Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design

Xin Kang; Xin-Zhu Li; Xiaoxia Bai; Yi Zhang; Shiyi Chen; Keying Wang

doi:doi:10.11648/j.edu.20251403.15

Research Article |

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Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design

Xin Kang

, Xin-Zhu Li^*

, Xiaoxia Bai, Yi Zhang, Shiyi Chen, Keying Wang

Published in Education Journal (Volume 14, Issue 3)

Received: 23 April 2025 Accepted: 3 June 2025 Published: 11 June 2025

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Abstract

With the rapid development of generative artificial intelligence (AI), prompt language has emerged as a crucial interface for human–AI collaboration, reshaping interaction design processes and the practice of design thinking. This study situates itself within an interaction design course, integrating the five-stage model of design thinking (empathize, define, ideate, prototype, test) with project-based learning to systematically explore the structural characteristics and cognitive pathways of prompt language. By analyzing 362 prompts created by 64 students, the study identifies five types of prompt structures—directive, descriptive, narrative, comparative, and hybrid—and reveals their significant impact on the accuracy, stylistic consistency, and creativity of the AI-generated content. Furthermore, the study proposes a “Prompt Interaction Design Model,” highlighting that prompt language in AI-assisted design functions not only as a task-oriented tool but also as a medium for expressing design thinking and as a vehicle for learning and reflection. This model provides a practical and operable language training framework for design education, enabling students to develop skills in prompt crafting, critical thinking, and creative experimentation. Ultimately, this research contributes to the pedagogical integration of AI tools in design education, offering insights for educators aiming to enhance AI literacy and foster innovative design practices among students.

Published in	Education Journal (Volume 14, Issue 3)
DOI	10.11648/j.edu.20251403.15
Page(s)	126-133
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Generative Artificial Intelligence, Prompt Language, Interaction Design, Design Thinking, Project-Based Learning, Human–AI Collaboration

References

[1]	Gachago, D., et al., Designing in the Times of AI: Co-Creation as a Strategy towards Emergent Learning Design. African Journal of Inter/Multidisciplinary Studies, 2024. 6(1): p. 1-13. https://doi.org/10.51415/ajims.v6i1.1536
[2]	Chellakkannu, S. Embracing Generative AI in Design: Practical Implementations. in International Conference on Artificial Intelligence and its Applications in the Age of Digital Transformation. 2024. Springer. https://doi.org/10.1007/978-3-031-71429-0_15
[3]	Zhu, Z., et al., AI assistance in enterprise workflows: Enhancing design brief creation for designers. Preprints 2023, 2023. 2023111231. https://doi.org/10.3389/frai.2024.1404647
[4]	Burlin, C., Explainability to enhance creativity: A human-centered approach to prompt engineering and task allocation in text-to-image models for design purposes. 2023.
[5]	Brown, T., et al., Language models are few-shot learners. Advances in neural information processing systems, 2020. 33: p. 1877-1901.
[6]	Sahoo, P., et al., A systematic survey of prompt engineering in large language models: Techniques and applications. 2024. https://doi.org/10.48550/arXiv.2402.07927
[7]	Brown, T., Change by design: How design thinking creates new alternatives for business and society. Collins Business, 2009. https://doi.org/10.23860/MGDR-2019-04-02-08
[8]	Muehlhaus, M. and J. Steimle, Interaction Design with Generative AI: An Empirical Study of Emerging Strategies Across the Four Phases of Design. 2024. https://doi.org/10.48550/arXiv.2411.02662
[9]	Wei, J., et al., Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 2022. 35: p. 24824-24837. https://doi.org/10.48550/arXiv.2201.11903
[10]	Zheng, C., et al. Charting the future of AI in project-based learning: a Co-design exploration with students. in Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 2024. https://doi.org/10.1145/3613904.3642807
[11]	Kučević, E., et al. The prompt-a-thon: designing a format for value co-creation with generative AI for research and practice. in Proceedings of the Hawaii International Conference on System Sciences (HICSS2024). 2024. https://doi.org/10.24251/HICSS.2024.200
[12]	Liu, X., et al., P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv: 2110.07602, 2021.

