Kate Sangwon LEE1,* and Jung-Joo LEE2
1Engineering Design and Innovation Centre, College of Design and Engineering (CDE)
2Division of Industrial Design, CDE
Lee, K. S. W., & Lee, J. J. (2024). Using generative AI in design thinking courses: Towards educators’ guidelines [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-kswlee-jjlee/
SUB-THEME
Opportunities from Generative AI
KEYWORDS
Generative AI, Design-thinking, UIUX course, students assignment
CATEGORY
Paper Presentation
INTRODUCTION
Generative AI (GenAI) applications have been extensively used in students’ assignments in design thinking courses (Hsiao & Tang, 2024) to express ideas and complete tasks (Chung et al., 2024), as shown in Figure 1. However, there are few clear guidelines about their usage due to GenAI’s novelty (Sun et al., 2024; Tholander & Jonsson, 2023; Wadinambiarachchi et al., 2024). This lack of guidance may confuse students and instructors regarding assignment guidelines and evaluation (Chung et al., 2024; Wadinambiarachchi et al., 2024). Therefore, this paper shares findings from observations on the current usage of GenAI by students in three design thinking courses at the College of Design and Engineering (CDE) at NUS during 2023-2024, aiming to identify challenges and opportunities. Finally, this paper proposes guidelines outlining students’ usage of GenAI at each stage of the design thinking process.
Figure 1. Observed GenAI usages on the design thinking process.
METHOD
The three courses of CDE, including CDE3301R “Ideas to Proof-of-Concept”, CDE4301A “Ideas to Start-up”, and CDE5311 “Essential Skills in UI/UX Design”, were observed by instructors in 2023-2024, and students’ assignments served as supplementary material. All three courses were design thinking-based, and students conducted relevant methods in each phase as their projects progressed. They used GenAI in this process, and the lecturer and teaching assistant analysed the submitted assignments in terms of the GenAI usage’s relevance and effectiveness.
FINDINGS
Inspired and restricted at the same time by conceptual images (Discover phase)
- In CDE3301R, students used the GenAI, Midjourney, to create conceptual images (Figure 2) by inputting project keywords as prompts to get inspiration for prototypes. Though the images helped specify initial ideas, they sometimes restricted students’ imagination boundaries.
Figure 2. An example of a GenAI-generated conceptual image.
Ambiguity in AI-generated images for storyboard (Define phase)
- Students used GenAI to create storyboards in CDE5311 (Figure 3). However, many of the storyboards were abstract and ambiguous, which is not aligned with the method’s purpose. A storyboard as a UX method should convey an accurate environment or facial expressions, which are desired to be shown to describe the exact situations and users’ pain points, such as in a student’s drawing storyboard (Figure 4). Current GenAI applications are not capable enough of generating accurate images. Thus, using GenAI to create a storyboard may not be recommendable.
Figure 3. An example of a GenAI-generated storyboard.
Figure 3. An example of a storyboard drawn by a student.
Quality gap in ideation and prototyping (Develop phase)
- In CDE4301A, students used GenAI for UI ideation in the early stage of UI prototyping as an inspiration tool (Figure 5). When compared to the UI created by the student (Figure 6), he reflected, “It is hard to generate specific UI details with acceptable quality…It is more suitable for ideation or early prototyping.”
Figure 5. An example of UI created by GenAI (Left),
Figure 6. An example of UI created by a student (Right).
Generic draft required refinement (Deliver phase)
- In CDE5311, students used ChatGPT to generate a user test protocol. GenAI effectively created a draft, but it lacked specificity about the project context; thus, the experienced instructor needed help refining it.
DISCUSSION AND CONCLUSION
In the divergent phases (Discover and Develop), GenAI can be used as a supporting tool to get inspiration (Tholander & Jonsson, 2023). However, in the convergent phases (Define and Deliver), GenAI’s ambiguity did not effectively convey the exact ideas needed due to its lack of specificity and accuracy, which are attributes required in those phases (Tholander & Jonsson, 2023). Furthermore, depending on the students’ ability in each phase, the effectiveness of GenAI can differ (Cai et al., 2023). For example, if a student is a design novice, they would not have enough ability to discern the most effective and relevant outcomes from GenAI. Educators must selectively recommend using GenAI regarding students’ expertise and experience in each phase. Our proposal includes a few suggestions for the Usage of GenAI in the design thinking courses as below:
- In the divergent phases (Discover and Develop), instructors should advise students to use various prompts to generate more diverse outcomes that can support ideation processes.
- In the convergent phases (Define and Deliver), students can use GenAI to create initial drafts, but experienced instructors should assist in refining them to increase specificity reflecting project context.
REFERENCES
Cai, A., Rick, S. R., Heyman, J. L., Zhang, Y., Filipowicz, A., Hong, M., Klenk, M., & Malone, T. (2023). DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration Proceedings of The ACM Collective Intelligence Conference. https://doi.org/10.1145/3582269.3615596
Chung, A., He, Y. C., Lin, L. F., & Liang, Y. W. (2024). Importance of Different AI-Generated Journey Map Modules from Industrial Design Students’ Perspectives. 2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI).
Hsiao, H. L., & Tang, H. H. (2024). A Study on the Application of Generative AI Tools in Assisting the User Experience Design Process. In International Conference on Human- Computer Interaction (pp. 175-189). Springer Nature Switzerland.
Sun, Y., Jang, E., Ma, F., & Wang, T. (2024). Generative AI in the Wild: Prospects, Challenges, and Strategies. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-16).
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. https://doi.org/10.1145/3563657.3596014
Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q., & Velloso, E. (2024). The Effects of Generative AI on Design Fixation and Divergent Thinking. Proceedings of the CHI Conference on Human Factors in Computing Systems.