AI in the Design Thinking Toolbox: What to Recommend, When, and Why
Resumo
Context: Although the use of Artificial Intelligence (AI) in software engineering is growing, the potential of Large Language Models (LLMs) to automate complex tasks in the software development process remains largely unexplored. Information-intensive decisionmaking tasks, such as selecting Design Thinking (DT) techniques for requirements elicitation, may significantly benefit from LLMbased solutions. Objective: This paper presents the automation of selecting DT techniques for specific project contexts, using LLMs as decision-support tools. We developed DT Selection Universe GPT, an LLM-based solution built on a structured repository containing 46 DT techniques and designed to operate using a prompt guided by the CRISPE model. Method: We investigated professionals’ perceptions of task automation through a qualitative study with four experienced participants from industry (INDT). The participants interacted with the tool and then participated in semi-structured interviews to share their perceptions of the experience. Results: Participants reported positive perceptions of using LLMs to automate the task of technique selection. They highlighted the system’s alignment with professional language, its adaptability to different contexts, the clarity of the recommendations provided, and the reliability of the results as key differentiators. Conclusion: The initial findings indicate that the DT Selection Universe GPT solution, developed using LLMs, shows strong potential to automate decision-support tasks for selecting DT techniques, offering benefits for novice and experienced professionals.
Palavras-chave:
Design Thinking, Design Thinking techniques, Technique Selection
Referências
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Walter Brenner, Falk Uebernickel, and Thomas Abrell. 2016. Design thinking as mindset, process, and toolbox. In Design thinking for innovation. Springer, 3–21.
Denys Dinkevych. 2023. CRISPE — ChatGPT Prompt Engineering Framework. [link] Acesso em: 02 maio 2025.
Franziska Dobrigkeit, Philipp Pajak, Danielly de Paula, and Matthias Uflacker. 2020. DT@ IT toolbox: design thinking tools to support everyday software development. In Design Thinking Research. Springer, 201–227.
Harry Essel, Dimitrios Vlachopoulos, Albert Essuman, and John Amankwa. 2023. ChatGPT effects on cognitive skills of undergraduate students: receiving instant responses from ai-based conversational large language models (LLMs). Computers And Education: Artificial Intelligence 6 (2023).
Brandon Harwood. 2023. Chai-dt: A framework for prompting conversational generative ai agents to actively participate in co-creation. arXiv preprint arXiv:2305.03852 (2023).
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Jennifer Hehn, Daniel Mendez, Falk Uebernickel, Walter Brenner, and Manfred Broy. 2019. On integrating design thinking for human-centered requirements engineering. IEEE Software 37, 2 (2019), 25–31.
Shuo Jiang and Jianxi Luo. 2024. Autotriz: Artificial ideation with triz and large language models. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 88377. American Society of Mechanical Engineers, V03BT03A055.
Shuo Jiang and Jianxi Luo. 2024. AutoTRIZ: Artificial Ideation with TRIZ and Large Language Models. Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference DETC2024-143166 (2024). DOI: 10.1115/DETC2024-143166
Christopher McComb Kosa Goucher-Lambert Kevin Ma, Daniele Grandi. 2024. Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions. Preprint (2024). DOI: 10.1115/1.4006145
Heather Lotherington, Mark Pegrum, Kurt Thumlert, Brittany Tomin, Taylor Boreland, and Tanya Pobuda. 2024. Exploring opportunities for language immersion in the posthuman spectrum: lessons learned from digital agents. Interactive Technology and Smart Education 21 (2024).
Kevin Ma, Daniele Grandi, Christopher McComb, and Kosa Goucher-Lambert. 2023. Conceptual design generation using large language models. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 87349. American Society of Mechanical Engineers, V006T06A021.
Kevin Ma, Daniele Grandi, Christopher McComb, and Kosa Goucher-Lambert. 2024. Exploring the capabilities of large language models for generating diverse design solutions. arXiv preprint arXiv:2405.02345 (2024).
Maria Meireles, Juliana Magalhães, Nasthya Barauna, Sabrina Rocha, Jose Carlos Maldonado, and Tayana Conte. 2024. Guiding theWay: Facilitating Requirements Elicitation with Selection Universe Approach. In Brazilian Symposium on Software Engineering. 158–168.
