The future is flow: Embodied constructal design for human creativity.

Design is how humans change the world, and in the age of AI it is increasingly a joint activity between humans and machines. This perspective argues that AI does not simply add new tools to the designer’s repertoire; it has the capability to reorganize the division of labor in design and, in doing so, reshape what it means to be a designer. We distinguish between representative technologies, which model and derisk complex systems (eg digital twins, immersive simulations), and operative technologies, which act directly within design processes (eg generative and agentic systems that propose, evaluate, and select alternatives). Viewed through the Five-Cycle model of design, we argue that these technologies widen inputs, accelerate exploration, and tighten feedback across problem definition, conceptual and embodiment design, and value proposition. In our model, representative tools derisk what to believe; operative tools derisk what to try. Together these technologies could enable closed-loop, hybrid human-AI design processes in which human roles shift from manual problem solving toward stewardship, curation, translation, and, in a democratized future, even historical guardianship of designerly knowledge. We contend that design science is essential for understanding and guiding this transition: explaining how human purpose, creativity, and responsibility are redistributed in human-AI teams; developing methods to study how values propagate through automated design workflows; and informing education and practice so that increasingly automated design processes remain aligned with human intent and societal well-being.

Physical intuitions about how enzyme structure and dynamics influence its function and property have enabled successful engineering outcomes, yet a systematic approach remains unknown for translating these qualitative and abstract "thoughts" into quantitative, actionable principles that lead to designs. Here we introduce MutexaGPT, an open-access, multi-agent large language model (LLM) platform that translates enzyme engineering intuition to physics-based simulations and thus variant designs. Through a web interface, MutexaGPT takes users' intuition-driven requests (expressed in plain English) as input, and then leverages its LLM agents (i.e., QuestionAnalyzer, WorkPlanningBoard, and ResultExplainer) to comprehend the request and elicit missing information, construct physics-based models, configure and execute high-throughput molecular modeling workflows, and eventually convert the molecular modeling results into actionable design proposals (such as a smart mutation library). An automated evaluation framework was established to systematically benchmark the prompt engineering strategies that allow each individual agent to achieve an optimal performance. We further demonstrate the utility of MutexaGPT in two protein engineering tasks. For the task of engineering halide methyltransferase towards bulkier substrates, MutexaGPT converted a cavity-engineering intuition into a smart library design that shows a 40% hit rate and around 4-fold activity improvement over a baseline strategy. For the task of engineering bidomain amylase for enhanced activity at lower temperature, MutexaGPT translated a statistics-based intuition into cold-adapted amylase variants that show 1.7-fold and 3.7-fold activity enhancement at 0 °C (experimentally validated). These results establish MutexaGPT as an intuition-to-design translator that integrates human creativity with high-throughput molecular modeling to democratize physics-guided, intuition-driven enzyme engineering.

ConspectusUpconverting nanoparticles (UCNPs) transform low-energy light into higher-energy photons, enabling applications in subwavelength and subsurface imaging, nanoscale sensing, therapeutics, optogenetics, printing, and optical computing. However, the widespread adoption of UCNPs is hindered by their low brightness and limited spectral tunability. Predicting the ideal nanoparticle architectures to overcome these limitations is challenging because UCNP photophysics are governed by highly nonlinear, complex energy transfer networks that span the excited states of lanthanide dopants. Due to the large number of possible combinations of dopants, concentrations, host matrices, heterostructures, and reaction conditions, optimizing the compositional and synthetic parameters of UCNPs using conventional trial-and-error approaches is intractable.This Account explores how researchers can overcome these challenges and enhance the properties of UCNPs using artificial intelligence (AI) and machine learning (ML). We first review how the early foundations of AI-guided discovery were established with automated experimental workflows and physical modeling. Using robotic synthesis platforms and differential rate equation models, researchers have successfully navigated high-dimensional compositional spaces to reveal optical phenomena, such as energy looping and photon avalanching, in nanoparticles.Building on these data-driven approaches, ML has been integrated into UCNP research initially for processing raw characterization data, such as automating the analysis of TEM images and time-resolved luminescence curves. AI approaches have been extended to interpret signals in applications that utilize UCNPs, such as classifying the cytotoxicity of drugs based on upconversion luminescence microscopy data. Most significantly, ML is driving the design of new UCNP compositions and structures, including our recent development of closed-loop active learning of UCNP core-shell heterostructures. By coupling Bayesian optimization with kinetic Monte Carlo (kMC) simulations, we achieved 110-fold enhancement in UCNP emission over just 40 iterations. To bypass the steep computational cost of simulating UCNP heterostructures with up to 9 shells, we leveraged differentiable deep learning surrogate models based on heterogeneous graph neural networks to perform inverse design. Notably, these hetero-GNNs were able to extrapolate far outside of the model's training data and predict UCNP heterostructure compositions with 6.5-fold more intense emission than the brightest UCNP in the training set.In the future, we predict that AI/ML approaches will become integral to the UCNP research. UCNP experiments may soon be accelerated by autonomous self-driving laboratories in which robotic synthesis, in-line characterization, and ML agents operate in a closed feedback loop to intelligently investigate underexplored chemical spaces. Large language models (LLMs) could parse literature to develop overarching hypotheses and detailed recipes for these autonomous workflows, with generative models suggesting novel structures to test. Together with human creativity and critical analysis, these AI tools will accelerate the discovery of advanced upconverting nanomaterials, aiding fundamental understanding of their mechanisms and inspiring a broader array of photonic applications.

