• 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.

We propose expanding the authors' shared novelty-seeking basis for creativity and curiosity by emphasizing an underlying computational principle: Minimizing prediction errors (mismatch between predictions and incoming data). Curiosity is tied to the anticipation of minimizing prediction errors through future, novel information, whereas creative AHA moments are connected to the actual minimization of prediction errors through current, novel information.

Fostering creativity is vital for tackling 21st-century challenges, and education plays a key role in nurturing this skill. According to the associative theory, creativity involves connecting distant concepts in semantic memory. Here, we explore how semantic memory changes following an educational intervention intended to promote creativity. Specifically, we examine how a scientific education curriculum-Scientific Creativity in Practice (SCIP) program-impacts the semantic memory networks of 10-18-year-old students in a chemistry class (n = 176). Students in an Intervention group who received the SCIP intervention, and a Control group who did not, completed creative thinking tests, as well as verbal fluency tasks to estimate semantic networks in science-specific (chemistry) and domain-general (animal) categories. Results showed that the SCIP intervention enhanced performance on one test of scientific creative thinking but showed no significant difference on another. Using network science methods, we observed increased interconnectedness in both science-specific and domain-general categories, with lower path distances between concepts and reduced modularity. These traits define a 'small-world' network, balancing connections between closely related and remote concepts. Notably, the chemistry semantic network showed substantially more reorganization, consistent with the chemistry contents of the SCIP intervention. The findings suggest that semantic memory reorganization may be a cognitive mechanism underlying successful creativity interventions in science education.

The role of top-down control in divergent creativity remains heavily debated. An outstanding question about the state dynamics of creativity concerns acute shifts between heightened and lowered creative states. Particularly, do transitions between creative states incur a switch cost as observed in other domains of cognition? Prior research showed that asymmetric switch costs are often incurred such that reaction time is asymmetrically slower when participants switch from a task involving more top-down control to a task involving less top-down control. We tested the hypothesis that frequent acute transitions from creativity-cued responding (associated with heightened creative state) to uncued responding (associated with lowered creative state) would incur an asymmetric switch cost such that uncued responding would be disproportionately impacted by state changes. We utilized the thin slices verb generation task in a task-switching paradigm. Consistent with the hypothesis of asymmetric switch costs in shifts between creative states, we observed a substantial switch cost when switching from creativity-cued trials to uncued trials, but no switch cost when switching from uncued trials to creativity-cued trials. These findings provide indirect evidence that heightened creative states may require substantially more top-down control than lowered creative states, supporting the theory that divergent creativity requires increased top-down control.

Abstract Undergraduate and graduate students need professional development skills to form expertise applicable to any job or future career. Mentoring is a way that students can learn how to engage in professional development. Likewise, students can learn professional development skills from mentors who they look to for expanding their knowledge base. To help address the needs of undergraduate and graduate students in engineering, the principal investigator developed and facilitated the Mentoring and Professional Development in Engineering Education (MPD-E2) Program. For this study, we examined the program's general functions and elements using session notes and discussion of our observations. The guiding research question for this study is: what are some elements of a mentoring and professional development program that students value? In this work, we present details about the elements of the program that support student development and insights about potential future opportunities for these types of programs.

Metaphor is crucial in human cognition and creativity, facilitating abstract thinking, analogical reasoning, and idea generation. Typically, human raters manually score the originality of responses to creative thinking tasks - a laborious and error-prone process. Previous research sought to remedy these risks by scoring creativity tasks automatically using semantic distance and large language models (LLMs). Here, we extend research on automatic creativity scoring to metaphor generation - the ability to creatively describe episodes and concepts using nonliteral language. Metaphor is arguably more abstract and naturalistic than prior targets of automated creativity assessment. We collected 4,589 responses from 1,546 participants to various metaphor prompts and corresponding human creativity ratings. We fine-tuned two open-source LLMs (RoBERTa and GPT-2) - effectively teaching them to score metaphors like humans - before testing their ability to accurately assess the creativity of new metaphors. Results showed both models reliably predicted new human creativity ratings (RoBERTa r = .72, GPT-2 r = .70), significantly more strongly than semantic distance (r = .42). Importantly, the fine-tuned models generalized accurately to metaphor prompts they had not been trained on (RoBERTa r = .68, GPT-2 r = .63). We provide open access to the fine-tuned models, allowing researchers to assess metaphor creativity in a reproducible and timely manner.