Qiyang Miao, Jiang Xu, Zhihao Song, ChengruiWang, and Yu Cui. 2024. Diamond of Thought: A Design Thinking-Based Framework for LLMs in Wearable Design. arXiv preprint arXiv:2410.06972 (2024).
Rafael Parizi, Marina Moreira, Igor Couto, Sabrina Marczak, and Tayana Conte. 2020. A design thinking techniques recommendation tool: An initial and on-going proposal. In 19th Brazilian Symposium on Software Quality. 1–6.
Rafael Parizi, Marina Moreira, Igor Couto, Sabrina Marczak, and Tayana Conte. 2020. A Design Thinking Techniques Recommendation Tool: An Initial and On-Going Proposal. In 19th Brazilian Symposium on Software Quality. 1–6.
Rafael Parizi, Matheus Prestes, Sabrina Marczak, and Tayana Conte. 2022. How has design thinking being used and integrated into software development activities? A systematic mapping. Journal of Systems and Software (2022), 111217.
Paul Ralph, Rashina Hoda, and Christoph Treude. 2020. ACM SIGSOFT empirical standards. (2020).
Jeba Rezwana and Mary Lou Maher. 2023. Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems. ACM Transactions on Computer-Human Interaction 30 (2023). DOI: 10.1145/3519026
Nicolas Rösch, Victor Tiberius, and Sascha Kraus. 2023. Design thinking for innovation: context factors, process, and outcomes. European Journal of Innovation Management 26, 7 (2023), 160–176.
Anderson Souza, Bruna Ferreira, Natasha Valentim, Lauriane Correa, Sabrina Marczak, and Tayana Conte. 2020. Supporting the teaching of design thinking techniques for requirements elicitation through a recommendation tool. IET Software 14, 6 (2020), 693–701.
Anselm Strauss and Juliet Corbin. 1998. Basics of qualitative research techniques. (1998).
Jéssyka Vilela and Carla Silva. 2023. An Experience Report on the use of Problembased learning and Design Thinking in a Requirements Engineering Postgraduate Course. In Proceedings of the XXXVII Brazilian Symposium on Software Engineering. 432–441.
Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Lidia Sam Chao, and Derek Fai Wong. 2025. A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions. Computational Linguistics 51 (2025). DOI: 10.1162/coli_a_00549
Walter Brenner, Falk Uebernickel, and Thomas Abrell. 2016. Design thinking as mindset, process, and toolbox. In Design thinking for innovation. Springer, 3–21.
Denys Dinkevych. 2023. CRISPE — ChatGPT Prompt Engineering Framework. [link] Acesso em: 02 maio 2025.
Franziska Dobrigkeit, Philipp Pajak, Danielly de Paula, and Matthias Uflacker. 2020. DT@ IT toolbox: design thinking tools to support everyday software development. In Design Thinking Research. Springer, 201–227.
Harry Essel, Dimitrios Vlachopoulos, Albert Essuman, and John Amankwa. 2023. ChatGPT effects on cognitive skills of undergraduate students: receiving instant responses from ai-based conversational large language models (LLMs). Computers And Education: Artificial Intelligence 6 (2023).
Brandon Harwood. 2023. Chai-dt: A framework for prompting conversational generative ai agents to actively participate in co-creation. arXiv preprint arXiv:2305.03852 (2023).
Brandon Harwood. 2023. CHAI-DT: A Framework for Prompting Conversational Generative AI Agents to Actively Participate in Co-Creation. ACM (2023). [link]
Jennifer Hehn, Daniel Mendez, Falk Uebernickel, Walter Brenner, and Manfred Broy. 2019. On integrating design thinking for human-centered requirements engineering. IEEE Software 37, 2 (2019), 25–31.
Shuo Jiang and Jianxi Luo. 2024. Autotriz: Artificial ideation with triz and large language models. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 88377. American Society of Mechanical Engineers, V03BT03A055.
