The central problem the review tackles is conceptual. Design Thinking is variously described as a process, a method, a mindset, or a pedagogical framework, and many studies use these labels interchangeably without explaining what they actually mean. The concept traces its modern popularity to Stanford University’s design school, from which it spread to more than 60 institutions worldwide, but scholars have long complained that the term is under-theorised and oversimplified. One influential analysis identified five distinct discourses of Design Thinking, ranging from the designer’s unique way of knowing to its use as a tool for organisational innovation management. To cut through this confusion, the review’s authors operationalised Design Thinking as a pedagogical strategy sharing four core features: human-centred framing, iterative inquiry, collaborative idea generation, and reflection on solutions. Only studies that demonstrated these features in a structured teaching or learning context were included.

The methodology followed the PRISMA guidelines for systematic reviews. The team searched the Scopus database, chosen for its broad multidisciplinary coverage of education and social science journals, using a Boolean string combining Design Thinking terms with learning experience and higher education terms. The initial search produced 588 records, which were screened by title and abstract down to 70 candidate reports. After full-text retrieval and eligibility assessment, 27 empirical studies made the final cut. Studies were excluded if they focused on K-12 education, treated Design Thinking purely as a business innovation tool, or lacked empirical data. The extracted information covered author, year, country, discipline, study design, student perceptions, academic satisfaction, theoretical framework, and key findings, which were then synthesised thematically to answer the review’s four research questions.

The first question concerned how Design Thinking is actually integrated into university teaching. The answer turned out to be remarkably diverse. Project-based learning was the most frequently reported strategy, followed by flipped learning and design studio pedagogy, with holistic and transdisciplinary approaches appearing less often. Project-based approaches dominated because they allow students to grapple with authentic problems, work collaboratively, generate ideas, build prototypes, and exchange feedback in a single structured cycle. Design studio pedagogy, rooted in architecture and urban design education, emphasises iterative critique and tutor-student feedback loops, in which students refine their solutions through repeated rounds of presentation and criticism. Flipped classroom models asked students to engage with materials before class, freeing classroom time for application and collaborative design work.

Beyond these three dominant models, the review documented a striking range of adaptations across disciplines. Design Thinking has been woven into STEAM and mathematics education, digital learning platforms, gamified courses, Kansei Engineering, traditional games, and co-curricular design tasks. Studies spanned engineering, architecture, medical education, art, sustainability, and online learning environments. This breadth demonstrates that Design Thinking is not confined to design disciplines; it functions as what the authors call an adaptable instructional logic that can be reshaped to fit different fields. That flexibility is a genuine strength, allowing the approach to respond to varied disciplinary needs, but it also creates conceptual ambiguity, since some studies appear to label ordinary active learning strategies as Design Thinking without clearly explaining which principles they applied.

On the question of student experience, the findings were largely positive but nuanced. Students consistently perceived Design Thinking-based learning as more engaging than traditional teacher-centred instruction, particularly when it was combined with gamification, project work, or co-curricular activities. Many reported valuing the opportunity to generate ideas, test alternatives, and build confidence in their creative abilities, which the authors link to a stronger sense of ownership over learning. Collaboration emerged as another recurring theme, with students negotiating ideas, giving and receiving peer feedback, and learning through shared problem-solving, a pattern that aligns closely with social learning theory. Empathy also surfaced as a distinctive feature, since students had to understand users, stakeholders, or communities before proposing solutions, though practising empathy proved difficult for those unfamiliar with reflective, user-centred inquiry.

Not every experience was comfortable. The review found that students often struggled with the uncertainty inherent in Design Thinking tasks. Unlike conventional assignments with fixed correct answers, Design Thinking requires learners to tolerate ambiguity, revise their ideas repeatedly, and accept failure as a normal part of the learning process. While this discomfort can drive deep learning, it can also produce anxiety in students who have never encountered open-ended inquiry. Crucially, the review found that Design Thinking does not automatically produce positive outcomes. Students gained the most when instructors provided clear tasks, structured feedback, emotional support, and careful guidance through the iterative stages. Without effective facilitation, the method can become confusing, overly broad, or emotionally demanding for both students and instructors, a finding the authors flag as an underexamined issue of conceptual and emotional labour.

The evidence on measurable learning outcomes was thinner than the enthusiasm surrounding the method. Several studies relied on students’ self-reported perceptions of creativity, autonomy, motivation, and problem-solving gains, results the authors say should be interpreted with caution because perceived improvements do not necessarily reflect objective gains. Fewer studies reported measured improvements in creative thinking, critical thinking, subject mastery, or academic achievement, and those that did suggest Design Thinking activities may contribute to gains in collaboration, motivation, and discipline-specific learning. Behavioural outcomes were more consistently documented, with multiple studies reporting increased participation, ideation, prototyping, reflection, and persistence. Course satisfaction was generally higher when Design Thinking courses were interactive, connected to real-world problems, and supported by clear instructional design, though satisfaction varied across disciplines, genders, prior experience levels, and cultural contexts.

The theoretical landscape underpinning this research proved fragmented in its own way. The reviewed studies drew on constructivist learning theory, experiential learning theory, self-determination theory, expectancy-value theory, creative process theory, and the TPACK model, which addresses the integration of technological, pedagogical, and content knowledge. Methodologically, qualitative approaches dominated, accounting for 52 percent of the studies that described their approach, followed by quantitative methods at 35 percent and mixed methods at 13 percent. This qualitative emphasis is valuable for capturing student experiences and classroom interactions, but it limits the field’s ability to establish causal claims about Design Thinking’s effectiveness. The authors argue that future research needs longitudinal designs, control groups, and comparisons against non-Design Thinking active learning strategies to determine whether reported gains in creativity, confidence, and satisfaction persist beyond a single course.

The review’s ultimate contribution is a reframing rather than a simple endorsement. Design Thinking, the authors conclude, works as a flexible pedagogical logic whose meaning and effects depend heavily on how it is operationalised, facilitated, and assessed. It should not be treated as a universal solution but as a pedagogy requiring contextual adaptation to each discipline, cohort, and institutional setting. The findings carry practical implications for curriculum reform, faculty training, and institutional innovation, suggesting universities that invest in proper facilitation and structured support are most likely to see returns. At the same time, the authors call for a validated instrument to assess Design Thinking-based learning, clearer definitions in future studies, and investigation into how instructors and students manage the empathy, frustration, and ambiguity the method demands. For now, the message to higher education is clear: Design Thinking can genuinely transform learning experiences, but only when implemented with rigour rather than hype.

Subject of Research: Design Thinking as a pedagogical strategy in higher education learning experiences

Article Title: A systematic review of Design Thinking as a pedagogical strategy in higher education learning experiences

Article References: Aboagye, G. A., Owusu, K. F., Aklasu, E., Nti, F. P. M., Adamtey, D. E., Duako, K. F., & Essel, H. B. (2026). A systematic review of Design Thinking as a pedagogical strategy in higher education learning experiences. Discover Education, 5(1), Article 803. https://doi.org/10.1007/s44217-026-02088-3

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02088-3

Keywords: Design Thinking, higher education, pedagogy, systematic review, student engagement, learning outcomes, project-based learning, design studio pedagogy, flipped learning, creativity, student satisfaction, PRISMA

The study, published in the journal Discover Artificial Intelligence, was conducted by Ece Leventoğlu and Burak Ayçiçek of Tokat Gaziosmanpaşa University. It focused on a construct called metacreativity, defined as the awareness and regulation of one’s own creative process. Rather than treating creativity as a spontaneous spark, the researchers framed it as something learners can deliberately monitor and steer, much like the planning, monitoring, and evaluation that define metacognition more broadly. In practice, metacreativity shows up when students understand problems more deeply, reorganize relationships among ideas, generate alternative strategies, and judge the quality of their emerging answers.

