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The Effects of Generative AI on Design Fixation and Divergent Thinking

Samangi Wadinambiarachchi, Ryan M. Kelly, Saumya Pareek, Qiushi Zhou, Eduardo Velloso

TL;DR

It is found that support from an AI image generator during ideation leads to higher fixation on an initial example and the effectiveness of co-ideation with AI rests on participants’ chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI’s suggestions.

Abstract

Generative AI systems have been heralded as tools for augmenting human creativity and inspiring divergent thinking, though with little empirical evidence for these claims. This paper explores the effects of exposure to AI-generated images on measures of design fixation and divergent thinking in a visual ideation task. Through a between-participants experiment (N=60), we found that support from an AI image generator during ideation leads to higher fixation on an initial example. Participants who used AI produced fewer ideas, with less variety and lower originality compared to a baseline. Our qualitative analysis suggests that the effectiveness of co-ideation with AI rests on participants' chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI's suggestions. We discuss opportunities for designing generative AI systems for ideation support and incorporating these AI tools into ideation workflows.

The Effects of Generative AI on Design Fixation and Divergent Thinking

TL;DR

It is found that support from an AI image generator during ideation leads to higher fixation on an initial example and the effectiveness of co-ideation with AI rests on participants’ chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI’s suggestions.

Abstract

Generative AI systems have been heralded as tools for augmenting human creativity and inspiring divergent thinking, though with little empirical evidence for these claims. This paper explores the effects of exposure to AI-generated images on measures of design fixation and divergent thinking in a visual ideation task. Through a between-participants experiment (N=60), we found that support from an AI image generator during ideation leads to higher fixation on an initial example. Participants who used AI produced fewer ideas, with less variety and lower originality compared to a baseline. Our qualitative analysis suggests that the effectiveness of co-ideation with AI rests on participants' chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI's suggestions. We discuss opportunities for designing generative AI systems for ideation support and incorporating these AI tools into ideation workflows.
Paper Structure (27 sections, 4 equations, 15 figures, 6 tables)

This paper contains 27 sections, 4 equations, 15 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Design Fixation
  4. The Emergent Role of AI in Creativity Support
  5. Method
  6. Study Design and Materials
  7. Participants
  8. Procedure
  9. Data Preparation
  10. Data Analysis
  11. Results
  12. Statistical Analysis
  13. Design Fixation
  14. Fluency
  15. Variety
  16. ...and 12 more sections

Figures (15)

  • Figure 1: The example with the 14 salient features we monitored. Note: The example was given to the participants without the callouts.
  • Figure 2: Examples of sketches created by participants in each experimental condition. (A) No support condition, (B) Image search condition, (C) GenAI condition
  • Figure 3: The overall experiment flow 1: Initial briefing and participant consent, 2: Pre-study questionnaire, 3-7: Main experimental task, 8: Post-study questionnaire, 9: Semi-structured interview and debriefing
  • Figure 4: An example of visual sequence board (A): Participant information and meta data, (B): AI image generation sequence, (B1): Image generation number, (C): AI-generated images in the order 1-2-3-4, (D): Prompt used for each generation, (E): Participant sketch sequence.
  • Figure 5: Theorised causal directed acyclic graph.
  • ...and 10 more figures