Generative AI & 3D CAD Design - An expert interview study on the feasibility of integrating 3D CAD AI tools within Aalto University’s School of Art, Design, and Architecture

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Volume Title

School of Arts, Design and Architecture | Bachelor's thesis
Ask about the availability of the thesis by sending email to the Aalto University Learning Centre oppimiskeskus@aalto.fi

Authors

Date

2024

Department

Major/Subject

Bachelor's Programme in Design

Mcode

ARTS3101

Degree programme

Muotoilun kandidaattiohjelma

Language

en

Pages

59 + 17

Series

Abstract

Artificial intelligence (AI) is a technology that allows computers and machines to imitate human intelligence and problem-solving abilities, potentially revolutionizing digital design processes. Generative AI, a subsection of this technology, can produce supposedly original content by training on a large dataset of examples and then controlled by users through textual inputs. In the context of three-dimensional (3D) computer-aided design (CAD), generative AI allows designers and engineers to produce novel and intricate geometries. Many believe that adopting and further advancing this technology could speed up and streamline design production processes that are considered laborious or challenging for human users. However, it is important to recognize the prerequisite knowledge required and understand the potential risks of carelessly engaging with generative AI. This study aims to explore the current state of generative AI adoption at Aalto University's School of Art, Design, and Architecture, further referred to as Aalto ARTS, and potentially uncover shortcomings or hindrances holding back the successful use of this technology. The goal is to understand the key requirements for implementing generative AI in design education and whether the current level of 3D CAD education is sufficient for successful adoption of this technology. This purpose differs from other studies in that it focuses solely on the current situation at Aalto ARTS rather than design education as a whole, providing valuable insights for those interested in participating in shaping the future of Aalto design education. The research methodology relies on qualitative methods, primarily focusing on semi-structured interviews with educators employed by Aalto University to gather relevant, in-depth insights. The study focused on a design bachelor's program with two groups of educators interviewed: direct educators—professors and teachers—and indirect educators—workshop masters and industry professionals employed by Aalto University to assist design students. Thematic analysis and In-Vivo coding were used to analyze the data, ensuring a comprehensive interpretation of the interviewees' insights. Despite the study's focus on two groups of educators, the findings offer significant insights into the current adoption of generative AI and the overall state of 3D CAD education at Aalto ARTS. The research identified two future roles for generative AI that will affect 3D CAD and the overall digital design space. Furthermore, the research revealed the current state of 3D CAD education as well as the requirements for successfully integrating generative AI tools into Aalto design education. Most notably, the lack of 3D CAD proficiency among design students currently prevents the use of generative AI tools. While there are some who express interest in generative AI, the overall situation indicates that neither students nor teachers are ready for this technology. These findings provide a nuanced understanding of how the 3D CAD proficiency level and overall digital design mindset can affect the future adoption of generative AI technology. The study's findings reveal not only the potential future roles of generative AI in the design space and their implications, but also the need for an improvement in Aalto design students' proficiency and understanding of 3D CAD. The research suggests that design educators should prioritize enhancing students’ problem-solving skills along with uplifting their overall perception and comprehension of different product production methods. Furthermore, promoting engagement with modern digital design tools and fostering workflow within the digital space itself has the potential to produce more technology- and future-conscious designers. These insights offer a tangible overview of the shortcomings and areas of improvement for future digital design education, contributing to a broader understanding of the requirements for a successful generative AI implementation in the context of 3D CAD.

Description

Supervisor

Person, Oscar

Thesis advisor

Jeong, Rebecca

Keywords

3D CAD, AI design workflow, digital design education, future design job market, generative AI, manufacturing with AI

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202410276896

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[kand] Taiteiden ja suunnittelun korkeakoulu / ARTS