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Engineering Creativity: A Narrative Review of Creativity Science for AI Development

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18 November 2025

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20 November 2025

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Abstract
This review explores the link between engineering and creativity, challenging the perception gap between structured training and creative fields. It reframes human creativity insights from prominent scholars to inform the development of AI systems capable of creative problem-solving. The paper translates abstract and philosophical models into structured, computationally tractable frameworks to bridge human creativity research and machine learning applications. The review focuses on four core frameworks to guide AI design: Wallas’s Four-Stage Process, Rhodes’ Four Ps Model, Simonton’s Creativity-as-Influence Model, and Runco’s prevailing framework. It traces the historical progression of creativity research from early efforts by Guilford and Torrance to later dynamic frameworks by Amabile and Csikszentmihalyi. The document discusses how these models, which evolved from abstract theorizing to structured, multidimensional constructs, provide a foundation for examining and applying creativity within technical domains. It also addresses the growing integration of AI, distinguishing between human creativity and artificial creativity produced by machines. The forward-looking perspective suggests an augmentative role for AI within hybrid human-AI workflows. Ultimately, the review aims to provide a blueprint for developing AI systems that move beyond rote problem-solving to exhibit adaptive, context-sensitive, and generative capabilities, capitalizing on the synergy between creativity science and AI.
Keywords: 
artificial intelligence
;  
machine learning
;  
intelligent systems
;  
cognition
;  
imagination
;  
man‑machine systems
;  
creative problem solving
Subject: 
Engineering  -   Control and Systems Engineering

Engineering Creativity: A Narrative Review of Creativity Science for AI Development

The hallmark of engineering is creativity: producing things and systems that did not exist before, which people eventually cannot live without (Cropley, 2015a, 2015b). Despite this fundamental link, the concept of creativity is often stereotypically associated with the arts (Sternberg, 2019; Stoycheva, 2021), leading many engineers to perceive a cultural mismatch between their structured training and the flexible mindset of creative fields. This perception gap exists even though the ability to innovate and create is an essential part of delivering design value in engineering and similar disciplines (Beghetto, 2021; Simonton, 1988).
While creativity remains a complex terrain of philosophical inquiry, several prominent scholars have laid important groundwork to guide our scientific understanding. We will begin with a brief tour. We will acknowledge figures like Guilford (1987), the "father of modern creativity research," Torrance (1974), who introduced a benchmark measurement method, and Csikszentmihalyi (1996), who introduced the concept of creative flow. Later contributors such as Kaufman (2023) advanced the 4C Model of creativity, and Amabile (1983) who developed the influential componential theory will be touched on.
This review reframes these human creativity insights to inform the development of AI systems capable of creative problem-solving. By translating abstract and philosophical models into structured, computationally tractable frameworks, we aim to bridge human creativity research and machine learning applications. We focus on four core frameworks that can guide AI design:
  • Wallas’s (1926) Four-Stage Process (Preparation, Incubation, Illumination, Verification),
  • Rhodes’ (1961) Four Ps Model (Person, Process, Press, Product),
  • Simonton’s (1988) Creativity-as-Influence Model focused on social acceptance, and
  • Runco’s (2020) prevailing framework.
This structure provides conceptual clarity and a roadmap for integrating science-backed creativity principles into AI systems, supporting algorithmic exploration, generative modeling, and evaluation of artificial creative outputs.

Approach of This Review

This work adopts a narrative review approach, emphasizing conceptual synthesis rather than exhaustive systematic retrieval. Sources were identified through targeted searches of major creativity and engineering psychology literature, focusing on seminal theoretical frameworks and widely cited models relevant to human and artificial creativity. The review integrates historical and contemporary perspectives to construct a conceptual bridge between creativity science and AI system design. No formal inclusion/exclusion criteria were applied, as the intent was to capture theoretical evolution rather than evaluate empirical effect sizes.

