The Double-Edged Roles of Generative AI in the Creative Process: Experiments on Design Work

This study explores the incorporation of generative AI (GenAI) within a 300-level advertising design and layout course. Through collaborative learning and hands-on experimentation, students engaged with GenAI across various projects, unveiling the potential to augment ideation and alternative workflows in design. Students emphasized the need to balance GenAI’s impact on originality and strategic creativity. While recognizing GenAI’s transformative power, students expressed concerns about its implications for careers and the integrity of creative output. Findings suggest curriculum adopting GenAI that students must understand GenAI, begin with a focus on process instead of final design, and make time for discussion and reflection. Collectively this builds confidence leading to creative uses of the technology.

Generative AI (GenAI) presents new opportunities for learner-centred teaching. This study explores its potential in co-creating learning personas—fictional yet research-informed representations of students – for energy digitalisation education, a field that attracts learners from diverse backgrounds. We document the process of generating nine learner personas with GenAI and evaluating their quality through expert review and benchmarking against publicly available LinkedIn profiles of professionals in similar roles. Our findings indicate that GenAI effectively differentiates professional roles, captures key job-related challenges, and reflects learner motivations, making it a valuable tool for curriculum design. However, critical limitations persist, with GenAI creating overly idealised professionals, lacking diversity in the supporting AI-generated images, and overlooking some nuanced real-world complexities. These challenges highlight the need for human oversight to ensure authenticity, inclusivity, and ethical depth. Based on our findings, we provide recommendations for co-creating personas with GenAI for curriculum design.

Objectives This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method Using a mixed‐methods approach, we combined quantitative, qualitative, and semi‐experimental methodologies to capture the complexities of GAI tool usage. A total of 897 individuals completed an online survey with both closed and open‐ended questions; additionally, 38 students participated in a semi‐experimental stage to assess the impact of an online course, “GAI: From Theory to Practice.” Results Key findings underscore the role of personality traits such as openness and self‐efficacy in promoting GAI usage, mediated by behavioural perceptions and digital engagement patterns. In line with the UTAUT2 model, Performance and Effort Expectancy were the strongest predictors of GAI usage. Social media engagement and information overload management also emerged as predictors, illustrating the interplay between digital behaviours and GAI adoption. Qualitative findings revealed a nuanced understanding of user perceptions, emotions, and societal needs, uncovering themes of GAI's revolutionary potential, emotional ambivalence, and the demand for regulatory and educational frameworks. The experimental results demonstrated significant improvements in students' perceptions of GAI utility, self‐reported skill levels, and GAI usage across work, study, and personal contexts following the course. Conclusion This research advances understanding of GAI's societal integration and highlights the need for targeted training and regulatory policies to support responsible adoption and enhance digital literacy. It offers practical recommendations for educators, policymakers, and designers of generative AI tools.

The use of generative AI has increased drastically in recent years, offering transformative potential in creativity, productivity, and decision-making across various domains. However, concerns are growing among practitioners and academics about its tendency to produce inaccurate or nonsensical outputs—a phenomenon known as generative AI hallucinations. While scholars have begun exploring this phenomenon, most of the existing research focuses on technical issues and solutions. This research takes a behavioral approach to examine the association between individual personality traits and tolerance of generative AI hallucinations. Drawing on Error Management Theory and the Big Five personality traits, we examine how individuals with different personality traits tolerate generative AI hallucinations. The study contributes to calls from management journals to investigate the challenges associated with generative AI use. Our findings provide important theoretical implications and offer actionable insights for engineering managers seeking to implement generative AI.

The arrival of Generative AI (GenAI) has sparked intense debates in Higher Education, particularly within open and distance learning (ODL), where digital mediation shapes how students and educators engage with knowledge. Much of this debate remains focused on regulation or instrumental use, leaving less space for creative, multimodal, and collaborative approaches that position GenAI as a learning partner. This paper draws on People, Places and Pasta or Uncreative Explorations as Collective Intelligence (Nerantzi et al., 2025), a project developed during and after the EDEN 2025 conference. The project wove together scholars’ notes and reflections into four creative outputs: a pedagogical recipe, a Socratic dialogue, a collective poem, and a mosaic of visual metaphors. Each functions as a pedagogical instrument that opens new pathways for integrating GenAI into ODL in inventive ways. These creative, critical, and multimodal processes are especially relevant in distributed, asynchronous environments that demand autonomy, collaboration, and multimodal fluency. We argue that these approaches can foster critical GenAI literacy, creative and critical expression, and student agency. They invite educators to embrace human–GenAI co-creation not as a substitution, but as a catalyst for thoughtful, critical, and collective intelligence for their own professional development and their students.