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APA Style

Kang, X., Li, X., Bai, X., Zhang, Y., Chen, S., et al. (2025). Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design. Education Journal, 14(3), 126-133. https://doi.org/10.11648/j.edu.20251403.15

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ACS Style

Kang, X.; Li, X.; Bai, X.; Zhang, Y.; Chen, S., et al. Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design. Educ. J. 2025, 14(3), 126-133. doi: 10.11648/j.edu.20251403.15

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AMA Style

Kang X, Li X, Bai X, Zhang Y, Chen S, et al. Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design. Educ J. 2025;14(3):126-133. doi: 10.11648/j.edu.20251403.15

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@article{10.11648/j.edu.20251403.15,
  author = {Xin Kang and Xin-Zhu Li and Xiaoxia Bai and Yi Zhang and Shiyi Chen and Keying Wang},
  title = {Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design
},
  journal = {Education Journal},
  volume = {14},
  number = {3},
  pages = {126-133},
  doi = {10.11648/j.edu.20251403.15},
  url = {https://doi.org/10.11648/j.edu.20251403.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.edu.20251403.15},
  abstract = {With the rapid development of generative artificial intelligence (AI), prompt language has emerged as a crucial interface for human–AI collaboration, reshaping interaction design processes and the practice of design thinking. This study situates itself within an interaction design course, integrating the five-stage model of design thinking (empathize, define, ideate, prototype, test) with project-based learning to systematically explore the structural characteristics and cognitive pathways of prompt language. By analyzing 362 prompts created by 64 students, the study identifies five types of prompt structures—directive, descriptive, narrative, comparative, and hybrid—and reveals their significant impact on the accuracy, stylistic consistency, and creativity of the AI-generated content. Furthermore, the study proposes a “Prompt Interaction Design Model,” highlighting that prompt language in AI-assisted design functions not only as a task-oriented tool but also as a medium for expressing design thinking and as a vehicle for learning and reflection. This model provides a practical and operable language training framework for design education, enabling students to develop skills in prompt crafting, critical thinking, and creative experimentation. Ultimately, this research contributes to the pedagogical integration of AI tools in design education, offering insights for educators aiming to enhance AI literacy and foster innovative design practices among students.
},
 year = {2025}
}

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TY  - JOUR
T1  - Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design

AU  - Xin Kang
AU  - Xin-Zhu Li
AU  - Xiaoxia Bai
AU  - Yi Zhang
AU  - Shiyi Chen
AU  - Keying Wang
Y1  - 2025/06/11
PY  - 2025
N1  - https://doi.org/10.11648/j.edu.20251403.15
DO  - 10.11648/j.edu.20251403.15
T2  - Education Journal
JF  - Education Journal
JO  - Education Journal
SP  - 126
EP  - 133
PB  - Science Publishing Group
SN  - 2327-2619
UR  - https://doi.org/10.11648/j.edu.20251403.15
AB  - With the rapid development of generative artificial intelligence (AI), prompt language has emerged as a crucial interface for human–AI collaboration, reshaping interaction design processes and the practice of design thinking. This study situates itself within an interaction design course, integrating the five-stage model of design thinking (empathize, define, ideate, prototype, test) with project-based learning to systematically explore the structural characteristics and cognitive pathways of prompt language. By analyzing 362 prompts created by 64 students, the study identifies five types of prompt structures—directive, descriptive, narrative, comparative, and hybrid—and reveals their significant impact on the accuracy, stylistic consistency, and creativity of the AI-generated content. Furthermore, the study proposes a “Prompt Interaction Design Model,” highlighting that prompt language in AI-assisted design functions not only as a task-oriented tool but also as a medium for expressing design thinking and as a vehicle for learning and reflection. This model provides a practical and operable language training framework for design education, enabling students to develop skills in prompt crafting, critical thinking, and creative experimentation. Ultimately, this research contributes to the pedagogical integration of AI tools in design education, offering insights for educators aiming to enhance AI literacy and foster innovative design practices among students.

