Defining the roles of large language models (LLM) agents in the model of design
DOI:
https://doi.org/10.30837/2522-9818.2025.3.058Keywords:
large language models; LLM agents; artificial intelligence; human-computer interaction; design innovations.Abstract
The subject of the study is the capabilities and limitations that large language models (LLM) demonstrate when they are implemented in intellectual, technical and creative processes, in particular in design. The goal of the work is to determine the place and roles of LLM-agents in design and to develop an appropriate model of the design process augmented by LLM-agents. The article sets the following tasks: (1) to conduct a review of modern publications on approaches and methods for assessing the capabilities of large language models, in particular, when performing creative, technical and design tasks; (2) to conduct an analysis of modern approaches and methods of interaction with LLM; (3) to develop a model that defines the place and roles of LLM agents in design in interaction with the design team, the external environment and design artifacts. The following methods were used during the study: comparative-historical and retrospective analysis of the content of technical, economic, philosophical, linguistic scientific and methodological research to form a holistic vision of the current state of development of large language models and approaches to interaction with them; structural-logical analysis for the formation of a model of the design process, augmented by LLM agents. The following results were achieved: nine main groups of abilities of large language models were identified; the main modern patterns of interaction with large language models were identified; a model of the design was developed as an iterative process of knowledge enrichment, which allows describing the interaction of people and LLM-agents with each other, with the external environment and with design artifacts. The conclusions emphasize the novelty of such abilities of LLM in the family of artificial intelligence technologies as understanding and generating text in natural and programming languages, multilingualism, possession of general and industry knowledge, the ability to reason, and agency. Additionally, the need for conceptual understanding of the place and role of large language models in creative processes is highlighted. A structural model has been developed that represents design as an iterative process of knowledge enrichment and allows to define the roles of LLM-agents in interaction with the design team, artifacts, and the external environment.
References
Список літератури
Perrault R., Clark J. Artificial Intelligence Index Report 2024. 2024. URL: https://hai.stanford.edu/ai-index/2024-ai-index-report
Learning to Reason with LLMs. OpenAI. 2024. URL: https://openai.com/index/learning-to-reason-with-llms/
Today, we shared evals for an early version of the next model in our o-model reasoning series: OpenAI o3. OpenAI Twitter Account. URL: https://x.com/OpenAI/status/1870186518230511844
Perrault R. et al. Artificial Intelligence Index Report 2025. 2025. URL: https://hai.stanford.edu/ai-index/2025-ai-index-report
Chumachenko D. et al. "Glossary of Terms in the Field of Artificial Intelligence. Ministry of Digital Transformation of Ukraine. 2024. 37 p.
Raji, I. et al. AI and the everything in the whole wide world benchmark. arXiv preprint arXiv:2111.15366. 2021. DOI: 10.48550/arXiv.2111.15366
Chang Y. et al. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology. 2024. No. 15.3. P. 1-45. DOI: 10.1145/3641289
Schulhoff S. et al. The Prompt Report: A Systematic Survey of Prompting Techniques. arXiv preprint arXiv:2406.06608. 2024. DOI:10.48550/arXiv.2406.06608
Zhou Z. et al. Examining how the large language models impact the conceptual design with human designers: A comparative case study. International Journal of Human–Computer Interaction. 2024. P. 1-17. DOI: 10.1080/10447318.2024.2370635
Novakovskyi A., Yaloveha I. Implementation of generative artificial intelligence technologies in creative activities: development of a structural model of design thinking. Innovative Technologies and Scientific Solutions for Industries. 2024. No. 2(28). P. 108–120. DOI: 10.30837/2522-9818.2024.2.108
Chakrabarty T. et al. Art or artifice? large language models and the false promise of creativity. Proceedings of the CHI Conference on Human Factors in Computing Systems. 2024. DOI: 10.1145/3613904.3642731
