The integration of artificial intelligence (AI) into creative work continues to expand, yet its impact on human creativity itself—beyond simply providing ideas—remains uncertain. We reposition AI's role from idea generator to idea evaluator, using trained models to provide real-time feedback on human-generated ideas. Across two studies—a preregistered online experiment involving individuals with varying levels of expertise (N = 554) and a large-scale naturalistic experiment during a year-long museum exhibit (N = 36,198)—participants generated solutions to real-world problems or created visual sketches. AI feedback significantly improved participant originality in both verbal and visual creative domains. Mediation analyses revealed these gains were partly driven by changes in individuals' self-evaluation of their own originality, implying a key role for metacognition—the ability to monitor, control and regulate one's thinking. These findings suggest that AI's potential extends beyond generation to include idea evaluation, helping humans assess and refine their ideas through real-time feedback.

ABSTRACT The PISA assessment 2022 of creative thinking was a moonshot effort that introduced significant advancements over existing creativity tests, including a broad range of domains (written, visual, social, and scientific), implementation in many languages, and sophisticated scoring methods. PISA 2022 demonstrated the general feasibility of assessing creative thinking ability comprehensively at an international scale. However, the complexity of its assessment approach—such as time‐consuming scoring requiring human raters—implies the risk that it may not be easily applied by the scientific community and practitioners. In this commentary, we outline important next steps building on the PISA assessment to further enhance future assessments of creative thinking. Crucial future directions include 1) determining what tasks and scorings ensure high psychometric quality including content validity, 2) enabling efficient, objective scoring by applying AI methods such as Large Language Models (LLMs), 3) ensuring high language accessibility via multilingual tests, 4) targeting a broader age group, and 5) facilitating standardized, reproducible assessments via an open online testing platform. In sum, these developments would lead to an efficient, validated multilingual test of creative thinking, which enhances the accessibility of effective creative thinking assessments and thereby supports the democratization and reproducibility of creativity research.

Metaphors are frequently used during communication, offering insight into creativity and cognition through figurative language. In bilingual individuals, metaphor production is influenced by language proficiency and cultural context. However, there is no psychometrically validated test measuring metaphor production ability beyond English. This study develops a tool to assess bilingual metaphor production using simple prompts (e.g., “The small dog is...”) that require original metaphors (e.g., “a tiny tornado”). Using automatic item generation with GPT-4, we generated 100 metaphor prompts, evenly split between English and Spanish. A sample of 160 Spanish-English bilinguals on Prolific provided responses. To evaluate metaphor originality, 100 Spanish-English bilingual raters scored responses using a planned missing data design. We applied a graded response model (GRM) to compute factor scores for each item and person, yielding sufficient rater agreement and measurement precision across both languages (Factor Determinacy Index = .77, ranging from .58 to .87). To further develop the assessment tool, additional factor analysis identified the most effective items, forming a short test of 10 items per language. This research provides a foundation for developing reliable and valid assessments of bilingual metaphor production, advancing the understanding of creative language use in multilingual populations and supporting linguistic assessment applications.

Mete Ismayilzada, Antonio Laverghetta Jr., Simone A. Luchini, Reet Patel, Antoine Bosselut, Lonneke Van Der Plas, Roger E. Beaty. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.

The influence of our surroundings on mental processes is a growing area of interest in psychology and architecture research. Understanding how to optimize spaces to support mental well-being and productivity is crucial. This research tested the hypothesis that nature enhances aesthetic appreciation, creativity, executive functioning, and mood by testing participants in a natural forest, a biophilic (nature-inspired) room, and a control laboratory room. The forest was appreciated aesthetically (as indicated by fascination, and hominess) the most, followed by the biophilic room and the control room. The biophilic room scored the highest in coherence compared to the forest and the control room. Divergent thinking was significantly higher in the nature condition compared to the biophilic room and the control room. Convergent thinking , working memory, attention, and delay discounting did not differ significantly between conditions. Participants' negative affect decreased after spending time in the control room or the biophilic room. By examining cognition, mood, and aesthetic appreciation in immersive real-world environments, this work provides a deeper understanding of how environmental settings affect mental processes, enriching knowledge from previous research focused on 2-dimensional images.

