Leveraging LLMs for Design Ideation: An AI Tool to Assist Creativity

TL;DR

This paper introduces ALIA, a speech-based AI tool leveraging Large Language Models to enhance group design ideation by providing structured stimuli, resulting in improved idea quality and positive user feedback.

Contribution

The paper presents a novel AI-assisted ideation method using speech-enabled LLMs, integrating synectic and dialectic principles to improve creative outcomes in design sessions.

Findings

Participants rated ideas better with ALIA assistance

Participants responded favorably to speech interactions

Preliminary results indicate improved idea quality

Abstract

The creative potential of computers has intrigued researchers for decades. Since the emergence of Generative AI (Gen AI), computer creativity has found many new dimensions and applications. As Gen AI permeates mainstream discourse and usage, researchers are delving into how it can improve and complement what humans do. Creative potential is a highly relevant notion to design practice and research, especially in the initial stages of ideation and conceptualisation. There is scope to improve creative potential in these stages, especially using machine intelligence. We propose a structured ideation session involving inspirational stimuli and utilise Gen AI in delivering this structure to designers through ALIA: Analogical LLM Ideation Agent, a tool for small-group ideation scenarios. The tool is developed by enabling speech based interactions with a Large Language Model (LLM) for inference generation. Inspiration is drawn from the synectic ideation method and the dialectics philosophy to design the optimal stimuli in group ideation. The tool is tested in design ideation sessions to compare the output of the AI-assisted ideation sessions to that of tradi tional ideation sessions. Preliminary findings showcase that participants have rated their ideas better when assisted by ALIA and respond favourably to speech-based interactions.