IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i1p372-d1561280.html
   My bibliography  Save this article

GDT Framework: Integrating Generative Design and Design Thinking for Sustainable Development in the AI Era

Author

Listed:
  • Yongliang Chen

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Zhongzhi Qin

    (School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

  • Li Sun

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Jiantao Wu

    (School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

  • Wen Ai

    (North Automatic Control Technology Institute, Taiyuan 030006, China)

  • Jiayuan Chao

    (School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

  • Huaixin Li

    (School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

  • Jiangnan Li

    (School of Arts and Design, Hebei Design Innovation and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China)

Abstract

The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) integrates human cognition and machine learning to enhance design automation. This study aims to explore the cognitive mechanisms underlying GDT and their impact on design efficiency. Using behavioral coding and quantitative analysis, we developed a three-tier cognitive model comprising a macro-cycle (knowledge acquisition and expression), meso-cycle (creative generation, intelligent evaluation, and feedback adjustment), and micro-cycle (knowledge base and model optimization). The findings reveal that increased task complexity elevates cognitive load, supporting the hypothesis that designers need to allocate more cognitive resources for complex problems. Knowledge base optimization significantly impacts design efficiency more than generative model refinement. Moreover, creative generation, evaluation, and feedback adjustment are interdependent, highlighting the importance of a dynamic knowledge base for creativity. This study challenges traditional design automation approaches by advocating for an adaptive framework that balances cognitive processes and machine capabilities. The results suggest that improving knowledge management and reducing cognitive load can enhance design outcomes. Future research should focus on developing flexible, real-time knowledge repositories and optimizing generative models for interdisciplinary and sustainable design contexts.

Suggested Citation

  • Yongliang Chen & Zhongzhi Qin & Li Sun & Jiantao Wu & Wen Ai & Jiayuan Chao & Huaixin Li & Jiangnan Li, 2025. "GDT Framework: Integrating Generative Design and Design Thinking for Sustainable Development in the AI Era," Sustainability, MDPI, vol. 17(1), pages 1-28, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:372-:d:1561280
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/1/372/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/1/372/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gwendolyn L. Kolfschoten & Frances M. T. Brazier, 2013. "Cognitive Load in Collaboration: Convergence," Group Decision and Negotiation, Springer, vol. 22(5), pages 975-996, September.
    2. Claire McInerney, 2002. "Knowledge management and the dynamic nature of knowledge," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(12), pages 1009-1018, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sara Alonzo, 2022. "Organizational Values and Knowledge Sharing in Public Institution: Economic-Public Sector in Guatemala," Digital Transformation: The Harmonic Convergence of People, Culture, Process, and Technology in the New Normal,, ToKnowPress.
    2. Iwona Dudek & Jean-Yves Blaise, 2017. "What Comes before a Digital Output? Eliciting and Documenting Cultural Heritage Research Processes," Post-Print halshs-01673865, HAL.
    3. Bibi Alajmi & Talal Alhaji, 2018. "Mapping the Field of Knowledge Management: Bibliometric and Content Analysis of Journal of Information & Knowledge Management for the Period from 2002–2016," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-16, September.
    4. Mohajan, Haradhan, 2016. "Sharing of Tacit Knowledge in Organizations: A Review," MPRA Paper 82958, University Library of Munich, Germany, revised 15 Jun 2016.
    5. Dimitris Tsintsaris & Milan Tsompanoglou & Evangelos Ioannidis, 2024. "Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business," Mathematics, MDPI, vol. 12(8), pages 1-27, April.
    6. Ghabban, Fahad & Selamat, Ali & Ibrahim, Roliana, 2018. "New model for encouraging academic staff in Saudi universities to use IT for knowledge sharing to improve scholarly publication performance," Technology in Society, Elsevier, vol. 55(C), pages 92-99.
    7. Swapnil Dhamal & Y. Narahari, 2015. "Formation of Stable Strategic Networks with Desired Topologies," Studies in Microeconomics, , vol. 3(2), pages 158-213, December.
    8. Zhang, Cong & Pan, Siyu & Zhao, Yanhui, 2024. "More is not always better: Examining the drivers of livestream sales from an information overload perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    9. Peter Gordon Roetzel, 2019. "Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework developmen," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 479-522, December.
    10. Xusen Cheng & Ying Bao & Xiaodan Yu & Yuanyanhang Shen, 2021. "Trust and Group Efficiency in Multinational Virtual Team Collaboration: A Longitudinal Study," Group Decision and Negotiation, Springer, vol. 30(3), pages 529-551, June.
    11. Joon Hyung Cho & So Young Sohn, 0. "Competing risk model for predicting stabilization period of university spin-off ventures," International Entrepreneurship and Management Journal, Springer, vol. 0, pages 1-20.
    12. Barão, Alexandre & de Vasconcelos, José Braga & Rocha, Álvaro & Pereira, Ruben, 2017. "A knowledge management approach to capture organizational learning networks," International Journal of Information Management, Elsevier, vol. 37(6), pages 735-740.
    13. IANCU, Eugenia & SOCACIU, Tiberiu, 2014. "Knowledge, Economic Models And Market Value Of Companies," Journal of Financial and Monetary Economics, Centre of Financial and Monetary Research "Victor Slavescu", vol. 1(1), pages 203-209.
    14. D. Mehta & Naveen K Mehta & Jitendra K. Sharma, 2011. "Knowledge Management in Economic Downturn: Indian Scenario," BRAND. Broad Research in Accounting, Negotiation, and Distribution, EduSoft Publishing, vol. 2(2), pages 16-21, October.
    15. Jankit Chotigo & Yasuo Kadono, 2021. "Were There Any Key Factors That Encourage Food Delivery Apps Adoption before the Covid-19 Pandemic in Thailand," Economy for a New Normal: Digitalisation and Human Relations in Business and Education,, ToKnowPress.
    16. Hugo Pinto & Jorge André Guerreiro & Manuel Fernández-Esquinas, 2023. "Sources of knowledge in the firm: a review on influential, internal and contextual factors in innovation dynamics," SN Business & Economics, Springer, vol. 3(2), pages 1-32, February.
    17. Yang Cai & Youming Song & Xia Xiao & Wendian Shi, 2020. "The Effect of Social Capital on Tacit Knowledge-Sharing Intention: The Mediating Role of Employee Vigor," SAGE Open, , vol. 10(3), pages 21582440209, July.
    18. Patient Rambe & Mpho Agnes Mbeo, 2017. "Technology-Enhanced Knowledge Management Framework for Retaining Research Knowledge among University Academics," Journal of Economics and Behavioral Studies, AMH International, vol. 9(1), pages 189-206.
    19. Pearce, Antony & Pons, Dirk & Neitzert, Thomas, 2018. "Implementing lean—Outcomes from SME case studies," Operations Research Perspectives, Elsevier, vol. 5(C), pages 94-104.
    20. Joon Hyung Cho & So Young Sohn, 2017. "Competing risk model for predicting stabilization period of university spin-off ventures," International Entrepreneurship and Management Journal, Springer, vol. 13(3), pages 777-796, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:372-:d:1561280. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.