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Abstract Creativity is a key 21st-century skill and a consistent predictor of academic learning outcomes. Despite decades of research on creativity and learning, little is known about the cognitive mechanisms underlying their relationship. In two studies, we examined whether creativity supports associative learning through associative thinking—the ability to generate novel word associations—an ability central to creativity which has not been previously tied to associative learning. In Study 1, we found that students who generated more novel word associations learned more words on a foreign language learning test 24 h later. In Study 2, we replicated and extended the effect to naturalistic creativity tasks (i.e., writing short stories and sketching line drawings), finding associative thinking mediated the relationship between creativity and associative learning. Importantly, both studies controlled for general intelligence. Our findings suggest that creativity’s contribution to learning operates partly through a shared cognitive capacity for making new connections. 

ABSTRACT Automated scoring is a current hot topic in creativity research. However, most research has focused on the English language and popular verbal creative thinking tasks, such as the alternate uses task. Therefore, in this study, we present a large language model approach for automated scoring of a scientific creative thinking task that assesses divergent ideation in experimental tasks in the German language. Participants are required to generate alternative explanations for an empirical observation. This work analyzed a total of 13,423 unique responses. To predict human ratings of originality, we used XLM‐RoBERTa (Cross‐lingual Language Model‐RoBERTa), a large, multilingual model. The prediction model was trained on 9,400 responses. Results showed a strong correlation between model predictions and human ratings in a held‐out test set (n = 2,682;r = 0.80; CI‐95% [0.79, 0.81]). These promising findings underscore the potential of large language models for automated scoring of scientific creative thinking in the German language. We encourage researchers to further investigate automated scoring of other domain‐specific creative thinking tasks. 

Human ratings are ubiquitous in creativity research. Yet the process of rating responses to creativity tasks—typically several hundred or thousands of responses, per rater—is often time consuming and expensive. Planned missing data designs, where raters only rate a subset of the total number of responses, have been recently proposed as one possible solution to decrease overall rating time and monetary costs. However, researchers also need ratings that adhere to psychometric standards, such as a certain degree of reliability, and psychometric work with planned missing designs is currently lacking in the literature. In this work, we introduce how judge response theory and simulations can be used to fine-tune planning of missing data designs. We provide open code for the community and illustrate our proposed approach by a cost-effectiveness calculation based on a realistic example. We clearly show that fine tuning helps to save time (to perform the ratings) and monetary costs, while simultaneously targeting expected levels of reliability.