Historical Attempts to Capture Creativity

Creativity research has advanced through waves of conceptual and methodological development, reflecting broader shifts in psychology, education, and cultural theory. Early efforts sought to transform creativity from a mystical or purely artistic notion into a measurable and reproducible cognitive process. J. P. Guilford (1987), often regarded as the father of modern creativity research, played a pivotal role in reshaping the study of creative thinking. He introduced the concept of divergent thinking, which he defined as the capacity to generate multiple possible solutions to a single problem. By identifying divergent thinking as a core component of creative ability, Guilford established a new framework that shifted creativity research from a focus on singular solutions to an appreciation of cognitive flexibility and idea fluency. This shift from talent to cognition marked a turning point: creativity became a legitimate object of scientific study rather than a poetic abstraction.
Following Guilford, Torrance (1974) operationalized the study of creativity through his Torrance Tests of Creative Thinking (TTCT), which provided a standardized measure for assessing originality, fluency, and flexibility. His work democratized creativity, suggesting that it was not confined to geniuses or artists but a skill that could be nurtured through educational and environmental support. This perspective paved the way for creativity’s integration into applied fields, including engineering education (Cropley, 2015a, 2015b).
The late twentieth century introduced more dynamic frameworks that connected creativity to motivation, expertise, and context. Amabile’s (1983) componential theory emphasized the interplay between domain-relevant skills, creative thinking processes, and intrinsic motivation. Around the same time, Sternberg (1999) reframed creativity as a form of “investment,” proposing that creative individuals metaphorically “buy low and sell high” in the marketplace of ideas, taking intellectual risks where others see little value.
Csikszentmihalyi’s (1996) work on flow added another dimension, linking creativity to the optimal psychological state in which challenge and skill are balanced. His systems perspective highlighted that creativity emerges not solely from the mind of an individual but through dynamic interactions between person, domain, and field. Later, de Bono (1985) provided practical heuristics for structured creativity, notably his Six Thinking Hats, which offered a tool-based approach to divergent and convergent thinking applicable to business and design contexts.
In the twenty-first century, scholars such as Lubart (2001), Beghetto (2021), and Kaufman (2023) have expanded and refined these foundations. Lubart’s 7Cs Model presented a panoramic taxonomy of creativity, ranging from “creator” to “context,” while Beghetto and Kaufman advanced pedagogical and developmental perspectives, including the 4C Model (mini-c, little-c, Pro-c, and Big-C creativity). These models recognize creativity as both process and practice, a continuum spanning everyday problem-solving to transformative innovation.
The progression of creativity research reflects a gradual shift from abstract theorizing to structured, multidimensional models that bridge psychology, education, and applied disciplines. Table 1 shows key milestones in this progression, highlighting how successive frameworks have broadened the understanding of creativity’s mechanisms and contexts. Collectively, these developments trace the discipline’s movement toward operational clarity; thus, establishing a foundation upon which creativity can be meaningfully examined and applied within engineering and other technical domains.
Taken together, these historical developments form a conceptual ecosystem that frames creativity as a multi-level, interdisciplinary construct. However, for the engineering domain where creative thought must be both structured and outcome-oriented certain frameworks offer clearer operational leverage. The four models selected for this review represent pivotal moments in the advancement of creativity theory. Each allows engineers to convert abstract cognitive principles into practical, procedural insight.

The Mechanics of the Creative Mind: Cognitive and Operational Models

Creativity, while often discussed as an emergent or ineffable quality, can also be understood through structured cognitive models that trace the underlying mechanics of idea generation and refinement. These models provide a framework for examining how humans, engineers included, navigate complexity, uncertainty, and constraint to produce novel and useful outcomes. The cognitive dimension of creativity situates innovation within mental operations: encoding, recombination, evaluation, and elaboration. Rather than viewing creativity as a mysterious spark, these models approach it as a dynamic system of attention, memory, and associative reasoning. In this section, we explore four landmark frameworks that continue to shape how creativity is conceptualized and practiced across domains.

Wallas’s Four-Stage Process (1926)