Shuo Jiang and Jianxi Luo. 2024. AutoTRIZ: Artificial Ideation with TRIZ and Large Language Models. Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference DETC2024-143166 (2024). DOI: 10.1115/DETC2024-143166
Christopher McComb Kosa Goucher-Lambert Kevin Ma, Daniele Grandi. 2024. Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions. Preprint (2024). DOI: 10.1115/1.4006145
Heather Lotherington, Mark Pegrum, Kurt Thumlert, Brittany Tomin, Taylor Boreland, and Tanya Pobuda. 2024. Exploring opportunities for language immersion in the posthuman spectrum: lessons learned from digital agents. Interactive Technology and Smart Education 21 (2024).
Kevin Ma, Daniele Grandi, Christopher McComb, and Kosa Goucher-Lambert. 2023. Conceptual design generation using large language models. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 87349. American Society of Mechanical Engineers, V006T06A021.
Kevin Ma, Daniele Grandi, Christopher McComb, and Kosa Goucher-Lambert. 2024. Exploring the capabilities of large language models for generating diverse design solutions. arXiv preprint arXiv:2405.02345 (2024).
Maria Meireles, Juliana Magalhães, Nasthya Barauna, Sabrina Rocha, Jose Carlos Maldonado, and Tayana Conte. 2024. Guiding theWay: Facilitating Requirements Elicitation with Selection Universe Approach. In Brazilian Symposium on Software Engineering. 158–168.
Qiyang Miao, Jiang Xu, Zhihao Song, ChengruiWang, and Yu Cui. 2024. Diamond of Thought: A Design Thinking-Based Framework for LLMs in Wearable Design. arXiv preprint arXiv:2410.06972 (2024).
Rafael Parizi, Marina Moreira, Igor Couto, Sabrina Marczak, and Tayana Conte. 2020. A design thinking techniques recommendation tool: An initial and on-going proposal. In 19th Brazilian Symposium on Software Quality. 1–6.
Rafael Parizi, Marina Moreira, Igor Couto, Sabrina Marczak, and Tayana Conte. 2020. A Design Thinking Techniques Recommendation Tool: An Initial and On-Going Proposal. In 19th Brazilian Symposium on Software Quality. 1–6.
Rafael Parizi, Matheus Prestes, Sabrina Marczak, and Tayana Conte. 2022. How has design thinking being used and integrated into software development activities? A systematic mapping. Journal of Systems and Software (2022), 111217.
Paul Ralph, Rashina Hoda, and Christoph Treude. 2020. ACM SIGSOFT empirical standards. (2020).
Jeba Rezwana and Mary Lou Maher. 2023. Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems. ACM Transactions on Computer-Human Interaction 30 (2023). DOI: 10.1145/3519026
Nicolas Rösch, Victor Tiberius, and Sascha Kraus. 2023. Design thinking for innovation: context factors, process, and outcomes. European Journal of Innovation Management 26, 7 (2023), 160–176.
Anderson Souza, Bruna Ferreira, Natasha Valentim, Lauriane Correa, Sabrina Marczak, and Tayana Conte. 2020. Supporting the teaching of design thinking techniques for requirements elicitation through a recommendation tool. IET Software 14, 6 (2020), 693–701.
Anselm Strauss and Juliet Corbin. 1998. Basics of qualitative research techniques. (1998).
Jéssyka Vilela and Carla Silva. 2023. An Experience Report on the use of Problembased learning and Design Thinking in a Requirements Engineering Postgraduate Course. In Proceedings of the XXXVII Brazilian Symposium on Software Engineering. 432–441.
Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Lidia Sam Chao, and Derek Fai Wong. 2025. A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions. Computational Linguistics 51 (2025). DOI: 10.1162/coli_a_00549
Publicado
22/09/2025
Como Citar
MACEDO, Isabella; MEIRELES, Maria Alcimar Costa; BARRETO, Hannah; STEINMACHER, Igor; MALDONADO, Jose Carlos; GADELHA, Bruno; CONTE, Tayana.
AI in the Design Thinking Toolbox: What to Recommend, When, and Why. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 39. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 755-761.
ISSN 2833-0633.
DOI: https://doi.org/10.5753/sbes.2025.11538.
