Generative AI systems have been hailed as tools for enhancing human creativity and inspiring divergent thinking, yet empirical evidence for these claims is limited. This study investigates the effects of exposure to AI-generated images on design fixation and divergent thinking during a visual ideation task. A between-participants experiment with 60 participants found that using AI during ideation led to higher design fixation, with participants generating fewer, less varied, and less original ideas compared to a baseline condition. Qualitative analysis suggests that the effectiveness of co-ideation with AI depends on participants' approach to prompt creation and their strategies for generating ideas in response to AI suggestions. The study highlights the need to design generative AI systems to support ideation and integrate these tools into ideation workflows. It also emphasizes the importance of addressing factors that may induce design fixation when using AI for inspiration and proposes strategies to mitigate design fixation. The results show that AI-generated images can lead to design fixation, with participants often copying features from the example design or AI-generated images, resulting in fixation displacement. The study contributes to the AI-powered creativity support literature by illustrating how AI-generated images influence design fixation and divergent thinking measures. It also explores the role of AI in providing inspiration during visual design tasks and the importance of considering factors that may induce design fixation when using AI tools.Generative AI systems have been hailed as tools for enhancing human creativity and inspiring divergent thinking, yet empirical evidence for these claims is limited. This study investigates the effects of exposure to AI-generated images on design fixation and divergent thinking during a visual ideation task. A between-participants experiment with 60 participants found that using AI during ideation led to higher design fixation, with participants generating fewer, less varied, and less original ideas compared to a baseline condition. Qualitative analysis suggests that the effectiveness of co-ideation with AI depends on participants' approach to prompt creation and their strategies for generating ideas in response to AI suggestions. The study highlights the need to design generative AI systems to support ideation and integrate these tools into ideation workflows. It also emphasizes the importance of addressing factors that may induce design fixation when using AI for inspiration and proposes strategies to mitigate design fixation. The results show that AI-generated images can lead to design fixation, with participants often copying features from the example design or AI-generated images, resulting in fixation displacement. The study contributes to the AI-powered creativity support literature by illustrating how AI-generated images influence design fixation and divergent thinking measures. It also explores the role of AI in providing inspiration during visual design tasks and the importance of considering factors that may induce design fixation when using AI tools.