To test whether AI could scaffold that kind of thinking, the team ran a convergent parallel mixed-methods study in an undergraduate Philosophy of Education course. Fifty students participated in the quantitative phase: 22 in an experimental group from the Special Education Teaching program and 28 in a control group from the Psychological Counseling and Guidance program. Both groups studied the same philosophical topics with the same instructor over the same five weeks, but only the experimental group worked with generative AI tools. The researchers measured attitudes toward AI, perceived metacreativity, and academic achievement before and after the intervention, and they supplemented the numbers with weekly reflective journals and an open-ended questionnaire.

The AI toolkit was broad. Students worked with ChatGPT, Gamma, Claude, NotebookLM, Suno, and Perplexity, using them to generate visual metaphors, creative texts, songs, and alternative representations of philosophical concepts. Crucially, the pedagogical design positioned AI as a scaffold rather than a substitute for thinking. Students were explicitly instructed not to accept generated content automatically but to examine its relevance, accuracy, and originality before revising or incorporating it into their final products. The instructor introduced weekly objectives and facilitated discussion but never supplied ready-made answers, leaving students responsible for questioning, selecting, revising, and evaluating what the machines produced.

The quantitative results revealed a distinctive pattern. In the experimental group, positive attitudes toward AI rose significantly from an average of 3.18 to 3.69 on a five-point scale, while negative attitudes fell from 3.20 to 3.00, both small-to-medium effects. Four of the six metacreativity subdimensions improved significantly: Comprehending the Main Problem and Sub-Problems, Reconstructing Connections, Finding Solutions, and the Affective Dimension, with effect sizes ranging from medium to medium-to-large. Notably, two subdimensions, Developing Additional Strategies and Evaluation, showed no significant within-group change at all. The control group, meanwhile, remained statistically flat across every attitude and metacreativity measure.

Between-group comparisons sharpened the picture further. After controlling for pretest scores, the experimental group scored significantly higher than the control group on Reconstructing Connections, Evaluation, Finding Solutions, and the Affective Dimension, with partial eta-squared values as large as 0.226 for Reconstructing Connections, a large effect by conventional standards. Interestingly, the Evaluation subdimension showed a significant adjusted between-group difference even though the experimental group had not improved significantly within itself, a result the authors attribute partly to the groups’ different baseline levels. Academic achievement rose significantly in both groups, suggesting ordinary course learning contributed to the gains, but the adjusted posttest scores still favored the experimental group, with a medium effect size of Cohen’s d equal to 0.66.

The qualitative data traced a clear developmental arc across the five weeks. First-week journals were dominated by novelty and excitement, laced with hesitation; one student admitted lingering prejudice because AI presents everything in an overly easy and ready-made way. By the second week, confidence grew, and by the third, students described practical applications, using an AI tool to build a presentation and directing its visuals themselves. By the fourth week, reflections turned pedagogical, with students imagining how song-generating AI might serve musically inclined learners and mind-mapping tools might help visual thinkers in their own future classrooms. One student captured a conceptual shift neatly: I used to think AI harmed creativity, but I later realized that creativity and creation are not the same.

Yet the students did not become uncritical enthusiasts. In the open-ended questionnaire, they flagged reduced mental effort, dependence on AI, misinformation risks, privacy and security concerns, and possible creativity suppression as genuine dangers. This balanced stance matters for interpretation. The authors argue that the educational consequences of AI are not inherently positive or negative; they depend on how AI is integrated, what tasks students perform, and whether the design explicitly encourages reflection, monitoring, and critical evaluation of machine-generated output. When AI replaced rather than supported self-regulated thinking, prior research has warned of weakened metacognitive engagement, but this study suggests such risks are contingent on pedagogy, not baked into the technology.

The selective pattern of gains is perhaps the study’s most instructive finding. The dimensions that improved, problem framing, connection-making, solution generation, and emotional engagement, align closely with what iterative AI use naturally affords: repeated cycles of idea generation, comparison, revision, and reflection. The dimensions that lagged, strategic diversification and evaluative judgment, represent higher-order regulatory processes that likely demand more practice, explicit evaluation criteria, and sustained scaffolding than a five-week intervention can provide. The authors recommend that future AI-supported activities push preservice teachers to compare alternatives, revise AI-generated content, justify their decisions, and evaluate their final products, and they suggest prompt templates, reflective journals, and structured reflection questions as practical ways to make students’ reasoning visible.

The researchers also stress the limits of what can be concluded. The sample was small and drawn from a single public university in Türkiye using convenience sampling, the two groups came from different academic programs, and metacreativity was measured through self-report rather than direct behavioral evidence. There was no delayed posttest to check whether the gains persisted, and because the intervention bundled AI use with creative production, discussion, and active learning, the effects reflect the overall instructional design rather than AI in isolation. Still, within those boundaries, the study offers a concrete template for teacher education: AI works best not as an answer machine but as a thinking partner, embedded in reflective, metacognitively oriented designs that keep students firmly in charge of their own creative judgment.

Subject of Research: AI-supported instructional design and preservice teachers' metacreativity, attitudes, and achievement in teacher education

Article Title: AI-supported instructional design and preservice teachers’ perceived metacreativity: a mixed-methods study

Article References: Leventoğlu, E., & Ayçiçek, B. (2026). AI-supported instructional design and preservice teachers’ perceived metacreativity: a mixed-methods study. Discover Artificial Intelligence, 6(1), Article 1239. https://doi.org/10.1007/s44163-026-02319-4

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02319-4

Keywords: artificial intelligence, metacreativity, teacher education, preservice teachers, instructional design, generative AI, metacognition, academic achievement, mixed methods, higher education, AI attitudes, creativity

The framework builds on a biopsychosocial perspective but pushes it further than the familiar three-pillar model. Rather than treating biology, mind, and society as separate influences whose contributions can simply be added together, Neroni proposes that creativity arises from dynamic, interdependent relationships that shift across individuals, tasks, developmental stages, and historical moments. The unit of explanation, in this view, is not the isolated determinant but the evolving configuration of determinants. This distinction matters because the same ingredient can play radically different roles depending on who is using it, what stage of creative work they are in, and what technological or social environment surrounds them. The perspective draws on earlier systemic models, such as the influential account locating creativity in the relationship among person, domain, and field, and on sociocultural approaches that dissolved the boundary between mind and material world. What is new is the extension of this relational logic across all levels of analysis, from neural networks to algorithms.

The neuroscience section of the argument illustrates the problem vividly. Over the past two decades, creativity research has moved away from hunting for a discrete creative brain area toward network-based models. Attention has focused on interactions between the default mode network, which supports internally directed thought, episodic memory, and spontaneous associative processing, and the executive control network, which contributes to working memory, attentional regulation, and the evaluation of emerging ideas. A third system, the salience network, appears to coordinate shifts between internally and externally oriented processing. Studies of divergent thinking, in which people generate multiple unusual uses for everyday objects, have repeatedly found functional coupling between these networks, supporting the idea that creative thought depends on cooperation between idea generation and cognitive control.

Yet recent critiques caution against replacing one reductionism with another. As the framework notes, studies differ substantially in the tasks they use, how they score creativity, and the neural patterns they report, and individual differences in how people approach creative tasks are often overlooked. Evidence that the default mode and executive networks cooperate during divergent thinking does not establish a general neural system for creativity. A broader concern holds that creativity should not be treated as a single cognitive faculty with a dedicated neural substrate, because creative activities recruit very different combinations of cognitive, emotional, perceptual, motor, and domain-specific processes. Findings from lesion studies reinforce this picture: damage to anatomically distinct brain regions can alter fluency, flexibility, or originality, yet distinct sites may connect to common functional circuits, suggesting distributed systems rather than a creative command center. Genetic research tells a similar story. Associations between creative performance and dopaminergic gene variants exist, but creativity is a complex behavioral phenotype that cannot map directly onto individual genes; genetic influences operate indirectly through temperament, reward sensitivity, and neural plasticity, and their expression depends on developmental and environmental conditions.

