Skip to main content

The Heuristic Design Innovation Approach for Data-Integrated Large Language Model

  • Conference paper
  • First Online:
Artificial Intelligence in HCI (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14736))

Included in the following conference series:

  • 112 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhang, F.: Design and implementation of industrial design and transformation system based on artificial intelligence technology. Math. Probl. Eng. 2022, 1–9 (2022)

    Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

  4. Zhong, M., et al.: Towards a unified multi-dimensional evaluator for text generation. arXiv preprint arXiv:2210.07197 (2022)

  5. Taylor, R., et al.: Galactica: a large language model for science. arXiv preprint arXiv:2211.09085 (2022)

  6. Yager, K.G.: Domain-specific chatbots for science using embeddings. Digital Disc. 2(6), 1850–1861 (2023)

    Article  Google Scholar 

  7. Ling, C., et al.: Beyond one-model-fits-all: a survey of domain specialization for large language models. arXiv preprint arXiv:2305.18703 (2023)

  8. Luo, Z., et al.: Augmented large language models with parametric knowledge guiding. arXiv preprint arXiv:2305.04757 (2023)

  9. Karabacak, M., Margetis, K.: Embracing large language models for medical applications: opportunities and challenges. Cureus 15(5), 1–5 (2023)

    Google Scholar 

  10. Ge, Y., Hua, W., Ji, J., Tan, J., Xu, S., Zhang, Y.: Openagi: when LLM meets domain experts. arXiv preprint arXiv:2304.04370 (2023)

  11. 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)

  12. 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)

  13. Gill, T.G.: Early expert systems: where are they now? MIS Q. 51–81 (1995)

    Google Scholar 

  14. Buchanan, B.G., Smith, R.G.: Fundamentals of expert systems. Ann. Rev. Comput. Sci. 3(1), 23–58 (1988)

    Article  Google Scholar 

  15. Liao, S.-H.: Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)

    Article  Google Scholar 

  16. 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)

    Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Leith, P.: The rise and fall of the legal expert system. Eur. J. Law Technol. 1(1), 179–201 (2010)

    Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. 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)

    Google Scholar 

  27. Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey. arXiv preprint arXiv:2312.10997 (2023)

  28. Douze, M., et al.: The faiss library (2024)

    Google Scholar 

Download references

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”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lingyan Zhang .

Editor information

Editors and Affiliations

Appendices

A Prompt of DIAbot

figure c
figure d
figure e

B Tools OpenAPI

figure f
figure g

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-60615-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-60614-4

  • Online ISBN: 978-3-031-60615-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics