Creativity and artificial intelligence in design

With the continuous development and maturity of artificial intelligence technology, a new round of technological and industrial change is rapidly emerging, profoundly affecting all aspects of human life, China is also rapidly entering the era of intelligence. In recent years, artificial intelligence has been widely applied in art design. Artificial intelligence technology has been extended to different types of art design fields. The main role of artificial intelligence in art design is assistant design. The purpose of this paper is to find out the shortcomings of artificial intelligence in the application of art design through the research on artificial-intelligence-based auxiliary methods of art design, so as to help realize the deep cross-border integration between artificial intelligence and art design. This paper first summarizes the concept and relationship between artificial intelligence and art design, and then briefly analyzes the role of auxiliary design. Then, the main problems of the current auxiliary methods are analyzed with the help of the intelligent algorithm. Finally, the improvement strategies of the auxiliary methods are proposed to solve these problems. The experiments in this paper show that the existing auxiliary design methods are general and have some problems, which hinder the deep crossover between artificial intelligence and artistic design.

Despite the rapid development of artificial intelligence (AI), skepticism persists among educators regarding its role in design education. This study uses a systematic literature review to locate and summarize the core papers from 2020 to 2023, categorizing the role of AI in design education. Following the PRISMA method, 35 papers were selected for review. The research reveals the potential impacts of AI on developing students' design skills, perceptions of AI applications among students and teachers, and challenges in implementing AI technologies. Drawing from these findings, the study proposes implications of AI on both the practice and theory of design education. By shedding light on the current state of AI integration in design education, this research aims to inform educators, policymakers, and stakeholders about the opportunities and challenges presented by AI technologies. Understanding these dynamics is crucial for effectively harnessing AI's potential to enhance design education and prepare students for future demands in the field.

With the rapid development of artificial intelligence (AI) technology, its application in the field of art design is expanding increasingly. The cross and integration of AI and art design have become the core driving force for promoting the reform of art design teaching and an inevitable path to achieve high-quality development of the art design industry. Firstly, based on the current development status of AI in the field of art design and professional education, this paper analyzes the transformation opportunities brought by AI to art design, including creativity generation, image recognition, style transfer, and design efficiency improvement. Secondly, it explores the paths for AIdriven reform of art design teaching from the dimensions of building an AI intelligent design support system, innovating teaching content, applying teaching methods, promoting interdisciplinary cooperation, and constructing teaching platforms. Finally, combined with the transformation trend of art design teaching, it puts forward countermeasures for reflections triggered by the integration of AI into teaching practice (such as copyright protection, avoiding technical dependence, and making up for emotional loss), thereby promoting the cross and integration of AI and art design teaching, providing practical methods for talent training of art design majors in various universities, and helping the art design industry achieve sustainable innovative development in the digital era.

The study investigates the evolving linguistic dynamics between humans and artificial intelligence (AI) by asking the central question: Who feeds whom? The research examines two key data sources through a discourse-analytic approach: real world human–AI interactions and AI-related discourse in public and corporate domains. Six recurring themes emerged: AI as Helper/Assistant, Human as Data Feeder, AI as Expert/Advisor, AI Anthropomorphized, Ambiguous Agency, and Human Deference to AI. This revealed a complex, bidirectional relationship shaped through language. While corporate discourse positions humans as data providers who “feed” and “train” AI, user interactions suggest a reversal, wherein AI increasingly supplies language, advice, and authority that users readily adopt. This linguistic inversion challenges traditional notions of control and authorship. The study also identifies a troubling ambiguity around agency, with users and developers attributing quasi-human traits and decision-making capacity to AI systems. Anthropomorphism and discursive deference further entrench AI as a credible, even empathetic, interlocutor. Language emerges as the mirror and engine of this relationship, shaping user expectations, reinforcing power dynamics, and influencing communicative norms. The findings highlight the urgent need for discursive transparency in AI design, critical literacy in user education, and expanded ethical frameworks that account for language as a form of soft power. Ultimately, this inquiry argues that understanding human–AI relationships requires more than technical literacy; it demands critical awareness of the linguistic structures through which these relationships are constructed, negotiated, and sustained. The metaphorical question—Who is feeding whom?—invites ongoing reflection on power, trust, and authorship in the age of AI.

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.

