This study explores integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into design ideation of industrial design. Conducted at TU Delft, the research involved brainwriting and video-based design (VBD) methodologies. The primary aim was to legitimize and valiate context-injected LLMs and VLMs in supporting designers' search for inspiration through development of experiment framework. The study measured workload, user experience, acceptance of technology, divergent thinking capabilites and attitudes towards AI. It also preliminary analysed the results, focusing on qualitative insights.

Data was collected through surveys and interviews, but although eye-tracking data was also gathered, it was excluded from the analysis. The study found that while AI tools support ideation by generating diverse ideas and handling repetitive tasks, they need improvement for contextually relevant and accurate information. Designers expressed cautious optimism about AI's potential, emphasizing the need for human oversight to retain creativity and ensure context-aware assistance. 

The research highlighted optimistic leaning opinions on AI integration, noting that current AI capabilities are not yet sufficient for design ideation demands. It emphasized the necessity for AI to act as a collaborative partner, preserving the designer's critical role in the creative process. 

In today’s world, products are increasingly intertwined with our daily lives, becoming more intricate. To design these interactions effectively, designers need to comprehend both the people involved in the activities and the technical systems. As user-centered design spreads, the focus shifts from viewing design as problemsolving to recognizing it as the social creation of new possibilities. This shift highlights the importance of users and their everyday lives, as well as their interactions with the product in designing.
Videos serve as a powerful tool for learning about users, their behaviors, and interactions. Video Based Design (VBD) involves analyzing videos to interpret, infer, question actions, and understand the user’s thought process. However, this approach can extend the time and analytical rigor needed due to the extensive analysis required to draw valid conclusions from the video content. To streamline this process and enhance researchers’ workflow, we prototyped a tool that can produce mind maps from videos. These mind maps serve as a visual representation of the video’s key concepts in an organized manner. This tool utilizes Large Language Models (LLMs) to process multimodal video inputs and output the generated mind maps.
To assess the impact of mind maps on the video-based design process, an experimental study was conducted involving 28 designers. Participants were asked to watch two videos from different contexts and then engage in activities using mind maps generated by both the LLM and a human designer. Through quantitative and qualitative analysis of the study data, we gained valuable insights, identified strengths and weaknesses, and proposed design enhancements for the next version of the tool.

This thesis bridges the gap between generative artificial intelligence and innovation in footwear design, an industry that is quite conservative in its design methods despite the constant demand for innovation. Initial applied research showed that while 3D technologies have the potential to enhance efficiency in development, they are still time-consuming, as the 3D generative AI was not advanced enough yet and therefore left out of the scope. As a result, the research focused on applying AI on image generation, particularly using LoRA fine tuning to effectively capture brand identity and consistently produce high-quality results with reference images and ControlNet. To validate these findings, the tools and workflows were applied in a case demonstration to a design brief. Additionally, a user test was done with a designer of Filling Pieces, comparing the outcomes of the AI framework versus traditional processes in a survey. The findings showed that the AI framework significantly accelerates the generation of design concepts and enhances creativity by producing more novel designs. However, the study identified limitations in clarity, completeness, and production feasibility, emphasizing the need for AI-generated designs to be complemented with technical drawings for communication with the factory. The relationship between AI and human designers should thus be complementary to achieve the best results.