Development itself dismantles the notion of a biologically fixed creative capacity. Lifespan research distinguishes creative potential, everyday creative behavior, domain-specific performance, and socially recognized achievement, each following different trajectories. Childhood offers neural plasticity and exploratory behavior that favor experimentation, but children lack accumulated domain knowledge, and whether their explorations yield outcomes judged creative depends on task, educational environment, materials, and evaluative criteria. Later adulthood may bring declines in processing speed alongside continued growth in expertise and semantic knowledge, changing the resource mix rather than simply raising or lowering creativity. Research on sex and gender drives the point home: recent meta-analyses find only minimal average gender differences in creative ability, and differences in variability cannot explain the much larger disparities in eminent creative achievement, which instead reflect educational opportunities, institutional barriers, professional networks, and systems of cultural recognition. The significance of any biological characteristic, the framework argues, depends on the developmental, social, and cultural conditions in which it operates.

Psychological research reveals the same conditionality. Executive functions such as working memory, inhibition, and cognitive flexibility can help maintain goals, suppress dominant responses, and shift perspectives, yet stronger executive control is not always beneficial. Control helps when a task demands evaluation, refinement, or adherence to constraints, while less constrained processing may better facilitate access to unusual associations during idea generation. Cognitive flexibility can restructure an ineffective representation, but constant shifting becomes counterproductive when creative work requires sustained elaboration. Metacognition offers a way to understand this coordination: creative activity requires not only generating ideas but assessing progress, selecting strategies, and deciding whether to continue exploring or change direction. Emotion follows the same pattern. Positive affect has been linked to broad attention and associative flexibility, while some negative states support persistence and analytical processing, but recent experimental work shows that cognitive reappraisal facilitated creativity particularly among individuals lower in openness to experience, demonstrating how an emotion-regulation strategy interacts with personality rather than acting universally.

Social and cultural environments do not merely facilitate or inhibit individual creativity; they help shape the possibilities people encounter, the forms of novelty they recognize, and the outcomes that acquire value. Ideas about originality, usefulness, authorship, and innovation vary across societies and historical periods, and recent reviews caution against treating Western and Eastern cultures as homogeneous categories. Multicultural experience has been associated with greater cognitive flexibility, but exposure alone is insufficient; its effects depend on openness, language competence, domain knowledge, and the quality of intercultural engagement. In education, the long-standing debate over whether schooling stifles creativity through standardization or provides the knowledge needed for creative development remains unresolved, and the framework argues the opposition between structure and freedom is too simple. Structure can limit exploration when rigid, but it also supplies the knowledge, strategies, and feedback needed to develop an idea. Autonomy encourages exploration but may be less useful when learners lack the metacognitive skills to evaluate and refine what they generate.

Organizational research adds further layers. Psychological safety, autonomy, and diversity can support creativity, but none guarantees it; actor-context relationships can be synergistic, compensatory, inhibitory, or characterized by diminishing returns. Conditions that increase individual idea generation do not necessarily improve collective innovation, because teams must coordinate expertise, manage disagreement, and select among alternatives. Digital environments complicate the picture further. Recommendation systems and platform architectures shape what information people encounter and which ideas become visible, sometimes expanding access to diverse material while reinforcing particular patterns of attention and popularity. Generative artificial intelligence makes technology’s role even more direct, participating in ideation, recombination, evaluation, and production rather than merely providing access to information.

The evidence on human-AI collaboration illustrates the framework’s central claim with striking clarity. One influential study found that access to generative AI improved evaluations of individual creative stories, particularly among less creative writers, but simultaneously increased similarity across outputs and reduced collective diversity. Another line of research compared large language models used as ghostwriters, taking the primary role in content generation, versus sounding boards providing feedback on human-generated material. Sounding boards improved outcomes for non-experts, while ghostwriting provided no comparable advantage and was detrimental to expert users, possibly by anchoring subsequent human contributions. A separate experiment found that participants produced less creative poetry when given an AI-generated poem to edit than when writing independently, but this disadvantage disappeared when the system supported iterative co-creation with human and AI alternately contributing, and the co-creative configuration preserved higher creative self-efficacy. A meta-analysis of human-AI collaboration found substantial variation in outcomes depending on the relative strengths of humans and machines and how their contributions were combined. The same technology, in other words, can benefit individual performance while constraining diversity at the collective level.

From this configurational account, the framework derives four mechanisms—constraint, affordance, regulation, and feedback or selection—that describe how relationships across levels develop over time. Constraints can limit exploration but also reduce an unmanageable search space and focus attention. Affordances are relational possibilities for action that emerge between a person and their environment, such as an AI system enabling rapid generation of alternatives whose usefulness depends on the user’s expertise and evaluative skills. Regulation encompasses metacognitive monitoring, cognitive control, motivation, and emotion at the individual level, and feedback, norms, and collaboration at the institutional level. Feedback and selection make creative activity recursive, as products are accepted or rejected and systems amplify some outputs while overlooking others. The framework yields testable propositions: the effect of any creativity-related factor should vary with conditions, factors supporting one phase of creative activity should not necessarily support another, the same intervention can have opposite effects at different levels, and the impact of opportunities or constraints should change across development. Rather than asking which factors universally increase creativity, researchers should now ask for whom, under which conditions, at which stage and level, and over what timescale particular factors support or constrain creative activity—a question that demands ecological, longitudinal, multilevel, and interdisciplinary research designs capable of following creative processes as they unfold in the real world.

Subject of Research: A biopsychosocial and sociotechnical framework explaining creativity as a configurational phenomenon across biological, psychological, cultural, and technological levels

Article Title: Creativity beyond the isolated mind: toward a biopsychosocial and sociotechnical perspective

Article References: Neroni, M. A. (2026). Creativity beyond the isolated mind: toward a biopsychosocial and sociotechnical perspective. Frontiers in Cognition, 5, Article 1893765. https://doi.org/10.3389/fcogn.2026.1893765

Image Credits: AI Generated

DOI: 10.3389/fcogn.2026.1893765

Keywords: creativity, neuroscience, biopsychosocial, generative AI, cognitive flexibility, default mode network, executive control, sociocultural, developmental psychology, organizational creativity, human-AI collaboration, metacognition

The study, conducted by Can Cui of Xuzhou Medical University and Northeast Normal University, along with Yiting Long, Chu Zhang, Yuan Yuan, and Yingjie Jiang, tackled a question that has lingered at the edge of creativity science: do reward effects generalize across gain and loss framing? Most prior work has explored how incentives shape creative cognition when people are told they can earn something. Far less attention has been paid to what happens when the same incentive is presented as a threat, something that could slip away if performance falls short. Given that real-world creative work, from scientific grants to artistic commissions, is often framed in terms of what might be lost rather than gained, the gap is more than academic.

To probe this, the team ran two experiments that manipulated two key properties of rewards: whether the money was real or merely hypothetical, and whether the amount was high or low. Participants solved problems under loss framing, meaning the reward was described in terms of potential loss rather than gain. The researchers then compared these new loss-frame results against previously published gain-frame datasets collected with the same tasks and the same reward manipulations, allowing a rare direct look at how framing itself reshapes motivational effects on cognition.

Two distinct creative tasks anchored the investigation. The first was the compound remote associates task, or CRA, a classic measure in which participants must find a single word that links three seemingly unrelated cue words. Solving these problems often involves a flash of insight, a sudden restructuring of how the elements relate. The second was a Chinese character chunk-decomposition task, or CD, in which participants must break apart unfamiliar or ill-structured character configurations into their component strokes or radicals. Crucially, the CD task distinguishes between ordinary decompositions, which rely on familiar radical-level units, and novel decompositions, which require breaking free of learned representations down to the level of individual strokes. This distinction gave the researchers a window into two flavors of creative processing: retrieving known structures versus constructing genuinely new ones.

The results revealed a striking asymmetry between the two tasks. For the CRA task, no interaction involving framing was detected for solution rates, suggesting that whether rewards were framed as gains or losses did not alter how many problems participants solved. However, a reward type by frame interaction did emerge for response times, hinting that framing subtly shifted how quickly people worked, even when their success rates held steady. Insight-driven word problems, it seems, are relatively robust to how the stakes are described.

