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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.23327 |
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| _version_ | 1866909557924560896 |
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| author | Zhang, Man Li, Ying Peng, Yang Sun, Yijia Guo, Wenxin Hu, Huiqing Chen, Shi Zhao, Qingbai |
| author_facet | Zhang, Man Li, Ying Peng, Yang Sun, Yijia Guo, Wenxin Hu, Huiqing Chen, Shi Zhao, Qingbai |
| contents | A key objective in artificial intelligence (AI) development is to create systems that match or surpass human creativity. Although current AI models perform well across diverse creative tasks, it remains unclear whether these achievements reflect genuine creative thinking. This study examined whether AI models (GPT-3.5-turbo, GPT-4, and GPT-4o) engage in creative thinking by comparing their performance with humans across various creative tasks and core cognitive processes. Results showed that AI models outperformed humans in divergent thinking, convergent thinking, and insight problem-solving, but underperformed in creative writing. Compared to humans, AI generated lower forward flow values in both free and chain association tasks and showed lower accuracy in the representational change task. In creative evaluation, AI exhibited no significant correlation between the weights of novelty and appropriateness when predicting creative ratings, suggesting the absence of a human-like trade-off strategy. AI also had higher decision error scores in creative selection, suggesting difficulty identifying the most creative ideas. These findings suggest that while AI can mimic human creativity, its strong performance in creative tasks is likely driven by non-creative mechanisms rather than genuine creative thinking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Delivers Creative Output but Struggles with Thinking Processes Zhang, Man Li, Ying Peng, Yang Sun, Yijia Guo, Wenxin Hu, Huiqing Chen, Shi Zhao, Qingbai Human-Computer Interaction A key objective in artificial intelligence (AI) development is to create systems that match or surpass human creativity. Although current AI models perform well across diverse creative tasks, it remains unclear whether these achievements reflect genuine creative thinking. This study examined whether AI models (GPT-3.5-turbo, GPT-4, and GPT-4o) engage in creative thinking by comparing their performance with humans across various creative tasks and core cognitive processes. Results showed that AI models outperformed humans in divergent thinking, convergent thinking, and insight problem-solving, but underperformed in creative writing. Compared to humans, AI generated lower forward flow values in both free and chain association tasks and showed lower accuracy in the representational change task. In creative evaluation, AI exhibited no significant correlation between the weights of novelty and appropriateness when predicting creative ratings, suggesting the absence of a human-like trade-off strategy. AI also had higher decision error scores in creative selection, suggesting difficulty identifying the most creative ideas. These findings suggest that while AI can mimic human creativity, its strong performance in creative tasks is likely driven by non-creative mechanisms rather than genuine creative thinking. |
| title | AI Delivers Creative Output but Struggles with Thinking Processes |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.23327 |