ABSTRACT Given the increasing incorporation of artificial intelligence tools in hospitality education and industry, the role of AI in student experiential learning becomes more important than ever. However, limited research has explored the effective integration of AI into student learning. Adopting experiential learning theory, this research identifies the types of learning outcomes students demonstrate and their perceptions of conventional teacher-directed, AI-based, and human-AI collaborative teaching approaches in hospitality education. Using qualitative approach, our findings show distinctive learning outcomes for each approach: teacher-directed learning enhances critical thinking and real-world application, AI-based learning improves data literacy and efficiency, and human-AI collaboration strengthens reflective learning and the integration of objective and subjective insights. Moreover, students’ perceptions highlight the importance of sequence-based teaching approaches to balance human creativity with AI-driven analysis. Our findings provide significant insights on how AI can be effectively integrated to enhance experiential learning for future hospitality leaders.

ABSTRACT Coming from a different faith tradition, Malcolm X provoked Black Christianity to reexamine itself to address existential matters in practical theological terms as they pertain to the Black condition in the African Diaspora. Malcolm X embodied a lit fire under Black Christianity, which aimed to awaken its self-awareness of its complicit role in promoting White nationalism in religious garb. Malcolm derived an examination of the misuse of a major world religion that has historically manipulated human creatures to become docile and submissive to broader hegemonic structures, as contrary to God's will, by nature.

ABSTRACT This study offers an in-depth qualitative examination of how music teachers conceptualise and integrate artificial intelligence (AI) within their pedagogical and creative practices. Through interviews conducted across Australia and Italy using thematic analysis, two overarching themes emerged: (1) music teaching, and (2) ethics, ownership, and challenges. Findings reveal that teachers are developing sophisticated, domain-specific uses of AI extending beyond administrative convenience. Participants identified AI as a time-saving and organisational tool supporting report writing, rubric generation, lesson planning, and content summarisation. Teachers also described AI as a catalyst for creative scaffolding, enabling rapid musical prototyping, idea generation, notation workflows, and the structuring of DAW-based tasks, while positioning these applications as supplementary rather than substitutive. Hybrid practices, in which AI functions as a 'third ear' or co-creative reference, were evident alongside in examples of human musicianship guided by interpretive and embodied decision-making. Teachers expressed concerns regarding authorship, reliability, aesthetic homogenisation, and the erosion of student agency and critical thinking. Inconsistent institutional responses prompted calls for redesigned assessment strategies emphasising oral, practical, and iterative work. Overall, the study highlights a cautious optimism in which teachers strategically incorporate AI while reaffirming the centrality of human judgement, creativity, and relational pedagogy in music education.

Artificial Intelligence (AI) has significantly transformed the landscape of academic and professional writing by offering intelligent support in idea generation, content organization, language refinement, and editing. AI-assisted writing tools such as ChatGPT, Gemini, Perplexity, and QuillBot are increasingly used as cognitive support systems that reduce mental effort and improve writing productivity. However, concerns regarding originality, plagiarism, misinformation, and overdependence on AI-generated content continue to raise ethical and academic challenges. The present study examines the influence of AI-assisted writing on writing cognition and content authenticity through the concept of Human–AI Synergy. The study adopted a quantitative research design using a structured questionnaire distributed among 30 respondents, including students, teachers, and content writers familiar with AI writing tools. Statistical techniques such as Percentage Analysis, Chi-Square Test, Correlation Analysis, One-Way ANOVA, and exploratory Structural Equation Modeling (SEM) were employed for data analysis. The findings reveal that AI tools positively influence writing cognition by enhancing brainstorming, idea organization, productivity, and language quality. SEM results confirmed significant relationships between Human–AI Synergy, Writing Cognition, and Content Authenticity. The study also emphasizes that while AI improves writing efficiency, human creativity, ethical judgment, and critical evaluation remain essential to ensure originality and authenticity in writing practices. The study concludes that AI should be viewed as a collaborative cognitive partner rather than a replacement for human intellectual effort. Effective integration of AI in writing requires ethical awareness, institutional guidelines, and responsible human supervision to balance technological assistance with creativity and academic integrity.