The chunk-decomposition task told a very different story. Here, the researchers uncovered a significant three-way interaction among reward type, reward magnitude, and framing, driven almost entirely by performance on novel, stroke-level decompositions, the very problems that demand departing from familiar representations. For these novel challenges, the reward pattern differed pronouncedly between the loss-frame sample and the previously collected gain-frame data. In contrast, for ordinary, radical-level decompositions that could be solved with well-practiced perceptual chunks, the framing-related estimate was close to zero. In other words, loss framing selectively rewrote the reward rules only for the most representationally demanding form of creative thinking.

Within the loss-frame sample itself, the pattern of results was counterintuitive. Real rewards of high magnitude produced better performance than either hypothetical high rewards or real low rewards, consistent with the intuition that tangible, substantial incentives motivate effort. Yet the highest overall performance came from the combination of hypothetical rewards and low magnitude, a pairing that on the surface should have been the least motivating. This paradox suggests that under loss framing, the psychological meaning of a reward is not simply a function of its size or reality, but of how these properties interact with the threat context to shape cognitive strategy.

To make sense of the findings, the authors turned to a metacontrol framework, a theoretical account in which cognition continually balances two competing modes: persistence, which stabilizes current representations and drives focused effort, and flexibility, which loosens constraints and permits restructuring. In this view, reward properties act as biases on the persistence-flexibility balance. Real, high-magnitude rewards may push the system toward persistence, which helps with problems requiring sustained application but can hinder the representational upheaval needed for novel decompositions. Hypothetical, low-magnitude rewards, by contrast, may leave the system freer to explore, an advantage precisely when problems demand breaking apart familiar structures. Loss contexts, the researchers propose, may additionally alter how novel representations are processed in the first place, adding a further layer of modulation beyond simple motivational intensity.

The authors are careful about the limits of their design. Because the gain-frame and loss-frame data came from independent samples rather than participants tested under both framings, the observed differences cannot be attributed uniquely to framing itself. Sample-level differences in ability, motivation, or strategy could contribute to the cross-sample patterns. Still, the specificity of the effect, confined to novel decompositions and absent from ordinary ones and from CRA solution rates, argues against a generic explanation and lends weight to the idea that framing genuinely interacts with the representational demands of the task.

The implications reach beyond the laboratory. Educational settings, workplace innovation programs, and incentive structures of every kind routinely deploy rewards without considering whether they are framed as gains to be won or losses to be avoided. If the findings hold up, the same bonus could enhance routine problem solving while quietly undermining the kind of thinking that produces genuinely original solutions, or vice versa depending on the framing. Motivational framing, the researchers conclude, may be a boundary condition on reward effects in creative problem solving, a factor that determines when incentives help and when they hurt. For anyone designing systems meant to cultivate human creativity, from classrooms to research labs, the message is that how you say it may matter as much as what you offer.

Subject of Research: How loss versus gain framing of rewards modulates creative problem solving performance

Article Title: Loss framing selectively reorganizes reward effects on creative problem solving

Article References: Cui, C., Long, Y., Zhang, C., Yuan, Y., & Jiang, Y. (2026). Loss framing selectively reorganizes reward effects on creative problem solving. BMC Psychology. https://doi.org/10.1186/s40359-026-05801-x

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05801-x

Keywords: creative problem solving, reward, loss framing, gain frame, chunk decomposition, compound remote associates, metacontrol, creativity, cognitive psychology, motivation, insight, BMC Psychology

The research team framed their investigation around a long-standing question in educational psychology: what does creativity actually look like in an era when a machine can generate essays, solutions, and ideas on demand? Rather than treating creativity as a single, vague quality, the study anchored its analysis in Bloom’s taxonomy, the widely used framework that organizes cognitive skills into levels ranging from remembering and understanding to applying, analyzing, evaluating, and creating. By mapping creative performance onto this hierarchy, the researchers could distinguish between lower-order engagement with AI tools and the higher-order skills that educators most want to cultivate. The study’s focus area, illustrated in figures accompanying the paper, centers on how this taxonomy interacts with peer interaction and AI-supported personalization to elevate thinking skills.

Personalized learning sits at the heart of the study’s rationale. Traditional classrooms often force students to move through material at the same pace, which can leave some learners behind while others grow bored. Large language models, by contrast, can adapt explanations, examples, and difficulty to an individual student’s needs in real time, offering hints rather than answers, rephrasing concepts until they click, and generating practice problems matched to a learner’s current level. The researchers hypothesized that this adaptive scaffolding could free students to spend more cognitive effort on the upper tiers of Bloom’s taxonomy, where analysis and creation happen, instead of getting stuck on foundational recall and comprehension.

The observational design of the study is important for interpreting its conclusions. The researchers followed 533 Indonesian students and assessed how engagement with large language model-supported personalized learning related to their creative abilities. Because the study was observational rather than a randomized controlled trial, it captures associations rather than definitive proof of cause and effect, a limitation the authors acknowledge. Nevertheless, the scale of the sample and the structured framework used to evaluate creativity give the findings weight in a field where much of the existing evidence consists of small-scale experiments or anecdotal classroom reports. The pattern that emerged pointed toward a meaningful enhancement of creativity, particularly in the analyzing and creating dimensions of higher-order thinking.

Why would analyzing and creating benefit most? The researchers’ framework suggests an answer rooted in how AI tools change the division of cognitive labor. When a language model handles routine drafting, summarizing, and information retrieval, students are pushed to evaluate the output critically, question its accuracy, and refine it toward their own goals. Those activities correspond directly to the analyzing and evaluating levels of Bloom’s taxonomy. Similarly, creating, the taxonomy’s highest tier, may be stimulated when students use AI-generated material as raw material for original work rather than as a finished product. In this view, the model acts less like an answer machine and more like a collaborator whose contributions must be interrogated, recombined, and improved.

Peer interaction emerges as the second pillar of the study’s framework. The paper’s title, which references both Bloom’s taxonomy and peer interaction, signals that the researchers see creative growth as a social process as well as a technological one. When students discuss AI-generated content with classmates, compare their interpretations, and critique one another’s reasoning, they engage in exactly the kind of evaluative dialogue that educational theorists have long associated with deeper learning. The combination of adaptive AI support and collaborative peer exchange may therefore be more powerful than either element alone, with technology personalizing the input and peers sharpening the thinking that follows.

The context of the study is also significant. Indonesia’s education system has been actively pursuing digital transformation, and the research team’s funding reflects institutional investment in this direction. The publication charges were supported by Universitas Padjadjaran through the Indonesian Endowment Fund for Education on behalf of the Indonesian Ministry of Higher Education, Science and Technology, managed under the EQUITY Program. Research from middle-income countries is underrepresented in the global literature on educational AI, which has been dominated by studies from North America, Europe, and East Asia. Evidence from Indonesian classrooms helps address that gap and suggests that the potential creative benefits of large language models are not confined to well-resourced Western institutions.

The authors, affiliated with institutions in Indonesia and Taiwan, published their work open access in PLOS One under a CC-BY 4.0 license, and they declared no competing interests. The journal’s peer review process means the study’s methods and claims have undergone external scrutiny, though as with any observational research, readers should weigh the findings alongside the design’s inherent limits. Observational studies cannot fully rule out the possibility that students who already possess stronger creative dispositions are simply more likely to embrace AI-supported personalized learning in the first place. Randomized interventions will be needed to establish causality with confidence, and the authors’ framework provides a testable structure for such future trials.

For educators, the practical implications are cautiously encouraging. The study suggests that banning or restricting large language models outright may forgo genuine learning benefits, while unstructured use may not automatically produce gains either. The more promising path indicated by the research is deliberate integration: designing assignments that require students to analyze and improve AI output, encouraging peer discussion around that work, and using the adaptive capabilities of these models to tailor support to individual learners. In such environments, the technology becomes a scaffold for higher-order thinking rather than a substitute for it, and the creative capacities at the top of Bloom’s taxonomy, analyzing, evaluating, and creating, become the explicit targets of instruction.

