任务(项目管理)
灵活性(工程)
计算机科学
自然语言处理
人工智能
心理学
统计
数学
工程类
系统工程
作者
Eran Hadas,Arnon Hershkovitz
标识
DOI:10.1016/j.tsc.2024.101549
摘要
In the Alternative Uses Task (AUT) test, a group of participants is asked to list as many uses as possible for a simple object. The test measures Divergent Thinking (DT), which involves exploring possible solutions in various semantic domains. In this study we employ a Machine Learning approach to automatically generate suitable categories for object uses and classify given responses into them. We show that the results yielded by this automated approach are correlated with results given by humans and can be used to predict expected behavior in the field. Educators and researchers may utilize this approach to address the limitations of subjective scoring, save time, and use the AUT as a tool for cultivating creativity.
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