Wallas (1926) proposed one of the earliest systematic explanations of how creative ideas emerge, a process he described as unfolding in four recursive stages: Preparation, Incubation, Illumination, and Verification. This model can be visualized as an “assembly line” of ideas, where each stage transforms cognitive raw material into increasingly refined forms of insight.
Preparation. This stage involves deliberate engagement with a problem, characterized by immersion in relevant data, technical knowledge, and prior solutions. For engineers, preparation may correspond to the research and problem-definition phases of design, in which foundational constraints and objectives are established.
Incubation. Once initial efforts have plateaued, the mind enters a subconscious phase where active problem-solving recedes. During incubation, unresolved tensions and ambiguities are reorganized beneath conscious awareness. Neuroscientific research has since validated this notion, linking creative incubation to diffuse attention and default-mode network activity (Abraham, 2025).
Illumination. The moment of insight, the “aha!” experience, marks illumination. It often arrives unpredictably, representing the sudden crystallization of previously latent connections. In engineering design, illumination may surface as an elegant simplification of a complex system or an unexpected material configuration.
Verification. Finally, the creative product undergoes scrutiny and refinement. Verification ensures that the insight aligns with practical, ethical, and disciplinary standards. For engineers, this corresponds to the iterative validation process: testing, simulation, and optimization before implementation.
Wallas’s framework remains enduring because it reconciles intuition with structure. It validates both the disciplined preparation characteristic of engineering and the serendipitous emergence of new ideas, a balance essential to innovation in technical fields.

Rhodes’s Four Ps Model (1961)

While Wallas (1926) was among the first to chart the creative process as a series of stages unfolding over time, Rhodes (1961) shifted attention from sequence to structure. In his landmark paper An Analysis of Creativity, Rhodes argued that every definition and study of creativity could be understood through four interrelated dimensions: Person, Process, Press, and Product. The framework remains influential because it changes the way we think about creativity. Rather than treating creativity as a sudden burst of inspiration or an isolated act of the mind, Rhodes’s model presents it as something much larger and more interconnected.
Creativity, in this view, emerges from the dynamic relationship between individuals, the environments that influence them, the processes they engage in, and the products they create. It is a living system rather than a momentary spark. Rhodes’s contribution was not to prescribe a new theory, but to synthesize existing ones into a coherent meta-framework. His model serves as a conceptual scaffold that accommodates diverse disciplinary perspectives, enabling psychologists, educators, and engineers alike to situate their work within a unified schema.
Person. The person dimension focuses on the individual characteristics that contribute to creativity, such as curiosity, tolerance for ambiguity, intrinsic motivation, and cognitive flexibility. In engineering contexts, this perspective encourages consideration of how professional identity, expertise, and mindset influence the likelihood of creative performance. It aligns with later research by Amabile (1983) and Beghetto (2021), which emphasized that motivation and self-efficacy are foundational conditions for creative engagement in structured domains.
Process. The process dimension overlaps with Wallas’s four stages, highlighting the cognitive and procedural mechanisms through which ideas evolve. This includes divergent and convergent thinking, analogical reasoning, problem reframing, and iterative prototyping. For engineers, this aspect translates into methodological creativity, where design thinking, simulation, and optimization cycles are structured to enhance ideation and discovery.
Press. Press refers to environmental and contextual influences on creativity, including social norms, organizational culture, collaboration networks, and available resources. Within engineering ecosystems, press can act as both constraint and catalyst. Tight deadlines or regulatory frameworks may limit exploration, yet these same pressures often stimulate innovative workarounds. This ecological view anticipates later models, such as Csikszentmihalyi (1996), in which creativity emerges from the interaction between individual agency and contextual opportunity.
Product. The product dimension concerns the tangible outcomes of the creative act, including ideas, designs, or artifacts that are both novel and useful within a given field. For engineers, this is the most visible expression of creativity, manifesting as a design prototype, a new algorithm, or an optimized system architecture that delivers measurable performance gains. Rhodes emphasized that evaluation depends on both novelty and appropriateness, anticipating later discussions on the social validation of creative output (Simonton, 1988).
Rhodes’s model shifts focus from the how of creativity, which emphasizes process, to the where and by whom creativity occurs. Its systemic perspective fits well with the interdisciplinary demands of engineering, where creativity seldom exists as an isolated cognitive act. Instead, it emerges through networks of expertise, environmental affordances, and iterative feedback loops. In contemporary research, this framework remains a foundational tool for analyzing creativity and has inspired expanded models.
By integrating individual, procedural, environmental, and outcome-based dimensions, Rhodes transformed creativity from a linear act into a dynamic ecosystem. This concept resonates deeply with modern engineering workflows. These workflows are characterized by collaboration, constraint, and continuous refinement.