As generative AI continues to spread through education systems worldwide, studies like this one help move the conversation beyond polarized narratives of either technological salvation or cognitive decline. The evidence from 533 Indonesian students indicates that the outcome may depend less on the technology itself than on the pedagogical structures wrapped around it. When personalization, critical engagement, and peer collaboration are combined, large language models might do more than help students learn faster; they might help them think more creatively. Confirming that promise will require experimental follow-up across diverse educational settings, but this study offers a concrete, theoretically grounded starting point for the next phase of research into how artificial intelligence and human creativity can develop together.

Subject of Research: The effect of large language model-supported personalized learning on students' creativity and higher-order thinking skills

Article Title: When LLMs enable personalized learning, they might enhance students' creativity, particularly by boosting analyzing and creating abilities, per observational study of 533 Indonesian students

Article References: When LLMs enable personalized learning, they might enhance students' creativity, particularly by boosting analyzing and creating abilities, per observational study of 533 Indonesian students. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: large language models, personalized learning, creativity, Bloom's taxonomy, higher-order thinking, peer interaction, education technology, observational study, Indonesia, PLOS One, generative AI, student learning

The study, led by researchers examining the platform’s natural experiment, takes advantage of a rare circumstance in social science research. Rather than recruiting volunteers into an artificial setting, the team was able to observe what happened when an AI drafting tool became available to millions of real users pursuing real causes, from local community disputes to global campaigns. This ecological validity matters enormously, because laboratory experiments on AI writing support have often measured productivity and perceived quality on short, contrived tasks. Whether those gains survive contact with messy, high-stakes, emotionally charged advocacy writing has been an open question.

The findings stand in instructive tension with earlier work that helped fuel enthusiasm for generative AI in knowledge work. A widely cited 2023 experiment by Shakked Noy and Whitney Zhang published in Science found that access to ChatGPT increased worker productivity and, on average, improved the quality of professional writing tasks, with the largest gains accruing to less experienced workers. That result, alongside many others, shaped expectations that AI assistance would be a rising tide lifting all boats. The petition platform study complicates that narrative by showing that improvements in the text itself do not automatically cascade into improvements in what the text is meant to accomplish.

What exactly changed when writers gained access to the AI tool? According to the study, the petitions themselves shifted measurably. Users’ texts became more polished and more alike, converging toward a common stylistic template. This homogenization effect echoes findings from a 2024 study by Anil Doshi and Oliver Hauser, published in Science Advances, which showed that generative AI enhanced the individual creativity of short-story writers, producing stories judged as better written, while simultaneously reducing the collective diversity of the content. When many people draw on the same statistical model for inspiration, their outputs drift toward the model’s central tendencies, and the distinctive voices that once differentiated one petition from another begin to blur.

The convergence of petition texts is not merely an aesthetic concern. In advocacy, distinctiveness can be strategically valuable. A petition that reads like every other petition may struggle to capture attention, convey urgency, or signal the authentic grassroots energy that decision-makers and journalists respond to. The study’s authors suggest that the AI tool effectively acted as a stylistic filter, smoothing the idiosyncrasies of individual writers. Whether that smoothing helped or hurt any given campaign is difficult to say, but the aggregate picture is clear: the measurable transformation of style was not accompanied by any measurable transformation of success.

This dissociation between outputs and outcomes is the study’s central contribution, and it speaks to a broader pattern documented in the human-AI collaboration literature. A 2024 systematic review and meta-analysis by Michelle Vaccaro, Abdullah Almaatouq and Thomas Malone in Nature Human Behaviour examined studies comparing human-AI combinations against humans alone and AI alone, and found a striking heterogeneity in results. In some tasks, combining humans and machines produced performance gains; in others, the combination actually underperformed either humans or AI working independently. The lesson emerging from this literature is that the value of AI integration is deeply task-dependent, and that assuming complementarity is a mistake.

Petition writing, it turns out, may be a particularly unfavorable case for AI assistance. The success of a petition depends on many factors beyond prose quality: the salience of the issue, the size and engagement of the petitioner’s network, timing, media coverage, the target of the demand, and the political or corporate context in which the campaign unfolds. If a petition’s fate is determined largely by these external factors, then improving the wording offers limited leverage. The AI tool may have made petitions read more professionally, but professional reading did not move the needle on signatures or outcomes, because the bottleneck was never the writing.

There is also a psychological dimension worth considering. Research by Florian Draxler and colleagues, published in ACM Transactions on Computer-Human Interaction in 2024, documented what the authors called the AI ghostwriter effect: when users compose text with AI assistance, they are less likely to feel ownership or authorship of the words they submit. Applied to advocacy, this raises questions about authenticity and commitment. A petitioner who feels that their campaign text is partly machine-generated may be less invested in promoting it, or audiences may respond differently to text perceived as formulaic. The new study did not set out to test these mechanisms directly, but its findings are consistent with a picture in which the human element, the passion and personal stake behind a petition, remains the decisive ingredient that no drafting tool can supply.

The broader implications extend well beyond Change.org. Online advocacy communities, civic technology platforms, crowdfunding sites, and grassroots organizations are all experimenting with generative AI to lower barriers to participation. The promise is seductive: if a well-written petition is the price of entry to public discourse, then AI could democratize access for people who lack confidence or training in writing. The new findings temper that promise. Lowering the barrier to producing text is not the same as lowering the barrier to achieving change. If style converges and outcomes stagnate, platforms may be investing in polish where they should be investing in connection, mobilization, and community.

For the researchers, the study is a caution against evaluating AI tools by the metrics that are easiest to measure. Text similarity, readability, and perceived quality are quantifiable, and generative models reliably improve them. But the ultimate purpose of a petition is not to be well written; it is to persuade, mobilize, and precipitate action. The gap between those two standards is where the technology’s limitations become visible. As generative AI spreads through yet another corner of public life, this natural experiment on one of the internet’s largest advocacy platforms offers a clear-eyed reminder: changing how we write is easy, changing what our writing achieves is another matter entirely.

Subject of Research: The effect of AI writing assistance on petition style and outcomes on an online petition platform

Article Title: Petition style changes with AI writing support but outcomes do not improve

Article References: Petition style changes with AI writing support but outcomes do not improve. (2026). Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02579-1 (Author names are not included in the publisher’s citation export.)

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02579-1

Keywords: generative AI, Change.org, online petitions, AI writing tools, human-AI collaboration, text homogenization, digital activism, Nature Human Behaviour, advocacy platforms, productivity, creativity, civic technology

The study, conducted by Figun Dinçer of Bursa Uludag University, recruited 150 third-year pre-service teachers from the English Language Teaching department of a large public university in Turkey. All were at B2 level on the Common European Framework of Reference for Languages, with an average age of 21.5 years. The participants were split into two intact class sections of 75 students each, with one section randomly assigned to an experimental condition and the other serving as a control. Crucially, both groups were taught by the same instructor across the five-week intervention, a design choice that ruled out differences in teaching style or personality as explanations for any observed effects.

The experimental group experienced a radically different kind of writing instruction. Rather than drafting paragraphs under the shadow of grammatical accuracy, these students analyzed and produced authentic multimodal texts: infographics drawn from World Health Organization materials on the global water crisis, digital brochures, and social media posts. They dissected how meaning was constructed across three modes—the emotional weight of a photograph of a child on cracked, dry ground, the urgency carried by words like “crisis” and “danger,” and the way spacing and grouping shaped readability. They then built their own visual-digital compositions using tools such as Canva, Piktochart, and Adobe Express, drawing on royalty-free image libraries. The control group, working on the same water-crisis theme, read a plain 500-word text, answered comprehension questions, and wrote a conventional short essay with no visual component at all.