VL  - 14
IS  - 3
ER  -

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Author Information

Xin Kang

School of Design, NingboTech University, Ningbo, China

http://orcid.org/0000-0002-2383-7036
Xin-Zhu Li

Department of Digital Media Design, Asia University, Taichung, Taiwan

Contact Email

http://orcid.org/0000-0002-6459-793X
Xiaoxia Bai

School of Design, NingboTech University, Ningbo, China
Yi Zhang

School of Design, NingboTech University, Ningbo, China
Shiyi Chen

School of Design, NingboTech University, Ningbo, China
Keying Wang

School of Design, NingboTech University, Ningbo, China

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Plain Text BibTeX RIS

APA Style

Kang, X., Li, X., Bai, X., Zhang, Y., Chen, S., et al. (2025). Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design. Education Journal, 14(3), 126-133. https://doi.org/10.11648/j.edu.20251403.15

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ACS Style

Kang, X.; Li, X.; Bai, X.; Zhang, Y.; Chen, S., et al. Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design. Educ. J. 2025, 14(3), 126-133. doi: 10.11648/j.edu.20251403.15

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AMA Style

Kang X, Li X, Bai X, Zhang Y, Chen S, et al. Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design. Educ J. 2025;14(3):126-133. doi: 10.11648/j.edu.20251403.15

Copy | Download

@article{10.11648/j.edu.20251403.15,
  author = {Xin Kang and Xin-Zhu Li and Xiaoxia Bai and Yi Zhang and Shiyi Chen and Keying Wang},
  title = {Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design
},
  journal = {Education Journal},
  volume = {14},
  number = {3},
  pages = {126-133},
  doi = {10.11648/j.edu.20251403.15},
  url = {https://doi.org/10.11648/j.edu.20251403.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.edu.20251403.15},
  abstract = {With the rapid development of generative artificial intelligence (AI), prompt language has emerged as a crucial interface for human–AI collaboration, reshaping interaction design processes and the practice of design thinking. This study situates itself within an interaction design course, integrating the five-stage model of design thinking (empathize, define, ideate, prototype, test) with project-based learning to systematically explore the structural characteristics and cognitive pathways of prompt language. By analyzing 362 prompts created by 64 students, the study identifies five types of prompt structures—directive, descriptive, narrative, comparative, and hybrid—and reveals their significant impact on the accuracy, stylistic consistency, and creativity of the AI-generated content. Furthermore, the study proposes a “Prompt Interaction Design Model,” highlighting that prompt language in AI-assisted design functions not only as a task-oriented tool but also as a medium for expressing design thinking and as a vehicle for learning and reflection. This model provides a practical and operable language training framework for design education, enabling students to develop skills in prompt crafting, critical thinking, and creative experimentation. Ultimately, this research contributes to the pedagogical integration of AI tools in design education, offering insights for educators aiming to enhance AI literacy and foster innovative design practices among students.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Exploring the Structural Logic and Learning Path of Prompt Language in AI-Assisted Interaction Design

AU  - Xin Kang
AU  - Xin-Zhu Li
AU  - Xiaoxia Bai
AU  - Yi Zhang
AU  - Shiyi Chen
AU  - Keying Wang
Y1  - 2025/06/11
PY  - 2025
N1  - https://doi.org/10.11648/j.edu.20251403.15
DO  - 10.11648/j.edu.20251403.15
T2  - Education Journal
JF  - Education Journal
JO  - Education Journal
SP  - 126
EP  - 133
PB  - Science Publishing Group
SN  - 2327-2619
UR  - https://doi.org/10.11648/j.edu.20251403.15
AB  - With the rapid development of generative artificial intelligence (AI), prompt language has emerged as a crucial interface for human–AI collaboration, reshaping interaction design processes and the practice of design thinking. This study situates itself within an interaction design course, integrating the five-stage model of design thinking (empathize, define, ideate, prototype, test) with project-based learning to systematically explore the structural characteristics and cognitive pathways of prompt language. By analyzing 362 prompts created by 64 students, the study identifies five types of prompt structures—directive, descriptive, narrative, comparative, and hybrid—and reveals their significant impact on the accuracy, stylistic consistency, and creativity of the AI-generated content. Furthermore, the study proposes a “Prompt Interaction Design Model,” highlighting that prompt language in AI-assisted design functions not only as a task-oriented tool but also as a medium for expressing design thinking and as a vehicle for learning and reflection. This model provides a practical and operable language training framework for design education, enabling students to develop skills in prompt crafting, critical thinking, and creative experimentation. Ultimately, this research contributes to the pedagogical integration of AI tools in design education, offering insights for educators aiming to enhance AI literacy and foster innovative design practices among students.

VL  - 14
IS  - 3
ER  -

Copy | Download