Simonton’s Creativity-as-Influence Model (1988)

While Rhodes provided a systemic framework for understanding creativity, Simonton (1988) shifted the focus toward the social evaluation of creative work. In his Creativity, Leadership, and Chance, Simonton argued that creativity is not solely a product of individual talent or process, but fundamentally a measure of influence within a social context. A creative idea or artifact becomes meaningful when it is recognized, accepted, and integrated into the domain in which it emerges. This perspective reframes creativity as an interaction between the innovator and the evaluative community.
Simonton’s model emphasizes several key elements. First, the quantity of output influences the likelihood of producing highly creative work, following the principle that prolific creators increase their chances of achieving breakthrough ideas. Second, domain expertise shapes the boundaries of what is considered novel and appropriate, ensuring that creative contributions meet both technical and cultural standards. Third, chance and timing play a critical role, reflecting the stochastic nature of discovery and the impact of external circumstances on how work is received.
In engineering, this model highlights that innovation is not just about ideation and design, but also about adoption and validation. A novel component, system, or process may be technically sound, yet it will only be deemed creative if it provides measurable value, gains acceptance by stakeholders, and influences subsequent developments. Simonton’s approach complements Wallas’s and Rhodes’s frameworks by linking individual and systemic creativity to social recognition and impact, providing a fuller picture of how engineering innovation operates in real-world contexts.
The creativity-as-influence model also aligns with contemporary concerns in technological and collaborative environments. In these settings, network effects, interdisciplinary teams, and institutional structures determine which ideas gain traction. Simonton reminds us that creativity is not only internal and procedural, but relational and performative, emphasizing the importance of context, communication, and domain relevance in the engineering workflow.
By incorporating social evaluation into the study of creativity, Simonton bridges the gap between cognitive processes and practical outcomes. Engineers, designers, and innovators can use this perspective to assess the potential impact of their work, prioritize efforts with the greatest likelihood of influence, and understand the role of peer and stakeholder feedback in shaping what is ultimately considered creative.

Runco’s Six Ps Framework

Runco’s early work engaged directly with Rhodes’s scholarship (Runco and Pritzker, 1999), exploring and expanding these dimensions. Simonton (1990) introduced Persuasion to account for social validation, an idea Runco (2007) later incorporated alongside Potential, representing latent creative capacity yet to be realized, resulting in a Six Ps model that distinguishes creative performance from unrealized potential. Runco and Kim (2020) formalized these refinements, framing creativity as a dynamic, multi-dimensional construct integrating individual characteristics, processes, products, social influence, and future possibilities. In 2019, Runco contributed a chapter to Beghetto and Corazza’s edited volume on dynamic creativity, explicitly connecting his work with the dynamic perspectives agenda. Extending this trajectory, Runco (2022) developed the Seven Cs of Human Creativity, integrating psychometric, performance, and neuroscientific perspectives to examine both creative potential and actualized performance, offering a more nuanced and explanatory approach.
Recent work by Runco (2023) introduces a crucial distinction for the contemporary landscape: the difference between human creativity and what he terms artificial creativity. While AI systems can generate outputs that mimic novelty and problem-solving, they lack the personal, intentional, and socially persuasive dimensions that define genuine human creativity. Complementing this perspective, Lockhart (2024) emphasizes the human condition and the experiential limits of machine-generated innovation, and Staneva-Britton (2024) highlights concerns about the lack of authenticity in AI-generated content. For engineers and AI developers, this underscores the importance of distinguishing between outputs that are merely novel and those that embody the nuanced qualities of authentic human creativity.
Table 2 presents a structured overview of Mark Runco’s scholarly contributions to the study of creativity, highlighting the intellectual progression of his work over more than two decades. It demonstrates how his research has shifted from establishing foundational definitions and consolidating knowledge, to developing hierarchical frameworks for understanding creativity, refining measurement and assessment tools, elaborating typologies, and exploring applied contexts across education, workplace, and cross-cultural settings. More recently, his work has addressed the emerging domain of artificial intelligence (AI), distinguishing human creativity from AI-generated outputs. By organizing the literature into thematic intellectual groups, the table provides a clear visualization of the conceptual development and continuity in Runco’s work, showing how his insights have both deepened theoretical understanding and informed practical applications.