The visual modality is central to both reception and expression of human creativity. Creativity assessment paradigms, such as structured drawing tasks Barbot (2018), seek to characterize this key modality of creative ideation. However, visual creativity assessment paradigms often rely on cohorts of expert or naive raters to gauge the level of creativity of the outputs. This comes at the cost of substantial human investment in both time and labor. To address these issues, recent work has leveraged the power of machine learning techniques to automatically extract creativity scores in the verbal domain (e.g., SemDis; Beaty & Johnson 53, 757-780, 2021). Yet, a comparably well-vetted solution for the assessment of visual creativity is missing. Here, we introduce AuDrA - an Automated Drawing Assessment platform to extract visual creativity scores from simple drawing productions. Using a collection of line drawings and human creativity ratings, we trained AuDrA and tested its generalizability to untrained drawing sets, raters, and tasks. Across four datasets, nearly 60 raters, and over 13,000 drawings, we found AuDrA scores to be highly correlated with human creativity ratings for new drawings on the same drawing task (r = .65 to .81; mean = .76). Importantly, correlations between AuDrA scores and human raters surpassed those between drawings' elaboration (i.e., ink on the page) and human creativity raters, suggesting that AuDrA is sensitive to features of drawings beyond simple degree of complexity. We discuss future directions, limitations, and link the trained AuDrA model and a tutorial (https://osf.io/kqn9v/) to enable researchers to efficiently assess new drawings.

Crystallized intelligence (Gc)-knowledge acquired through education and experience-supports creativity. Yet whether Gc contributes to creativity beyond providing access to more knowledge, remains unclear. We explore the role of a flexible semantic memory network structure as a potential shared mechanism of Gc and creativity. Across two studies (N = 506 and N = 161) participants completed Gc tests of vocabulary knowledge and were divided into low, medium, and high Gc groups. They also completed two alternate uses task, to assess verbal creativity, and a semantic fluency task, to estimate semantic memory networks. Across both studies, the semantic memory network structure of the high Gc group was more flexible-less structured, more clustered, and more interconnected-than that of the low Gc group. The high Gc group also outperformed the low Gc group on the creativity tasks. Our results suggest that flexible access to semantic memory supports both verbal intelligence and creativity. Educational relevance statement: Crystallized intelligence (Gc)-knowledge acquired through education and experience-supports creativity, yet whether Gc contributes to creativity beyond providing access to more knowledge (semantic memory), remains unclear. In this two-part study, we find that individuals with higher Gc tended to have a more flexible semantic memory structure, which in turn supported greater verbal creativity. This finding suggests that building students' vocabulary knowledge and verbal skills may not just expand their knowledge base, but also increase creativity through enabling more flexible access to that knowledge. If supported by further research, this could mean educational interventions targeting growth in Gc and semantic flexibility may foster students' creative capacities beyond just improving content mastery. Overall, this research highlights the interplay between building domain knowledge and cultivating creative thinking, suggesting educators should aim to develop both abilities in tandem rather than treating them separately.

Women reliably perform worse than men on measures of spatial ability, particularly those involving mental rotation. At the same time, females also report higher levels of spatial anxiety than males. What remains unclear, however, is whether and in what ways gender differences in these cognitive and affective aspects of spatial processing may be interrelated. Here, we tested for robust gender differences across six different datasets in spatial ability and spatial anxiety (N = 1257, 830 females). Further, we tested for bidirectional mediation effects. We identified indirect relations between gender and spatial skills through spatial anxiety, as well as between gender and spatial anxiety through spatial skills. In the gender → spatial anxiety → spatial ability direction, spatial anxiety explained an average of 22.4% of gender differences in spatial ability. In the gender → spatial ability → spatial anxiety direction, spatial ability explained an average of 25.9% of gender differences in spatial anxiety. Broadly, these results support a strong relation between cognitive and affective factors when explaining gender differences in the spatial domain. However, the nature of this relation may be more complex than has been assumed in previous literature. On a practical level, the results of this study caution the development of interventions to address gender differences in spatial processing which focus primarily on either spatial anxiety or spatial ability until such further research can be conducted. Our results also speak to the need for future longitudinal work to determine the precise mechanisms linking cognitive and affective factors in spatial processing.

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.