At the heart of the study lies a new theoretical concept the author calls Cognitive Load Diffusion, or CLD. The idea synthesizes John Sweller’s Cognitive Load Theory, Richard Mayer’s Cognitive Theory of Multimedia Learning, and the New London Group’s multiliteracies framework into a single explanatory mechanism. When a writing task demands grammatical precision, discourse organization, and syntactic encoding all at once, working memory becomes saturated, and anxiety rises. CLD proposes that when learners are permitted to carry part of a task’s meaning-making load through images, layout, and digital design—what biologist Jesper Hoffmeyer termed “semiotic scaffolding”—less mental capacity is devoted to monitoring linguistic accuracy. That redistribution, the theory holds, is associated with lower anxiety and more psychological room for creative risk-taking. The author is careful to frame CLD as an integrative explanatory construct rather than a fully validated freestanding theory, noting that the study offers initial empirical support within a single instructional context.

The quantitative results were striking. Analyzed with a one-way Multivariate Analysis of Covariance that controlled for pre-test writing anxiety, the data revealed a significant multivariate effect of instructional method: Wilks’ Lambda of 0.80, F(4, 144) = 7.85, p < .001, meaning instructional condition accounted for roughly 20 percent of the variance in the combined outcomes. Follow-up analyses showed the experimental group scored significantly lower on the Second Language Writing Anxiety Inventory, with a post-test mean of 2.38 versus 3.85 in the control group and a partial eta-squared of 0.40—by conventional benchmarks, a large effect. Intrinsic motivation, measured with the Attitude/Motivation Test Battery, rose to 4.12 versus 3.18. Creativity, assessed with the Torrance Tests of Creative Thinking, reached 3.89 versus 2.85. And multiliteracy awareness, measured with a researcher-developed 16-item scale validated by expert panel and factor analysis, climbed to 4.26 versus 3.05, with a partial eta-squared of 0.43. All four effects were large.

Perhaps the most practically significant number is the duration of the intervention: just five weeks, one three-hour session per week, totaling 11.25 clock hours of instruction. An effect size of 0.43 on multiliteracy awareness means that a single short module measurably shifted how prepared future teachers felt to design and evaluate multimodal materials—a skill increasingly demanded of them once they enter their own classrooms. The author argues that curriculum designers could reasonably expect comparable gains by embedding similar visual-digital tasks within existing methods courses, without extending total course time. For teacher educators, the anxiety finding carries particular weight: a partial eta-squared of 0.40 suggests instructional design alone could plausibly separate trainees who avoid writing-intensive assignments from those who embrace them.

A secondary process-perception questionnaire, administered only to the experimental group, helped illuminate why the intervention worked. The 24-item instrument, resolved through exploratory factor analysis into four sub-dimensions, showed the highest positive perception for emotional comfort, with a mean of 4.05, followed by creativity support at 3.89, task clarity at 3.74, and perceived manageability of cognitive effort at 3.44. Participants reported that the design-based tasks guided them effectively despite the complexity of the digital tools, and that expressing themselves through images rather than words alone created a more comfortable psychological environment. The task clarity score is notable because it counters a common objection to multimodal pedagogy—that introducing design software adds confusion rather than clarity.

Qualitative data converged with the statistics. Weekly reflective design journals and end-of-treatment reflections, coded independently by two researchers with 88 percent inter-coder agreement, yielded two dominant themes. The first, reduction of linguistic pressure, captured a shift from preoccupation with grammatical mistakes toward a focus on meaning: one participant described no longer feeling limited to worrying about grammar once designing with visuals, and instead being able to concentrate on what they wanted to say. The second, creative agency through semiotic resources, reflected a stronger sense of self-expression once color and layout became part of meaning-making; another participant reported feeling freer to express themselves when visual elements entered the text. These accounts map directly onto the lower anxiety scores and higher creativity scores in the quantitative analysis, providing convergent evidence for the proposed CLD mechanism.

The study is candid about its limits. Assignment occurred at the section level rather than the individual level, so pre-existing differences between the two classes cannot be entirely excluded. The two conditions differed not only in the presence of visuals but also in the depth of instructional scaffolding, meaning the observed gains may reflect the richer pedagogy as a whole rather than visual elements alone. All primary outcomes relied on self-report instruments, and no rubric-scored measure of actual writing quality was included, so no claim is made about the objective quality of the texts produced. The sample came from a single state university, the intervention lasted only five weeks, and no independent fidelity checks were performed during delivery. Baseline measures were collected only for writing anxiety, leaving equivalence on the other three outcomes statistically unverified. The author calls for future research using individual-level randomization, Solomon Four-Group designs, objective writing assessments, multiple institutions, and longer follow-up periods.

Even with those caveats, the implications reach beyond language classrooms. The findings suggest that multimodal pedagogy is not merely a tech-savvy alternative but a psychologically supportive framework—one that treats anxiety, motivation, and creativity as intertwined dimensions of the same cognitive process rather than isolated variables. As communication itself becomes ever more visual and digitally mediated, the gap between how we teach writing and how the world actually communicates continues to widen. This study offers early evidence that closing that gap may require less time, and yield larger emotional and creative dividends, than traditional teacher education has assumed. For the next generation of English teachers, the message is clear: sometimes the fastest way to unblock a sentence is to draw the picture first.

Subject of Research: Effects of visual-digital writing instruction on anxiety, motivation, creativity, and multiliteracy awareness in pre-service EFL teachers

Article Title: Visual-digital writing instruction and its effects on creativity, motivation, multiliteracy awareness, and writing anxiety in pre-service EFL teachers

Article References: Dinçer, F. (2026). Visual-digital writing instruction and its effects on creativity, motivation, multiliteracy awareness, and writing anxiety in pre-service EFL teachers. Discover Education, 5(1), Article 1170. https://doi.org/10.1007/s44217-026-02288-x

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02288-x

Keywords: multimodal writing, Cognitive Load Diffusion, writing anxiety, pre-service teacher education, EFL, digital pedagogy, multiliteracies, creativity, intrinsic motivation, semiotic scaffolding, instructional design, Discover Education

For decades, researchers probing animal creativity have relied on trained paradigms, in which an animal is cued to produce a novel behavior and rewarded for originality. Dolphins have excelled in such studies, famously inventing new tricks on command, but critics have noted a limitation: those tasks measure creativity only under stimulus control, shaped by human expectations and reinforcement schedules. The new study took a different route. Instead of asking dolphins to perform, the team examined object play, a naturally occurring, self-initiated behavior, and asked whether it displayed two key features associated with creative cognition: fluency, meaning the diversity and frequency of behaviors produced, and elaboration, meaning the sequential organization of those behaviors into increasingly complex chains.

The researchers analyzed video footage of bottlenose dolphins (Tursiops truncatus) housed at the Roatan Institute for Marine Sciences in Honduras and wild Atlantic spotted dolphins (Stenella frontalis) studied near Bimini in the Bahamas. Data collection at both sites was funded and organized through the Dolphin Communication Project, and all observations were non-invasive, relying on footage rather than direct interference with the animals. Trained coders, using the event-logging software BORIS, marked each object play event and broke it down into its component behaviors, the type of object involved, and the social context in which it occurred. That granular coding allowed the team to apply quantitative measures of fluency and elaboration rather than relying on anecdotal impressions of playfulness.

The results revealed striking patterns that differed between the two populations. Among the managed-care bottlenose dolphins, object play varied significantly by sex and age. Males accounted for 66.2 percent of recorded play events, and their interactions were dominated by jawing, a mouthing behavior. Females, by contrast, showed greater behavioral diversity, with high proportions of carrying and pushing behaviors. Age mattered as well: younger dolphins showed higher frequencies of jawing, while older individuals engaged in more complex manipulation behaviors. The objects themselves were used differently across the sexes, with males primarily interacting with a single object category and females more frequently engaging with leaves and seagrass. These differences hint that play may serve different developmental and social functions across demographic groups.