This paper proposes an innovative integration of human creativity with artificial intelligence through the "prompt injection" method to significantly enhance efficiency and productivity in generating high-detail paintings, illustrations, and artwork which are typically created by layering shapes, colors, textures, lighting, and fine details over time. Artists often begin with rough sketches and composition, then gradually refine the piece through shading, highlights, brushwork, and small visual elements to build depth and realism. Their creation can often be time and resource-consuming. The study addresses this and aims for precise, detailed visuals in illustration, film storyboarding, and game design. By leveraging widely available AI systems as prototyping tools, the research aims to augment rather than replace human artistry, preserving creative essence while accelerating workflows. A comprehensive review evaluates state-of-the-art AI image generators against traditional tools. The prompt injection technique breaks down concepts into granular elements to guide AI outputs precisely. Artists decompose scenes, categorize keywords, and iterate for refinement, enabling rapid experimentation. The proof-of-concept applies this methodology in real-life scenarios, recreating award-winning filmography visuals from films. NIMA is used for technical and aesthetic benchmarking across six trials. The paper introduces a reusable repository for object descriptions and AI benchmarking to streamline future creations. While emphasizing benefits such as inspiration, democratization, and hybrid human-AI art, it also discusses challenges, including prompt-engineering precision, ethical concerns, and the need for refined algorithms. Ultimately, this approach offers a transformative tool for artistic prototyping, extending to creative industries and fostering novel digital-age art evolution.

This study aims to systematically investigate the role of entrepreneurial human capital in fostering creativity and innovation in the context of entrepreneurship. The approach used is a systematic literature review (SLR) with reference to the PRISMA guidelines to identify, select, and synthesize relevant articles from reputable databases. The results demonstrate that entrepreneurial human capital, encompassing knowledge, skills, and experience, has a significant influence on increasing innovation capacity. Additionally, creativity plays a crucial role in the link between human capital and innovation through the ability to generate new ideas. The findings also indicate that the relationship between human capital and innovation is influenced by mediating variables such as absorptive capacity and moderating variables such as entrepreneurial orientation and the digital context. However, there are variations in the findings, indicating that the relationship between variables is complex and is influenced by contextual factors such as the organizational environment and individual characteristics. This study makes a theoretical contribution by integrating various perspectives into the explanation of the relationship between human capital, creativity, and innovation. Moreover, this study also offers practical implications for the development of entrepreneurship by improving the quality of human capital and creating an environment that supports innovation. Consequently, this study is expected to serve as a basis for further research in the field of innovation-driven entrepreneurship.

ABSTRACT This study examines the ideological beliefs of religious Bible teachers in Israel’s State-General education system, challenging the assumed correlation between teacher identity and teacher ideology, and its subsequent impact on Bible pedagogy. Through qualitative interviews with 14 educators, the research identifies two distinct groups. ‘Traditionalist’ teachers maintain orthodox beliefs and reject biblical criticism, either through a priori theological dismissal or by actively undermining its relevance, though some selectively accept scientific claims that do not contradict their core faith. In contrast and paradoxically, ‘Critically Oriented’ teachers integrate academic-critical scholarship – viewing the biblical text as a human creation or the product of ‘soft’ divine intervention – and often find profound existential and faith-based benefits in these approaches. Aligning with broader socio-political progressivism, they maintain their religious commitment through theological flexibility and compartmentalisation, finding a natural home in pluralistic settings. This suggests that educational policymakers and school leaders must move beyond sociological stereotypes, focusing instead on educators’ actual ideological positions when navigating recruitment and placement, thereby offering a new lens for the global discourse on religious education.

