Assessing Creativity across Multi-Step Intervention Using Generative AI Models

Abstract

Creativity is an imperative skill for today's learners, one that has important contributions to issues of inclusion and equity in education. Therefore, assessing creativity is of major importance in educational contexts. However, scoring creativity based on traditional tools suffers from subjectivity and is heavily time- and labour-consuming. This is indeed the case for the commonly used Alternative Uses Test (AUT), in which participants are asked to list as many different uses as possible for a daily object. The test measures divergent thinking (DT), which involves exploring multiple possible solutions in various semantic domains. This study leverages recent advancements in generative AI (GenAI) to automate the AUT scoring process, potentially increasing efficiency and objectivity. Using two validated models, we analyze the dynamics of creativity dimensions in a multi-step intervention aimed at improving creativity by using repeated AUT sessions (N=157 9th-grade students). Our research questions focus on the behavioural patterns of DT dimensions over time, their correlation with the number of practice opportunities, and the influence of response order on creativity scores. The results show improvement in fluency and flexibility, as a function of practice opportunities, as well as various correlations between DT dimensions. By automating the scoring process, this study aims to provide deeper insights into the development of creative skills over time and explore the capabilities of GenAI in educational assessments. Eventually, the use of automatic evaluation can incorporate creativity evaluation in various educational processes at scale.