Zhao Y. et al. Assessing and understanding creativity in large language models. arXiv preprint arXiv:2401.12491. 2024. DOI: 10.48550/arXiv.2401.12491
Tholander J., Jonsson M. Design ideation with ai-sketching, thinking, and talking with Generative Machine Learning Models. Proceedings of the 2023 ACM Designing Interactive Systems Conference. 2023. P. 1930–1940. DOI: 10.1145/3563657.3596014
Thoring K., Huettemann S., Mueller R. M. The augmented designer: a research agenda for generative AI-enabled design. Proceedings of the Design Society. 2023. No. 3. P. 3345-3354. DOI:10.1017/pds.2023.335
Xi Z. et al. The rise and potential of large language model based agents: A survey. Science China Information Sciences. 2025. No. 68.2. 121101. DOI: 10.1007/s11432-024-4222-0
Hou X. et al. Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology. 2024. No. 33.8. P. 1-79. DOI: 10.1145/3695988
Guo T. et al. Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680. 2024. DOI: 10.48550/arXiv.2402.01680
Johansson‐Sköldberg U., Woodilla J., Çetinkaya M. Design thinking: Past, present, and possible futures. Creativity and innovation management. 2013. No. 2. P. 121-146. DOI: 10.1111/caim.12023
References
Perrault R., Clark J. (2024), "Artificial Intelligence Index Report 2024", available at: https://hai.stanford.edu/ai-index/2024-ai-index-report
Learning to Reason with LLMs, OpenAI. 2024, available at: https://openai.com/index/learning-to-reason-with-llms/
"Today, we shared evals for an early version of the next model in our o-model reasoning series: OpenAI o3", OpenAI Twitter Account, available at: https://x.com/OpenAI/status/1870186518230511844
Perrault R. et al (2025), "Artificial Intelligence Index Report 2025", available at: https://hai.stanford.edu/ai-index/2025-ai-index-report
Chumachenko D. et al. (2024), "Glossary of Terms in the Field of Artificial Intelligence", Ministry of Digital Transformation of Ukraine, 37 p.
Raji, I. et al. (2021), "AI and the everything in the whole wide world benchmark", arXiv preprint arXiv:2111.15366. DOI: 10.48550/arXiv.2111.15366
Chang Y. et al. (2024), "A survey on evaluation of large language models", ACM Transactions on Intelligent Systems and Technology, No. 15.3, P. 1-45. DOI: 10.1145/3641289
Schulhoff S. et al. (2024), "The Prompt Report: A Systematic Survey of Prompting Techniques", arXiv preprint arXiv:2406.06608. DOI:10.48550/arXiv.2406.06608
Zhou Z. et al. (2024), "Examining how the large language models impact the conceptual design with human designers: A comparative case study", International Journal of Human–Computer Interaction, P. 1-17. DOI: 10.1080/10447318.2024.2370635
Novakovskyi A., Yaloveha I. (2024), "Implementation of generative artificial intelligence technologies in creative activities: development of a structural model of design thinking", Innovative Technologies and Scientific Solutions for Industries, No. 2(28), P. 108–120. DOI: 10.30837/2522-9818.2024.2.108
Chakrabarty T. et al. (2024), "Art or artifice? large language models and the false promise of creativity", Proceedings of the CHI Conference on Human Factors in Computing Systems. P. 1-34. DOI: 10.1145/3613904.3642731
Zhao Y. et al. (2024), "Assessing and understanding creativity in large language models", arXiv preprint arXiv:2401.12491. DOI: 10.48550/arXiv.2401.12491
Tholander J., Jonsson M. (2023), "Design ideation with ai-sketching, thinking, and talking with Generative Machine Learning Models", Proceedings of the 2023 ACM Designing Interactive Systems Conference, P. 1930–1940. DOI: 10.1145/3563657.3596014
Thoring K., Huettemann S., Mueller R. M. (2023), "The augmented designer: a research agenda for generative AI-enabled design", Proceedings of the Design Society, No. 3, P. 3345-3354. DOI:10.1017/pds.2023.335
Xi Z. et al. (2025), "The rise and potential of large language model based agents: A survey", Science China Information Sciences, No. 68.2, 121101 р. DOI: 10.1007/s11432-024-4222-0
Hou X. et al. (2024), "Large language models for software engineering: A systematic literature review", ACM Transactions on Software Engineering and Methodology, No. 33.8, P. 1-79. DOI: 10.1145/3695988
Guo T. et al. (2024), "Large language model based multi-agents: A survey of progress and challenges", arXiv preprint arXiv:2402.01680. DOI: 10.48550/arXiv.2402.01680
Johansson‐Sköldberg U., Woodilla J., Çetinkaya M. (2013), "Design thinking: Past, present, and possible futures", Creativity and innovation management, No. 2, P. 121-146. DOI: 10.1111/caim.12023
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.