AI and Creativity: Synthesizing Runco’s and Lockhart’s Contemporary Frameworks

The preceding sections traced the evolution of creativity scholarship across psychological, educational, and applied domains, culminating in a range of frameworks summarized in Table 2 . While these diverse perspectives collectively illuminate creativity’s multidimensional nature, two scholars have recently emerged as central to discussions of human versus artificial creativity. Runco’s empirical and theoretical work provides a parsimonious psychological model that redefines the Standard Definition of Creativity in light of AI’s emergence, whereas Lockhart’s philosophical framework situates creativity within existential, embodied, emotional, and ethical dimensions of human experience. The following section narrows the analytical focus to these complementary but distinct perspectives, using them to clarify how the integration of AI reshapes fundamental conceptions of creativity.
The growing integration of AI has sparked urgent questions about the nature, scope, and limits of machine-generated creativity. While AI systems can produce outputs that appear novel or solve defined problems, they operate fundamentally differently from human creators. Recent scholarship highlights that AI lacks the deeply personal, intentional, and socially situated dimensions that define human creativity (Runco, 2023; Lockhart, 2025; Staneva-Britton, 2024). These limitations are not merely philosophical; they have practical implications where novelty must be balanced with ethical, functional, and social considerations.
While other recent studies (e.g., Abraham, 2025; Aruu, 2024; Doshi & Hauser, 2024; Öztaş & Arda, 2025) have examined AI’s creative capacity through cognitive, computational, or sociotechnical lenses, the present discussion centers on Runco’s and Lockhart’s frameworks as they most explicitly delineate the psychological and philosophical boundaries of human versus artificial creativity. A useful starting point for understanding these distinctions is a comparison of human and AI creativity frameworks.
Table 3 summarizes Lockhart’s broad philosophical criteria for human creativity alongside Runco’s (2023) proposal, which formalizes specific components designed to distinguish human from artificial creativity. This comparison underscores how the human creative process integrates complex emotional, social, and embodied dimensions that AI cannot replicate.
Lockhart’s framework also highlights more granular philosophical and psychological dimensions that make human creativity unique. Table 4 summarizes these criteria, emphasizing qualities such as self-awareness, engagement with social and physical contexts, and ethical reflection. These factors collectively illustrate why AI, despite generating novel outputs, cannot currently fully replicate the lived and socially embedded aspects of human innovation.
Current design and development concerns reflect these distinctions. Authenticity and authorship are increasingly scrutinized, as algorithmic outputs can obscure the origin, intent, or value of ideas (Staneva-Britton, 2024). Interpretability and control remain critical, especially when AI systems propose solutions without clear rationale, potentially introducing hidden biases or unsafe design choices (Lockhart, 2024). Similarly, creative diversity and domain knowledge are at stake: AI can explore vast combinatorial spaces, but it lacks contextual understanding and the ability to weigh trade-offs in complex systems (Runco, 2023). Ethical and social considerations including accountability, intellectual property, and the potential devaluation of human expertise further differentiate human from machine-generated innovation.
Despite these challenges, forward-looking perspectives suggest framing AI as augmentative rather than substitutive. Hybrid workflows allow AI to expand the design space, accelerate ideation, and simulate scenarios, while humans provide the cognitive, ethical, and social judgment necessary for meaningful innovation (Lockhart, 2024). This co-creative paradigm aligns with broader trends in human-AI collaboration, emphasizing the complementary strengths of algorithmic exploration and human intuition, insight, and persuasion.
These debates illuminate a broader tension in creativity research: AI challenges traditional boundaries of cognition, intentionality, and value, prompting engineers and scholars to reconsider what constitutes genuine innovation. The future of creativity in engineering likely resides in a spectrum of human-AI interaction, where machine roles are carefully calibrated to support, not replace, the uniquely human dimensions of design thinking, problem-solving, and socially meaningful invention.