There is increasing attention to a new design methodology incorporating the latest achievements of a non-hierarchical way of forming based on the learning process of computers or machines, namely the concepts of artificial intelligence (AI) and immersive design, using virtual and other types of reality. The study aims to reveal the necessity to identify the main trends of artificial intelligence and immersive design use in architectural environment formation. The results can contribute to rethinking architectural design algorithms at the present stage and identifying potential opportunities for improvement. The paper describes the possibilities of AI and immersive design in architectural environment formation. Analysis of current research in this field has made it possible to identify trends that likely will continue to develop further and impact the architectural profession. One of these trends is the improvement of the architect’s toolkit, currently provided by additional plug-ins to existing programmes or separate services based on AI algorithms, but in the future, it may be a change in the programme code itself with a completely updated structure. In this case, the role of software from the architect-managed toolkit may shift to another plane, such as a digital assistant that partially or fully manages its responsibilities in working on an architectural project. The symbiosis of multidirectional but powerful immersive design technologies (VR, AR, MR) and AI algorithms is another trend in architectural environment formation. It can also be characterised as the most experimental and, at the same time, dynamically progressing, given the active development of its components. Another modern trend is to bring architectural design to a new level of a balanced combination of virtual and real. The immersion of the user in virtual reality for design work and demonstration of the relevant results is gradually changing as the 3D model of an architectural structure is increasingly manifested in the physical world, including at the implementation site, and interactive equipment with flexible and constantly evolving functionality provides architects with more opportunities for creativity. Keywords: artificial intelligence, immersive design, architectural environment.

Artificial intelligence (AI) is rapidly advancing and holds both opportunities and risks for the social work profession. This review article provides an analysis of the potential applications and implications of AI technologies for social work practice, education, research, and ethics. The article highlights promising uses of AI tools like predictive risk models, virtual simulations, chatbots, and data analytics to enhance social work services and training. However, it also critically examines the ethical considerations and social justice concerns related to the integration of AI in social work, including issues of privacy, transparency, accountability, consent, bias, displacement of roles, and threats to human dignity. To responsibly leverage AI's benefits while mitigating its risks, the article offers recommendations for social workers to collaborate on AI design, follow ethical protocols, pursue education on AI, and conduct further research. Key areas for future research identified include studying client perspectives on AI, testing the efficacy of AI systems for social work tasks, auditing algorithms for bias, and analyzing AI's impact on social work employment. In conclusion, while acknowledging AI's significant potential, the article emphasizes that purposeful oversight, participatory design, and adherence to social work values must remain priorities to ensure AI promotes dignity, autonomy, and justice. This review provides a balanced, evidence-based analysis to inform discussions on optimizing AI's integration in the social work profession.

We have to go back, back to the future! Reflecting on 75 years of human factors in the UK to shape a future of responsible artificial intelligence innovation

Global healthcare organisations are using artificial intelligence (AI) to improve cross-cultural understanding and diversity in patient care. AI helps with language translation, making communication, diagnosis and treatment more effective. AI also creates care plans that consider patients’ cultural preferences and assign culturally appropriate caregivers, enhancing care quality. However, biases in AI models are a concern, and involving minorities in AI design is important to reduce these biases. AI has great potential in healthcare, especially for underprivileged populations, by improving communication and reducing prejudices. Challenges like data biases, though, need to be addressed to prevent increasing inequalities. Strong regulations and diverse AI features are necessary to ensure fair and effective healthcare. Overall, AI can improve patient care by considering cultural diversity, but careful implementation is needed to avoid inequalities.

English speaking with artificial intelligence (AI): The roles of enjoyment, willingness to communicate with AI, and innovativeness

BackgroundDespite advancements in artificial intelligence (AI) to develop prediction and classification models, little research has been devoted to real-world translations with a user-centered design approach. AI development studies in the health care context have often ignored two critical factors of ecological validity and human cognition, creating challenges at the interface with clinicians and the clinical environment.ObjectiveThe aim of this literature review was to investigate the contributions made by major human factors communities in health care AI applications. This review also discusses emerging research gaps, and provides future research directions to facilitate a safer and user-centered integration of AI into the clinical workflow.MethodsWe performed an extensive mapping review to capture all relevant articles published within the last 10 years in the major human factors journals and conference proceedings listed in the “Human Factors and Ergonomics” category of the Scopus Master List. In each published volume, we searched for studies reporting qualitative or quantitative findings in the context of AI in health care. Studies are discussed based on the key principles such as evaluating workload, usability, trust in technology, perception, and user-centered design.ResultsForty-eight articles were included in the final review. Most of the studies emphasized user perception, the usability of AI-based devices or technologies, cognitive workload, and user’s trust in AI. The review revealed a nascent but growing body of literature focusing on augmenting health care AI; however, little effort has been made to ensure ecological validity with user-centered design approaches. Moreover, few studies (n=5 against clinical/baseline standards, n=5 against clinicians) compared their AI models against a standard measure.ConclusionsHuman factors researchers should actively be part of efforts in AI design and implementation, as well as dynamic assessments of AI systems’ effects on interaction, workflow, and patient outcomes. An AI system is part of a greater sociotechnical system. Investigators with human factors and ergonomics expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system.