This article discusses the valiha, a Malagasy tube zither, recognizing it as an anthropological phenomenon – posthumanistically speaking – relational. The valiha, Madagascar’s traditional and par excellence national musical instrument, is not merely an organological object constituting the material heritage of the Malagasy people. As a human creation, the instrument is not simply a carrier of knowledge about its culture, but an active participant in it, organizing human experience within the framework of social practices related to the transmission of ancestral tradition and adaptation in processes of global changes. Transformations in construction, tuning, and the instrument’s function are linked to stabilizations and transformations of social attitudes and values. Inspired by posthumanisms, the article transcends the mind-body and culture-nature divides to rethink the relationship between human and matter. The analysis covers the ontology of the musical instrument, its anthropological-organological evolution, its agency in shaping Malagasy music and identity, and valiha’s multidimensional instrumentality – musical, cognitive, social, and transcendental.

ABSTRACT This article is a response to a quiet crisis in gifted education: the rise of artificial intelligence (AI) as a potentially existential threat to human excellence (intelligence, talent, creativity, and wisdom) and, consequently, gifted and talented education. The article has three parts and is organized around eight arguments. The first part concerns the opportunities and challenges that the pervasive and profound influences of AI bring to gifted and talented education (the good, bad, and ugly of an age of AI). The second part concerns the need to clarify what we believe in and value: the power of the human mind, the power of human experience, and the power of human creativity and wisdom. The third part is a proposal to redefine giftedness and talent in a way that would strengthen the impact of gifted and talented education in the age of AI, by working with AI for developing human instrumentality, by working beyond AI to develop humanity and purpose, and by working above AI to assert control and leadership over technological development. Together, they make the transformational imperative for gifted and talented education for the decades to come.

Narrative design in historical games plays a critical role in shaping player immersion, cultural understanding, and historical representation. With the rapid advancement of large language models, their potential to generate historically grounded interactive narratives has drawn increasing attention, yet systematic comparisons with human-authored content remain scarce. This study investigates the narrative performance of AI models and professional game designers across three culturally distinct historical settings: Ancient Egypt, Medieval Europe, and Ming Dynasty China. A two-stage empirical design was employed: narrative generation by 10 advanced AI models and 30 designers, followed by expert evaluation using a validated five-dimensional framework encompassing historical authenticity, interactivity, immersion, structure, and innovation. The iterative development of the five-dimensional framework makes it a reusable methodological tool. Structural equation modeling revealed that AI excelled in structural coherence and adaptability, especially in culturally distant contexts, while human designers achieved greater cultural depth and emotional resonance in familiar settings. Results indicate that narrative quality does not depend solely on authorship but on the interaction between cultural familiarity, domain expertise, and narrative dimensions. The findings highlight the complementary strengths of AI and human creators, supporting hybrid workflows that combine AI’s efficiency with human cultural insight. Cultural familiarity was identified as a key moderating variable. Statistical analysis indicates that approximately 70% of the evaluated models achieved or exceeded the average performance of human designers in overall scores. However, this improvement was primarily driven by higher performance in the structure and interactivity dimensions. When these technical dimensions were excluded, only about 30% of frontier-scale models exhibited performance comparable to that of human experts in cultural authenticity.

The transformational changes that characterize the current stage of global economic development determine the growing role of innovative technologies in ensuring the stability and competitiveness of enterprises. One of the key areas of these changes is the introduction of the Industry 5.0 concept, which combines the digital tools of Industry 4.0 with a human-centered approach, sustainable development, and deep integration of the latest technologies into business processes. In this context, logistics is not only an operational function but also a strategic element capable of shaping the long-term competitive advantages of enterprises. In the context of globalization, growing market instability, increasingly complex supply chains, and the impact of external shocks (such as pandemics, military conflicts, and resource crises), an effective logistics system is becoming a determining factor in business resilience. Industry 5.0 offers new tools to address these challenges: the use of artificial intelligence, robotic systems, the Internet of Things, digital twins, and environmentally friendly technologies. Combining these with human experience, creativity, and analytical skills enables the creation of highly efficient logistics models that can quickly adapt to change. In addition, there is a growing need for personalized products and individualized approaches to customers, which also requires high flexibility and technological support for logistics operations. In such conditions, modern enterprises need a comprehensive analysis of the impact of Industry 5.0 on logistics activities.

ABSTRACT As organizations navigate the emphasis on human‐centric innovation in Industry 5.0, understanding artificial intelligence (AI) adoption in creative domains is crucial for organizational success. While established technology adoption models effectively explain AI integration in conventional business contexts, they insufficiently address the unique considerations of creative industries, where technological decisions involve complex negotiations between technical capabilities and artistic values. This study addresses this research gap by developing a comprehensive framework specifically designed to explain and facilitate AI adoption by creative professionals. The integrated framework combines established technology acceptance constructs with novel artist‐centric variables that account for the symbolic, identity‐based, and aesthetic dimensions of technology use in the arts. Through qualitative phenomenological analysis of 42 music professionals from the Indian film music industry and synthesis of multidisciplinary literature, we demonstrate how this framework addresses the limitations of current technology adoption models when applied to creative contexts. A key finding is that partial adoption represents an optimal stable endpoint rather than a transitional state, with 78% of successful adopters maintaining deliberate boundaries between AI assistance and human creativity control. The proposed model offers significant theoretical contributions by expanding technology adoption research beyond utilitarian organisational contexts and provides practical value for businesses seeking to implement human‐centric AI solutions that respect creative values while driving organisational excellence in Industry 5.0.