Conclusion

Creativity is a hallmark of human problem-solving, yet its complexity has historically limited its translation into structured frameworks suitable for computational modeling. This review has traced the evolution of creativity science, from foundational definitions and hierarchical models to measurement approaches, typologies, applied contexts, and considerations of artificial creativity. By examining human creativity through these lenses, we gain insights not only into the processes and conditions that foster innovation but also into the ways these processes can inform AI systems capable of generating novel, meaningful solutions.
The works of Runco and Lockhart are particularly significant in providing a crucial distinction between human and artificial creativity. Runco's progression of work, from defining and operationalizing creativity to distinguishing between potential and performance, provides a scaffold for computational translation by highlighting aspects that can be simulated in AI. Conversely, Lockhart's philosophical criteria emphasize empathy, existential struggle, and self-awareness that remain uniquely human and fundamentally beyond the scope of current AI replication.
In sum, a rigorous understanding of human creativity offers a blueprint for developing AI systems that go beyond rote problem-solving to exhibit adaptive, context-sensitive, and generative capabilities. Future research should focus on integrating psychometric, cognitive, and contextual insights into algorithmic frameworks, enabling AI to navigate the fine line between artificial novelty and authentic creativity. Ultimately, the synergy between creativity science and AI holds promise for systems that not only replicate but also extend human inventive capacity.

