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Mark A. Runco; Burak Turkman; Selcuk Acar; Ahmed M. Abdulla Alabbasi – Journal of Creative Behavior, 2025
Research suggests that generative AI (GAI) responds to divergent thinking (DT) prompts with multiple ideas, some of which seem to be original. The present investigation administered 55 DT tasks to three GAI services (Bard, GPT 3.5, and GPT 4.0). Instead of examining individual responses, an Idea Density algorithm was used to assess the output.…
Descriptors: Artificial Intelligence, Creative Thinking, Models, Differences
Selcuk Acar; Peter Organisciak; Denis Dumas – Journal of Creative Behavior, 2025
In this three-study investigation, we applied various approaches to score drawings created in response to both Form A and Form B of the Torrance Tests of Creative Thinking-Figural (broadly TTCT-F) as well as the Multi-Trial Creative Ideation task (MTCI). We focused on TTCT-F in Study 1, and utilizing a random forest classifier, we achieved 79% and…
Descriptors: Scoring, Computer Assisted Testing, Models, Correlation
Peter Organisciak; Selcuk Acar; Denis Dumas; Kelly Berthiaume – Grantee Submission, 2023
Automated scoring for divergent thinking (DT) seeks to overcome a key obstacle to creativity measurement: the effort, cost, and reliability of scoring open-ended tests. For a common test of DT, the Alternate Uses Task (AUT), the primary automated approach casts the problem as a semantic distance between a prompt and the resulting idea in a text…
Descriptors: Automation, Computer Assisted Testing, Scoring, Creative Thinking

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