In order to rectify the historical marginalization of Black workers in computing fields, many academic and industrial initiatives have been established to increase the diversity and inclusion in the tech industry. In this study, we explored how one particular post-secondary education program at one historically black institution known as the Google in Residence (GIR) program impacted students' interests and their pursuit of computing-related careers. Data on employment outcomes at this institution showed that the GIR participants who obtained internships and full-time employment at Google were predominantly international students, not African American students who make up the majority of the undergraduate student and about half of the GIR participants. To understand this phenomenon better, we interviewed 27 students who spanned all 4 years of study and included African American and international students. The interviews were structured to elicit a wide range of factors influencing career decisions. Our analysis describes the impact of a particular GIR introductory-level programming course and other GIR program impacts beyond the introductory-level course. We found that the GIR introductory course was engaging, inspired students to change their degree in a more computing-oriented direction and supported internship applications. The informational workshops, networking events, mock-interviews, and advising of the computer science club, which were supported by the GIR program, built students' confidence in interviewing and obtaining full-time employment within the tech industry. While the GIR course and program had a positive influence on students of all backgrounds, the reputation of Google was especially motivating for international students. However, the African American students in this study generally had career aspirations and goals that were less tied to specific companies or industries. Based on our findings, potential improvements could include broadening awareness and access of the GIR course to non-computer science majors, offering summer programs to increase students' computational confidence, and increasing the diversity of GIR and computer science instructors.

• Introduces two automated transformer models for assessing cognitive question complexity: high-performing LLaMA 2 and efficient, robust RoBERTa. • These fine-tuned transformer language models greatly outperform surface-based and semantic-distance baselines. • Both models predict open-ended question complexity and align strongly with human ratings. • Introduces PrediQT , a free web app for automated question-complexity scoring. • Bridges creativity, cognition, and AI for educational assessment innovation. Question-asking, an essential yet understudied activity, holds significant implications for learning, creativity, and cognitive development. Research shows that asking complex, open-ended questions is better for learning than closed ones. Previous research has explored open-ended question complexity through Bloom's taxonomy, but the measurement of complexity remains challenging. Recent advancements in natural language processing have enabled automated scoring of psychological tasks aligned to human-ratings. However, automatic assessment of open-ended questions remains understudied. We address this gap by fine-tuning transformer language models to predict human ratings of open-ended question complexity and comparing them to existing baseline measures (i.e., word count and semantic distance). Using previously collected human-rated responses and Bloom ratings from a creative question-asking task, we trained an encoder model (RoBERTa) and a Large Language Model (Llama-2-7B). Our results reveal that RoBERTa correlated strongly with human ratings of complexity (r = .73), exceeding baseline measures and offering an efficient, lightweight solution suitable for broad adoption. Our fine-tuned LLaMA 2 model achieved stronger performance (r = .84), establishing a new benchmark for predictive accuracy. Thus, we demonstrate how language models can be utilized to automatically score the complexity of open-ended questions. Importantly, LLaMA 2 demonstrates higher accuracy, while RoBERTa provides a replicable, accessible, and cost-effective option for everyday educational and psychological applications. Our work paves the way for automatic assessment of open-ended questions, which are critical across a wide range of cognitive domains.

Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size. Therefore, we propose four strategies to increase effect sizes in neuroimaging research, which may help to enable the detection of replicable between-person effects in samples in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging tasks and behavioral constructs of interest; (2) increasing the reliability of both neural and psychological measurement; (3) individualization of measures for each participant; and (4) using multivariate approaches with cross-validation instead of univariate approaches. We discuss challenges associated with these methods and highlight strategies for improvements that will help the field to move toward a more robust and accessible neuroscience of individual differences.

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.

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain's Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N = 2433)-constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N = 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.