Editorial: Creativity and creativity awareness: new directions
Bibliographic record
Abstract
People vary in their awareness of creativity. A professional soccer coach needs to be keenly aware of soccer creativity when viewing a young soccer player demonstrating innovative moves to pass a defender and to score a goal. If creativity is to flourish in a community, people need to be aware of creativity when exposed to it.How individuals develop creativity awareness and intentionally engage in creative processes constitutes a primary focus of this Frontiers in Psychology Research Topic "Creativity and Creativity Awareness: New Directions". Readers will explore this topic with the aid of seven articles edited by Bart and Can.In an empirical article entitled "Direction and contextual influences on creative thinking", Tadik, Runco, and Bahar define discretion as a specific form of creativity awareness with a focus on how individuals intentionally decide whether to express creative ideas depending on social contexts. They utilized a novel method to assess the role of discretion in the creative process, and operationalized it as deliberate engagement in idea expression. By examining discretion across playful versus work-oriented settings, their research suggests that contextual awareness influences how and when individuals engage in creative behavior as well as how they exercise discretion in producing ideas.In an original research article entitled "Exploring creative thinking skills in PISA: An ecological perspective on high performing countries", Gelmez-Burakgazi and Reiss investigated how educational contexts and ecological factors shape student creative thinking across countries. They examined the PISA 2022 assessment of creative thinking, which assessed the ability of 15-year-old students to generate, evaluate, and refine ideas. Focusing on Singapore, Canada and Finland -three high-performing countries -, they analyzed the systemic influences on shaping student creative thinking by using Bronfenbrenner's ecological perspective. Their results highlight the influence of ecological systems on student creative thinking abilities and suggest that cross-national insights from these high-performing countries can guide researchers, policy makers, and educators in nurturing creativity.In an article entitled "A new perspective on scoring children's originality: A standards-based criterion-referenced assessment approach", Bahar and Maker advance the understanding of creativity awareness by proposing a standards-based criterion-referenced assessment approach for scoring originality with a particular focus on teaching for creativity.They illustrate their perspective with examples drawn from evidence-based educational models that are aimed at developing student ability to solve real-world problems creatively.They contended that appropriate assessment methods are crucial for supporting and tracking learning and that their approach holds important implications for both instructional practice and future research.In a conceptual article entitled "Observation: Unlocking, assessing, and nurturing creative problem solving", Maker and Bahar highlight the importance of observing student actions to unlock, assess, and nurture creative problem solving. Drawing on 37 years of research using the DISCOVER (Discovering Intellectual Skills and capability while Observing Varied Ethnic Responses) model, they identified observable behaviors as indicators of creative problem-solving ability. The authors also offer practical methods by describing activities, experiences, and materials that can be used to elicit and develop these skills.In the original research article entitled "Development and validation of the scale of aesthetics and creativity in chess", Scherbakova, Engelhard, and Bahar deepen our awareness of how creative potential can be recognized and measured in specific contexts by showing how creativity and aesthetics manifest differently across levels of expertise. They investigated the psychometric properties of the Scale of Aesthetics and Creativity in Chess (SACC) using data from 132 chess players ranging from expert to non-expert players. Results from the Many-facet Rasch model showed that the scale was unidimensional, explaining 50.59% of the variance. The reliability indices were strong for both items (0.83) and participants (0.93). Their findings also demonstrated that the level of chess player expertise predicted SACC scores. However, Intermediate players scored lower than Beginners, more Advanced, and Expert players.Artificial intelligence is at present a fashionable topic in cognition and this compendium on creativity awareness includes an article on that topic. In an empirical article entitled "The influence of generative artificial intelligence on creative cognition of design students: A chain mediation model of self-efficacy and anxiety", Hwang and Wu examined how generative AI can promote the cognition of students in design through the mediating roles of self-efficacy and anxiety reduction. Using survey data from 121 university students in Southern China, they found that generative AI positively influenced innovative thinking of students, mediated by self-efficacy and anxiety reduction, including a serial mediation effect.Their results suggest that integrating AI into design curricula could promote creative cognition, improve academic achievement, and develop student design capabilities.The final and seventh article for this research topic is entitled "A systems approach to creative flourishing: Conceptual foundations and implications for development". In this review article, Glass introduces the concept of creative flourishing. Defined as the convergence of creative agency, creative self-efficacy, and flow proneness within a supportive environment, creative flourishing reflects the harmonious alignment of creative desire, perceived skill, and contextual experience. Glass examines intervention strategies that promote awareness of creative flourishing and discusses ways to assess its growth.Overall, these studies offer practical implications for policy makers, educators, and researchers. By presenting the dynamic interplay among individual abilities, social context, and technological innovations, this collection posits that promoting creativity in the 21st century requires an integrated and multidimensional approach. In doing so, these contributions can help recognize and nurture the creative potential in diverse contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.026 | 0.028 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".