Against the backdrop of generative artificial intelligence entering the artistic domain, this study examines how artists' creative agency and professional identity are reshaped by AI intervention. Through comprehensive semi-structured in-depth interviews with eight professional creators spanning diverse artistic disciplines including dance, theatre, photography, design, and new media art, this research provides valuable insights into the evolving relationship between human creativity and artificial intelligence. The findings reveal a nuanced perspective where artists predominantly view AI as an augmentative technological tool that enhances creative efficiency and expands conceptual boundaries, rather than perceiving it as an autonomous creative entity. Notably, artists with more extensive professional experience demonstrate greater confidence and less anxiety regarding potential AI replacement. While AI exhibits significant strengths in image generation and workflow optimization, the study identifies clear limitations in its capacity for emotional expression and narrative development, particularly within the context of theatrical performance, hence the research indicates that AI integration has not fundamentally disrupted artists' sense of creative agency nor significantly altered their core professional identity in the immediate term. However. The increasing prevalence of AI in creative practices necessitates a strategic response from educational institutions; based on these findings, this study strongly advocates for the integration of comprehensive AI literacy programs in higher education curricula, aiming to foster a sustainable and synergistic relationship between artistic practice and artificial intelligence technologies.

Generative Artificial Intelligence (GenAI), which refers to models capable of creating original outputs such as text, images, audio, 3D content, and code, is transforming creative industries at a rapid pace. This paper examines the influence of key generative approaches—including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), autoregressive language models, diffusion models, and multimodal systems—on workflows in areas such as art, design, animation, music, marketing, and software development. It outlines the study’s objectives and scope, and discusses underlying architectures, operational processes, and the necessary hardware and software infrastructure. In addition, the paper explores practical applications, advantages, and major challenges, including technical limitations, ethical concerns, and economic implications. The study concludes by proposing practical strategies for individuals and organizations to adopt GenAI responsibly, ensuring that innovation is balanced with the preservation of human creativity and broader societal values.

This article explores debates concerning the copyrightability of AI-generated content (AIGC) within Chinese legal scholarship through the lens of art law, calling for a humanistic approach to the interpretation of Chinese copyright law given new technological conditions. The three basic concepts that guide the analysis are the following. The first is "authorship," where the art theory's deconstruction of the artistic subjectivity should not eliminate the human creator as an author from the copyright framework of the creative artist. The authorship framework legally establishes authorship based on natural persons. Secondly, the article discusses the concept of "creation" and distinguishes spontaneous artistic expression arising from algorithmic generation. Thirdly, regarding the standard of "originality," the article points out that AIGC lacks the historicity and authenticity associated with traditional human representations of creativity. The formalistic criteria used to assess originality cannot be applied with equal weight when evaluating AIGC.

The rise of Large Language Models which are often referred to as LLMs has brought about a significant change in how software development is conducted. This change marks the start of a collaborative relationship between humans and artificial intelligence. LLMs are not just automation tools anymore; they are increasingly seen as intelligent partners that help developers during various stages of the software development process. This literature review seeks to analyze critically the nature of collaboration between humans and AI in modern software engineering, focusing specifically on how LLMs impact developer experience, productivity, and decision-making. This paper draws on a wide array of academic studies, technical reports, and industry practices to look into the various roles that LLMs play in tasks like code generation, debugging, documentation, testing, and knowledge retrieval. It examines how developers engage with these systems in real-world situations, often participating in iterative and conversational workflows that mix human creativity with machine assistance. The review points out that LLMs not only speed up development timelines but also change cognitive workflows by lessening repetitive tasks and allowing developers to concentrate on more complex problem-solving and design issues. However, the use of LLMs in software development comes with its own set of challenges. This paper evaluates critically the concerns regarding the reliability and accuracy of outputs generated by these models, the danger of becoming too reliant on AI systems, and the possible decline of basic programming skills. It also discusses wider issues such as ethical concerns, data privacy, biases in the outputs from models, and the lack of transparency in decisions made by AI.