Artificial intelligence (AI) systems are rapidly transforming the workplace, performing tasks once limited to human intelligence such as decision-making, prediction, and pattern recognition. While AI adoption offers opportunities to improve productivity, it can also create new occupational hazards and alter working conditions in ways that may harm worker health, safety, and wellbeing. Despite broader and growing attention to safe and responsible AI, there is limited integration of occupational health and safety (OHS) principles into AI design and adoption decisions. This paper outlines a framework for embedding an OHS perspective throughout the AI system life cycle, from problem definition to system retirement. The framework aims to ensure that safety, fairness, and worker wellbeing are prioritized in AI. We describe key OHS goals for each phase of the AI life cycle and describe practical strategies to support implementation. These strategies include participatory co-design with workers, equitable data collection, model training and validation that identify and minimize safety risks, transparent deployment practices, and continuous monitoring and retraining guided by risk management frameworks. We emphasize collaboration among AI system developers, OHS professionals, and worker and workplace representatives, to anticipate and address emerging risks. Integrating OHS principles into the AI system life cycle not only helps prevent harm but also fosters worker trust, strengthens system reliability, and promotes sustainable technological adoption. Embedding OHS principles into AI development ensures that the technology contributes to, rather than compromises, the protection and wellbeing of workers in a changing world of work.

The integration of Artificial Intelligence (AI) and Augmented Reality (AR) in education holds transformative potential, particularly in fostering inclusive learning environments for students with disabilities. This study investigates the integration of Artificial Intelligence and Augmented Reality to advance inclusive education. This research examines the analysis and design of AI and AR-based applications, employing a User Interface and User Experience (UI/UX) approach to cater to the diverse needs of students with physical, sensory, or cognitive impairments. By emphasizing inclusive design principles, the study aims to develop adaptive and accessible educational tools that promote engagement, deepen understanding, and improve learning outcomes. The research adopts a multidisciplinary methodology, combining insights from accessibility standards, UI/UX design frameworks, and educational technologies. Key objectives include identifying accessibility barriers, designing user-centric interfaces, and evaluating the effectiveness of AI-driven personalization and AR-enhanced interactivity in real-world learning scenarios. Preliminary findings highlight the importance of responsive and adaptive interfaces in facilitating equitable access and promoting active participation among students with disabilities. The study underscores the role of innovative technologies in bridging gaps in traditional education systems, ultimately promoting an inclusive and empowering learning experience. This research contributes to the broader discourse on leveraging technology to ensure educational equity and inclusion in the digital age.

This article examines how generative artificial intelligence (AI) design platforms are reshaping fashion and textile design by mediating creative processes, reconfiguring design roles and influencing aesthetic outcomes. While these design practices have traditionally relied on embodied techniques such as sketching or draping, AI platforms shift the creative process towards prompt-writing, thus transforming the designer’s role into one of linguistic precision. These systems not only facilitate design but also condition imaginaries, authorship and aesthetic norms. Drawing on an AI-augmented walkthrough methodology, we analyse four platforms through two themes: what we term the ‘limits of democratization’ and the ‘myth of the muse’. We show how platforms position themselves as accessible and inspirational while at the same time depending on opaque infrastructures, embedding aesthetic biases and upholding technical gatekeeping. Claims to democratization often obscure the need for professional knowledge and prompt fluency and overlook how defaults marginalize certain bodies, identities and styles. At the same time, the framing of AI as a muse conceals the constrained, algorithmically structured nature of digital creativity. Drawing on theories of ‘cultural infrastructure’, ‘distributed agency’ and ‘market agencements’, we examine how interface design, algorithmic suggestions and user scripts configure what can be created, who is positioned as a designer, and how creative practice is recognized and valued within fashion design culture. These dynamics show that AI design platforms do not simply mediate creativity; they reconfigure design practices and redistribute creative agency. Rather than serving as neutral tools, they function as socio-technical systems embedded in cultural and commercial infrastructures. By offering a critical, design-led perspective, this article contributes to understanding how generative AI reshapes the conditions under which fashion is imagined, produced and valued, thereby advancing emerging debates on AI and creativity in fashion.