The application of generative artificial intelligence in Creativity Support Tools (CSTs) presents the challenge of interfacing two black boxes: the user's mind and the machine engine. According to Artificial Cognition, this challenge involves theories, methods, and constructs developed to study human creativity. Consistently, the paper investigated the relationship between semantic networks organisation and idea originality in Large Language Models. Data was collected by administering a set of standardised tests to ChatGPT-4o and 81 psychology students, divided into higher and lower creative individuals. The expected relationship was confirmed in the comparison between ChatGPT-4o and higher creative humans. However, despite having a more rigid network, ChatGPT-4o emerged as more original than lower creative humans. We attributed this difference to human motivational processes and model hyperparameters, advancing a research agenda for the study of artificial creativity. In conclusion, we illustrate the potential of this construct for designing and evaluating CSTs.

翻译：生成式人工智能在创造力支持工具中的应用面临着连接两个黑箱的挑战：用户思维与机器引擎。根据人工认知理论，这一挑战涉及为研究人类创造力而发展的理论、方法与构念。相应地，本文研究了大型语言模型中语义网络组织与想法原创性之间的关系。数据通过向ChatGPT-4o及81名心理学学生（分为高创造力与低创造力个体）施测一系列标准化测试收集。在ChatGPT-4o与高创造力人类的比较中，预期关系得到证实。然而，尽管ChatGPT-4o具有更刚性的网络结构，其原创性仍高于低创造力人类。我们将此差异归因于人类动机过程与模型超参数，并提出了人工创造力研究的前瞻性议程。最后，我们阐述了该构念在设计与评估创造力支持工具中的潜在价值。