The wild Atlantic spotted dolphins told a different story. In this population, the researchers found no significant differences in object play across sex or age, and interactions were overwhelmingly dominated by seaweed, which featured in at least 81 percent of play events across all groups. The contrast between the two species is intriguing: the managed-care bottlenose dolphins, living in an environment with access to a wider variety of objects, displayed a richer and more differentiated play repertoire, while the wild spotted dolphins converged on a single favored material. Whether that reflects ecological opportunity, species differences, or the different social structures of the two populations remains an open question that the authors frame as a direction for future work.

Perhaps the most technically significant finding came from lag sequential analysis, a statistical method that examines whether one behavior reliably predicts the next in a sequence. The analysis revealed structured, non-random behavioral organization in the object play of both populations. In other words, dolphins were not simply grabbing objects and doing whatever came next; their play followed discernible patterns of transitions from one action to another. The bottlenose dolphins exhibited more diverse behavior transitions than the spotted dolphins, suggesting a broader repertoire of possible play sequences. This kind of sequential structure is precisely what the elaboration criterion requires: evidence that play behaviors are chained together in organized ways rather than emitted as isolated, disconnected actions.

Going a step further, the team applied higher-order analyses that looked beyond immediate transitions to identify behavioral associations spanning longer stretches of the play sequence. These analyses identified associations beyond the immediate next-step level, supporting the interpretation that dolphins elaborate on their play, building extended, organized sequences rather than repeating simple two-step patterns. In the framework of creative cognition, elaboration is considered a marker of generative thinking, the capacity to take an initial idea or action and develop it into something more complex. Finding that signature in spontaneous play, without any training or prompting, strengthens the argument that object play can serve as a proxy for creativity in dolphins.

The study, which formed part of the lead author’s master’s thesis at Nova Southeastern University, contributes to a growing body of work suggesting that innovative and flexible behaviors in non-human animals are indicators of cognitive sophistication. Dolphins have long been candidates for such claims, given their large brains, complex social lives, and documented vocal and behavioral innovations. But demonstrating creativity rigorously requires more than charm; it requires measurable structure. By quantifying fluency and elaboration in an unprompted behavior, the researchers provided exactly that, and they did so across two ecologically distinct populations, which adds weight to the conclusion that structured object play is a real feature of dolphin behavior rather than an artifact of captivity or human interaction.

There are caveats worth keeping in mind. The managed-care sample lived in an environment where humans provide objects and where the dolphins’ daily experience differs profoundly from that of wild animals, and the wild data came from a single study site with a narrow range of available materials. Play itself remains notoriously difficult to define, and inferring internal states such as creativity from external behavior is inherently an interpretive act. The authors are careful to describe object play as a window into spontaneous creativity rather than definitive proof of it. Still, the demonstration that play in both populations is statistically non-random and elaborated moves the conversation forward in a way that purely observational anecdotes cannot.

The broader implications reach beyond dolphins. If spontaneous object play can be rigorously analyzed for fluency and elaboration, the same toolkit could be applied to other species, from primates to corvids to cetaceans yet unstudied, offering a common metric for comparing creative capacities across the animal kingdom. For dolphins specifically, the study suggests that the playful dolphin twirling seaweed at the surface may be exercising a form of behavioral innovation that parallels, in its structure, the creative processes psychologists study in humans. As the authors conclude, object play offers a way to better understand dolphin cognitive capacity in novel situations, and it does so on the dolphins’ own terms, in behavior they choose freely, without a trainer, a cue, or a reward in sight.

Subject of Research: Spontaneous object play as a measure of creativity in bottlenose and Atlantic spotted dolphins

Article Title: Fluency and elaboration in dolphin object play: evidence for creativity in behavioral exchanges?

Article References: Adams, A. H., Themelin, M., Manitzas Hill, H., Yeater, D. B., Walker, R. T., Hirons, A. C., & Dudzinski, K. M. (2026). Fluency and elaboration in dolphin object play: evidence for creativity in behavioral exchanges?. Animal Cognition. https://doi.org/10.1007/s10071-026-02117-6

Image Credits: AI Generated

DOI: 10.1007/s10071-026-02117-6

Keywords: dolphins, object play, creativity, animal cognition, bottlenose dolphins, Atlantic spotted dolphins, fluency, elaboration, behavioral sequences, play behavior, cetacean cognition, lag sequential analysis

The research team, led by Hartono Bancong of Universitas Muhammadiyah Makassar in Indonesia, together with Dian Pramana Putra, Abd Rakhim Nanda, and Muhammad Abd Hadi Bunyamin of Universiti Teknologi Malaysia, applied bibliometric methods to trace the quantitative contours of STEAM scholarship. Bibliometrics, at its core, treats the published literature itself as data. By extracting records from Scopus as of 31 May 2025 and subjecting them to co-citation analysis, keyword co-occurrence mapping, and thematic clustering, the researchers could identify patterns invisible to any single literature review. The tools of choice were VOSviewer, a widely used software package for constructing and visualizing bibliometric networks, and Biblioshiny, the graphical interface of the bibliometrix R package developed for comprehensive science mapping analysis.

One of the clearest signals in the data is temporal. Publications on STEAM education remained relatively sparse in the early years of the study window, but growth accelerated sharply from 2016 onwards. That inflection point aligns with a period in which national curricula and funding bodies across multiple continents began formally embracing integrated approaches to teaching the sciences alongside creative disciplines. The surge, the authors argue, reflects a global commitment to curricular innovation and to the technological tools that make integration practical: robotics kits, augmented reality applications, and digital fabrication technologies that allow students to move fluidly between abstract concepts and tangible artifacts.

The geography of the field is equally revealing. International collaboration networks show robust ties among China, South Korea, and the United States, forming a triad of co-authorship that anchors much of the global conversation. South Korea’s prominence is no accident; the country was among the earliest to mandate STEAM-style integrated instruction in its national curriculum, and a substantial body of Korean research has examined how teachers perceive and implement these reforms. China and the United States contribute complementary strengths, with Chinese institutions driving large-scale technology integration studies and American researchers contributing extensively to the theoretical and pedagogical literature. Beyond this core, the collaboration map extends across Europe, Southeast Asia, and the Middle East, underscoring that STEAM is not the project of any single education system but a genuinely transnational undertaking.

Individual scholars also emerge from the network analysis. Among the most prominent contributors identified are Lavicza Zsolt, whose work has connected mathematics, architecture, and cultural heritage through augmented reality, 3D printing, and dynamic geometry tools such as GeoGebra, often in the informal learning environments of museums; and researchers including Jeon M. and Kim J., whose publications rank among the most influential in the field. Their visibility in co-authorship and citation networks illustrates how STEAM research rewards scholars willing to work across disciplinary boundaries, since the field itself demands exactly that kind of boundary-crossing from its practitioners.

Where does this research get published? The analysis identifies Education Sciences, the open-access journal from MDPI, as a key venue, alongside Lecture Notes in Computer Science and the ACM International Conference Proceedings Series. The prominence of computer science venues is telling. A significant share of STEAM scholarship appears not in traditional education journals but in conference proceedings and lecture note series devoted to educational technology, human-computer interaction, and computing education. This dual identity, spanning pedagogy and technology, is one of the field’s defining characteristics and one of the reasons bibliometric mapping is particularly valuable for understanding it.

The keyword and thematic analyses reveal the conceptual heart of the field. Creativity stands out as a dominant theme, reflecting the central claim of the arts integration movement: that adding the arts to STEM does not dilute technical learning but enriches it, fostering the divergent thinking that innovation requires. Interdisciplinary learning appears as a second pillar, alongside a cluster of technology-centered keywords including robotics and augmented reality. A third cluster centers on teacher professional development, an acknowledgment that even the most elegant curriculum fails without educators equipped to deliver it. Studies of collaborative teacher training programs, self-efficacy scales for primary teachers, and design-based professional learning all feed this strand of the literature.