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Table 1. Historical Progression of Theories and Models of Creativity.
Table 1. Historical Progression of Theories and Models of Creativity.
Period / Scholar Key Contribution Conceptual Shift Relevance to Engineering Creativity
J. P. Guilford
(1950, 1987)		 Introduced divergent thinking as a measurable component of creativity, emphasizing fluency and flexibility in idea generation. Transformed creativity from an artistic or mystical trait into a scientific construct grounded in cognition. Established a cognitive foundation for modeling creative problem-solving in design and innovation processes.
E. P. Torrance
(1974)		 Developed the Torrance Tests of Creative Thinking (TTCT), operationalizing originality, fluency, and flexibility. Democratized creativity as a skill that can be assessed and cultivated through education. Provided measurable frameworks for integrating creativity assessment into engineering education.
T. M. Amabile
(1983)		 Proposed the Componential Theory of Creativity, integrating domain-relevant skills, creative processes, and intrinsic motivation. Positioned creativity within a motivational–cognitive system influenced by environment and expertise. Supported models linking technical expertise and intrinsic motivation in engineering design teams.
R. J. Sternberg (1999) Advanced the Investment Theory of Creativity, creative individuals “buy low and sell high” in the idea marketplace. Framed creativity as a strategic, risk-taking process involving intellectual entrepreneurship. Encouraged innovation-oriented mindsets in engineering management and product development.
M. Csikszentmihalyi (1996) Introduced the systems view of creativity and the concept of flow, the optimal state of engagement balancing challenge and skill. Reframed creativity as an emergent property of person–domain–field interactions. Informed the design of work environments that foster sustained creative engagement among engineers.
E. de Bono
(1985)		 Developed Six Thinking Hats and Lateral Thinking, offering structured heuristics for creative and critical thought. Shifted focus toward deliberate, tool-based facilitation of divergent and convergent thinking. Provided practical techniques for structured ideation and design brainstorming.
T. Lubart
(2001)		 Proposed the 7Cs Model encompassing creator, creating, collaboration, context, creation, consumption, and consequences. Expanded creativity into a multi-dimensional, systemic construct. Framed creativity as an ecosystem applicable to complex engineering systems.
R. A. Beghetto &
J. C. Kaufman
(2021, 2023)		 Developed the 4C Model of Creativity (mini-c, little-c, Pro-c, Big-C) emphasizing developmental and contextual continua. Conceptualized creativity as scalable from personal insight to eminent innovation. Enabled recognition of creativity at multiple levels within engineering education and practice.
Note. This table summarizes major theoretical advances in creativity research from mid-20th century to the present, emphasizing their conceptual progression and practical relevance to engineering education and practice.
Table 2. Intellectual Progression of Runco’s Creativity Frameworks (1999–2025).
Table 2. Intellectual Progression of Runco’s Creativity Frameworks (1999–2025).
Intellectual Group / Theme Key Concepts / Framework Contribution to Framework Representative Studies / Citations
Foundational Knowledge & Consolidation Compilation of creativity knowledge, definitions, measurement approaches Established baseline understanding of creativity; emphasized hierarchical frameworks for studying creativity; provided structured definitions and initial typologies. Runco & Pritzker (1999); Runco (2007)
Hierarchical & Process-Oriented Models Hierarchical framework, dynamic & parsimonious models; primary vs secondary creativity Shifted from categorical to dynamic, continuous perspectives; emphasized personal, context-sensitive creativity and parsimonious explanatory models. Runco (2007, 2014, 2019); Runco & Beghetto (2019)
Measurement, Assessment & Divergent Thinking Divergent/convergent thinking, idea density, semantic distance, reliability and validity of creativity tests Validated cognitive predictors and assessment tools; clarified relationships between creative potential and actual performance; advanced empirical rigor in creativity research. Alabbasi, Runco, Acar & Jahrami (2025); Runco, Turkman, Acar & Alabbasi (2025); Runco, Giannouli, Ayoub & Alabbasi (2025); Alabbasi, Acar, Runco & Ogurlu (2024); Abdulla Alabbasi, Acar, Runco, Alsuqer & Aljasim (2025)
Types and Levels of Creativity Seven C’s framework, Four Ps, authentic vs pseudo-creativity, primary vs secondary creativity Refined taxonomy of creativity; distinguished between authentic human creativity, pseudo-creativity, primary/secondary forms, and multiple dimensions of creative acts; bridged psychometrics with conceptual theory. Runco (2020, 2022, 2025); Runco & Kim (2020); Yao, Runco, Mo, Zhang et al. (2025); Runco & Beghetto (2019)
Applied Creativity & Contextual Factors Creativity in education, workplace, curriculum, cross-cultural and virtual team contexts Demonstrated real-world application of creativity frameworks; highlighted influence of context, support, motivation, and time on creative outcomes. Palmer & Runco (2025); Alabbasi et al. (2025, 2025); Tadik, Runco & Bahar (2025); Tan, Ramsay et al. (2025); Gundogan & Runco (2025); Runco (2024, Transformational Creativity)
Human vs Artificial Creativity AI as artificial creativity, distinction from human creative processes, educational implications Delineated boundaries between human creativity and AI output; reinforced Seven C’s and parsimonious models in light of emerging technology; highlighted importance of authentic creative processes. Runco (2023, 2025, 2025); Runco & Gaynor (2024)
Meta-Analytic & Systematic Review Contributions Gender differences, gifted vs non-gifted populations, creativity gap across contexts Provided synthesis of large datasets; examined variability, reliability, and generalizability across populations; informed refinements to empirical measures and theoretical models. Alabbasi, Thompson, Runco et al. (2025); Alabbasi, Acar, Runco et al. (2024); Abdulla Alabbasi, Runco et al. (2024)
Note. The table organizes Runco’s work by intellectual focus, showing progression from foundational definitions to hierarchical models, measurement, typologies, applied contexts, and AI considerations. Publication years indicate chronological development, highlighting both theoretical advances and practical applications across his scholarship.
Table 3. Comparison of Human and AI Creativity Frameworks.
Table 3. Comparison of Human and AI Creativity Frameworks.
Dimension Lockhart (2024) Runco (2023)
Approach Broad philosophical and psychological theories (existentialism, intersectionality, embodied cognition) Specific theoretical update to the psychological Standard Definition of Creativity
Primary Focus The human condition (emotions, lived experience, identity, vulnerability) as the source of unique creativity Focus on the definition itself, adding components to differentiate sources of creativity
Key Components Empathy, social context, existential struggle, bodily experience Authenticity and intentionality
View of AI Fundamentally unable to replicate human creativity due to lack of human experience AI generates “artificial creativity,” which, while potentially original and effective, lacks human authenticity and intent
Note. Adapted from Lockhart (2024) and Runco (2023). Lockhart provides a broad philosophical foundation for human creativity, while Runco formalizes specific components to distinguish human from AI creativity.
Table 4. Philosophical Criteria Distinguishing Human Creativity from AI.
Table 4. Philosophical Criteria Distinguishing Human Creativity from AI.
Criterion Core Philosophical Concept Philosophers
Confronting existential challenges The courage to create Rollo May
Identity formation through social norms Identity and performativity Judith Butler
Navigation of multiple, overlapping identities Intersectionality Kimberlé Crenshaw
Engagement in emotional labor and communal experiences Love and vulnerability bell hooks
Self-awareness and struggle for freedom Self-creation and authenticity Simone de Beauvoir
Destruction of established ideas Creation and destruction Friedrich Nietzsche
Basis in physical and social contexts Embodied cognition Heidegger & Lockhart
Preference for human authenticity Ethical considerations Contemporary insights
Note. Derivative from Lockhart (2024). These criteria emphasize the human-specific qualities of creativity that AI cannot currently replicate.
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