetry analysis. arXiv preprint arXiv:2307.01387 (2023)
Mukherjee, M., Hellendoorn, V.J.: Stack over-flowing with results: the case for domain-specific pre-training over one-size-fits-all models. arXiv preprint arXiv:2306.03268 (2023)
Gill, T.G.: Early expert systems: where are they now? MIS Q. 51–81 (1995)
Buchanan, B.G., Smith, R.G.: Fundamentals of expert systems. Ann. Rev. Comput. Sci. 3(1), 23–58 (1988)
Liao, S.-H.: Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)
Tripathi, K.P.: A review on knowledge-based expert system: concept and architecture. IJCA Spec. Issue Artif. Intell. Techn. Novel Approaches Pract. Appl. 4, 19–23 (2011)
Fu, Y., Li, C., Yu, F.R., Luan, T.H., Zhang, Y.: Hybrid autonomous driving guidance strategy combining deep reinforcement learning and expert system. IEEE Trans. Intell. Transport. Syst. 23(8), 11273–11286 (2021)
Leith, P.: The rise and fall of the legal expert system. Eur. J. Law Technol. 1(1), 179–201 (2010)
McComb, C., Boatwright, P., Cagan, J.: Focus and modality: defining a roadmap to future AI-human teaming in design. Proc. Design Soc. 3, 1905–1914 (2023)
Mesbah, S., Arous, I., Yang, J., Bozzon, A.: Hybrideval: a human-AI collaborative approach for evaluating design ideas at scale. In: Proceedings of the ACM Web Conference 2023, pp. 3837–3848 (2023)
Jin, X., Evans, M., Dong, H., Yao, A.: Design heuristics for artificial intelligence: inspirational design stimuli for supporting UX designers in generating AI-powered ideas. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–8 (2021)
Windl, M., Feger, S.S., Zijlstra, L., Schmidt, A., Wozniak, P.W.: ‘It is not always discovery time’: four pragmatic approaches in designing AI systems. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2022)
Wang, G., Zhao, J., Van Kleek, M., Shadbolt, N.: 12 ways to empower: designing for children’s digital autonomy. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1–27 (2023)
Gmeiner, F., Yang, H., Yao, L., Holstein, K., Martelaro, N.: Exploring challenges and opportunities to support designers in learning to co-create with AI-based manufacturing design tools. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1–20 (2023)
Lukowicz, P., et al.: Towards responsible AI: developing explanations to increase human-AI collaboration. In: HHAI 2023: Augmenting Human Intellect: Proceedings of the Second International Conference on Hybrid Human-Artificial Intelligence, vol. 368, p. 470. IOS Press (2023)
Mao, Y., Rafner, J., Wang, Y., Sherson, J.: A hybrid intelligence approach to training generative design assistants: partnership between human experts and AI enhanced co-creative tools. In: HHAI 2023: Augmenting Human Intellect, pp. 108–123. IOS Press (2023)
Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey. arXiv preprint arXiv:2312.10997 (2023)
Douze, M., et al.: The faiss library (2024)
Acknowledgments
This project is funded by the General Research Project of the Zhejiang Provincial Department of Education “Research and Application of Smart Home Product Design Pathways Driven by AIGC Technology” (Y202354062), the National Social Science Fund Major Project in Art Studies (20ZD09), and the 2023 Design-AI Lab Research Project “Research on Design Intelligence Manufacturing AI System Tools Integrating Design Data with LLMs”.
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Appendices
A Prompt of DIAbot
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B Tools OpenAPI
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Zhou, L., Zhang, L., Chen, X., Ding, Y., Wang, Y. (2024). The Heuristic Design Innovation Approach for Data-Integrated Large Language Model. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2024. Lecture Notes in Computer Science(), vol 14736. Springer, Cham. https://doi.org/10.1007/978-3-031-60615-1_12
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