Perhaps the most striking finding is the temporal evolution of research topics. The authors document a strategic shift over the decade from foundational work, in which researchers defined STEAM, argued for its legitimacy, and designed early curricula, toward sophisticated, technology-driven educational models. Early publications concentrated on establishing what arts-integrated STEM education could look like in practice. Later work embraces immersive virtual reality for learning electronic circuits, augmented reality platforms for active learning, artificial intelligence and the Internet of Things as pervasive supports for future classrooms, and computational thinking cultivated through design-based interdisciplinary activities. The field has also begun to expand its own acronym, with proposals such as STREAM, which adds reading and writing or research to the mix, signaling continued conceptual experimentation.

The study is candid about the field’s weaknesses. Despite the explosion of publications, the authors highlight the necessity for greater empirical assessment of STEAM pedagogies. Much of the literature remains descriptive or programmatic, documenting what a given intervention looks like rather than rigorously testing whether it improves learning outcomes. Systematic reviews comparing STEM and STEAM approaches have found promising effects on student creativity, but the evidence base remains thinner than the enthusiasm surrounding the movement would suggest. The authors also call for greater inclusivity of diverse research perspectives, noting that the field’s intellectual center of gravity remains concentrated in a handful of countries, and for serious consideration of the ethical issues raised by technology-enhanced learning environments, from data privacy in AI-supported classrooms to equitable access to expensive digital tools.

For educators, policymakers, and researchers watching the STEAM movement, the study offers both a map and a warning. The map shows a field that has matured rapidly, built durable international networks, and evolved from curriculum experiments into a technologically sophisticated research enterprise. The warning is that maturity brings scrutiny: as STEAM continues to expand, the next decade of research will need to move beyond advocacy and demonstration toward rigorous, inclusive, and ethically grounded evaluation. The trajectory documented in this bibliometric analysis suggests the field has the momentum to make that turn; whether it has the discipline remains the open question that will shape classrooms for years to come.

Subject of Research: Bibliometric analysis of global STEAM education research trends from 2011 to 2025

Article Title: A decade of STEAM education: trends, themes, and global collaborations through a bibliometric lens

Article References: Bancong, H., Putra, D. P., Nanda, A. R., & Bunyamin, M. A. H. (2026). A decade of STEAM education: trends, themes, and global collaborations through a bibliometric lens. SN Social Sciences, 6(10), Article 445. https://doi.org/10.1007/s43545-026-01738-5

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01738-5

Keywords: STEAM education, bibliometric analysis, interdisciplinary learning, creativity, augmented reality, robotics, teacher professional development, VOSviewer, Scopus, international collaboration, educational technology, research trends

The research team set out to answer a deceptively simple question: how do humans perceive and evaluate creativity when it is attributed to artificial intelligence? As generative AI systems produce paintings, poems, and ideas that are increasingly difficult to distinguish from human output, the psychological question of evaluation becomes urgent. If identical works are judged differently depending on whether people believe a human or an algorithm produced them, then much of what passes for creativity assessment may be contaminated by attitudes toward the creator rather than qualities of the creation.

The investigators addressed this question across three experiments, each probing a different corner of creative production. The first experiment focused on artistic creation, using paintings and poetry as stimuli. Participants evaluated creative outputs, and the researchers manipulated only the source label: some participants were told a work was created by a human, while others were told the identical work came from an AI system. The results were unambiguous. Outputs labeled as human-created were rated as more creative than the same outputs bearing an AI label. In the realm of art, at least, a persistent pro-human bias remains firmly intact.

This finding aligns with a long tradition in creativity research suggesting that artistic creativity is judged not only on formal properties such as novelty and usefulness but also on beliefs about intentionality, emotion, and lived experience. A painting is not merely an arrangement of pigments; it is presumed to encode a mind that chose, suffered, and expressed. When that presumption is removed by an AI attribution, evaluators appear to discount the work, even though the physical stimulus before their eyes has not changed by a single brushstroke or syllable.

The second experiment added a layer of nuance by examining co-created works, in which the attribution of responsibility between human and machine was manipulated. Here the picture became more textured. When poetry was labeled as involving human ideation with AI assistance, perceived originality was enhanced relative to other attributions. In other words, for poetry, a collaborative framing actually boosted judgments of originality, suggesting that participants saw the combination of human conceptual direction and machine execution as a virtue rather than a contaminant. Painting, however, told a different story: works attributed primarily to humans remained dominant in evaluators’ judgments. The medium itself, it seems, shapes how comfortable people are with machine involvement in the creative process.

Why would collaboration elevate poetry but not painting? The authors’ findings point toward a domain-specific pattern in attitudes toward AI creativity. Poetry, as a verbal art, may be perceived as closer to the kind of combinatorial, idea-driven processing at which large language models excel, so human-AI partnership reads as a natural extension of the writer’s toolkit. Painting, by contrast, may be more tightly bound to embodied skill, personal vision, and the aura of the individual artist, making any dilution of human authorship costly in evaluators’ eyes. The study’s summary of these results captures the tension precisely: viewing collaboration positively yet maintaining a persistent bias in valuing AI’s creative contributions.

The third experiment delivered the study’s most striking reversal. Instead of asking participants to judge finished artworks, the researchers used the Alternative Uses Test, a classic measure of divergent thinking in which respondents generate unusual uses for everyday objects. Divergent thinking is a cornerstone construct in creativity research, operationalizing the capacity to produce varied and original responses in open-ended tasks. When the same output was presented under different source labels, participants gave higher creativity ratings to responses bearing an AI label. The pro-human bias that dominated artistic judgment flipped entirely when the task was framed as idea generation rather than artistic creation.

This reversal is more than a curiosity; it carries real implications for how organizations, educators, and researchers measure creative potential. If evaluators unconsciously reward ideas attributed to AI in brainstorming and ideation contexts, then human contributors may be systematically undervalued in precisely the settings, such as innovation workshops, design sprints, and research labs, where divergent thinking is most prized. Conversely, in galleries, literary prizes, and art markets, AI-labeled work may face a penalty that no improvement in algorithmic technique can overcome. The bias is not a single attitude but a pair of domain-specific expectations about where minds, biological or artificial, belong.

Methodologically, the study relied on repeated-measures multivariate analysis of variance, known by the abbreviation RM MANOVA, a statistical framework well suited to comparing ratings across multiple dependent measures when the same evaluators encounter multiple conditions. By holding the creative products constant and varying only the attribution, the design isolates attribution itself as the causal factor, ruling out the alternative explanation that AI outputs simply differ in quality. The research was approved by the Ethics Committee of the Jing Hengyi School of Education at Hangzhou Normal University under approval number 2023019, and all procedures followed the ethical standards of the Declaration of Helsinki, with informed consent obtained electronically before data collection. The work was supported by the National Natural Science Foundation of China, the Natural Science Foundation of Zhejiang Province, and the Organized Research Program of Hangzhou Normal University.

There is also an irony worth savoring: the authors acknowledge that during the preparation of the work they used ChatGPT, DALL-E, and other AI tools to make experimental materials and correct the manuscript. A study revealing that humans discount AI-labeled art while favoring AI-labeled ideas was itself produced with substantial machine assistance, a fact that would likely have boosted its perceived originality had it been framed as a poetry project, and diminished it had it been framed as a painting. As generative models weave themselves ever deeper into creative work, this research suggests that the hardest problem may not be teaching machines to create, but teaching ourselves to judge what they create on its merits. Until then, the label on the canvas, or on the idea, will keep quietly rewriting the verdict before anyone has truly looked.

Subject of Research: Domain-specific human bias in evaluating creativity attributed to AI versus humans

Article Title: The domain-specific bias in creativity evaluation: human evaluators favor human-created art, but prefer AI outputs for divergent thinking

Article References: Hou, Y., Zhang, Y., Wang, B., Zhu, Y., & Wu, X. (2026). The domain-specific bias in creativity evaluation: human evaluators favor human-created art, but prefer AI outputs for divergent thinking. BMC Psychology. https://doi.org/10.1186/s40359-026-05704-x

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05704-x

Keywords: artificial intelligence, creativity, creativity evaluation, divergent thinking, Alternative Uses Test, human-AI collaboration, artistic creation, poetry, painting, attribution bias, BMC Psychology, psychology