The advancements achieved by generative AI have marked a significant step in creative art, raising copyright law questions, particularly around issues of authorship, originality and the role of AI in the creative process. Copyright law has grappled with similar challenges before, especially with the advent of emerging technologies, as evidenced during the early days of photography. Just as photography was initially viewed as the product of a machine rather than a human creation, so is output created with the support of generative AI often seen as predominantly AI driven. By drawing an analogy with the camera, this article explores how the users’ expression of free and creative choices, a fundamental aspect of originality under EU copyright law, can be identified at various stages in the production process of AI-supported output. It argues that the stage of ‘taking a photograph’ encompasses a series of human decisions rather than being an isolated act of pressing the shutter. Similarly, if the execution stage is seen as a process of converting users’ decisions into a preliminary output—much like ‘shooting a photograph’—those decisions can also serve as an additional element for attributing originality to AI-supported outputs. In analysing the emerging standards of authorial intent and control in assessing originality for AI-supported outputs, the article explores the distinction between semantic and categorial intentions, as well as the interplay between intent, control and the predictability of the outcome. It concludes that since categorial intent is always achieved and therefore infallible, the AI-supported output will consistently align with the author’s conception or fundamental approach to the work. Moreover, while authorial control and intent alone cannot establish originality, they can nonetheless contribute to assessing the originality of AI-supported output when they are evident in the creative process or its outcomes.

Generative AI is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. A key question is how generative AI affects learning-namely, how humans acquire new skills as they perform tasks. Learning is critical to long-term productivity, especially since generative AI is fallible and users must check its outputs. We study this question via a field experiment where we provide nearly a thousand high school math students with access to generative AI tutors. To understand the differential impact of tool design on learning, we deploy two generative AI tutors: one that mimics a standard ChatGPT interface ("GPT Base") and one with prompts designed to safeguard learning ("GPT Tutor"). Consistent with prior work, our results show that having GPT-4 access while solving problems significantly improves performance (48% improvement in grades for GPT Base and 127% for GPT Tutor). However, we additionally find that when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction in grades for GPT Base)-i.e., unfettered access to GPT-4 can harm educational outcomes. These negative learning effects are largely mitigated by the safeguards in GPT Tutor. Without guardrails, students attempt to use GPT-4 as a "crutch" during practice problem sessions, and subsequently perform worse on their own. Thus, decision-makers must be cautious about design choices underlying generative AI deployments to preserve skill learning and long-term productivity.

Against the backdrop of a sustained surge in global demand for learning Chinese, international Chinese language teachers face severe challenges across multiple dimensions, including cross-cultural teaching, instructional innovation, and resource development. The advent of Generative Artificial Intelligence (generative AI) offers new approaches to address these challenges; however, its value and potential risks in promoting the professional development of international Chinese language teachers have yet to be systematically explored. Drawing upon the Technological Pedagogical Content Knowledge (TPACK) framework, this paper investigates the empowering potential of generative AI for international Chinese language teachers on multiple levels. It also identifies key challenges in practical implementation, such as technological barriers, ethical and cultural sensitivities, and the integration of new technologies into teaching. Through a comprehensive analysis of these issues, the paper proposes a system-level pathway involving technological optimization, teacher capacity building, and pedagogical integration. The aim is to provide an actionable framework and strategies for the wider adoption and deeper integration of generative AI in international Chinese language education. The findings show that only when technology optimization, policy support, and teacher training work in tandem can generative AI effectively drive the professional growth of international Chinese language teachers and foster teaching innovation, thus ultimately contributing to higher-quality and more culturally inclusive sustainable development of Chinese language education worldwide.

Students with learning disabilities (LDs) face significant challenges in key academic areas such as reading comprehension, cognitive organization, self-expression, mathematics, and handwriting. These difficulties increase their susceptibility to discrimination and mental health related issues. Although existing studies have primarily focused on AI’s diagnostic capabilities, there is limited research examining how Generative AI (GenAI) can be utilized to produce measurable learning outcomes and enhance learning experiences for students with LDs. Moreover, GenAI is increasingly gaining prominence in educational settings. Therefore, the relationship between GenAI tools, LDs, and instructional methods needs to be further examined. This research aims to develop a comprehensive theoretical framework for helping design and implement tools specifically tailored to the unique needs of students with LDs. A prototype based on this framework will be implemented in selected educational settings to assess its effectiveness in improving learning outcomes and providing targeted support to students with LDs. The prototype will provide mobile phone integration to ensure scalability and enhance educational accessibility. The expected findings will contribute to the promotion of more inclusive learning environments for students with LDs.

This study investigates the potential of generative artificial intelligence–based text-to-image models as early-stage decision-support tools in sustainable tiny house design. Within the conceptual design process, the study systematically models key components—formal schema development, material selection, and spatial atmosphere—through a controlled prompt framework that enables structured comparison of design alternatives. Design outputs generated by diffusion-based models, including ChatGPT (DALL·E 3), Copilot, and Gemini AI, are evaluated using a multi-criteria framework encompassing sustainability principles (use of natural materials, compact planning, energy-efficient openings), typological consistency, and overall design quality. To examine how prompt variations shape design outcomes, three analytical dimensions are defined: (1) morphological decisions (plan layout, roof typology, form), (2) material decisions (timber, recycled composites, hybrid systems), and (3) spatial atmosphere (daylighting, color palette, interior warmth). The findings show that text-to-image workflows not only accelerate the generation of design alternatives but also enable a structured interpretation of how different design options align with specified criteria. In this sense, generated outputs function as interpretable design propositions that can be comparatively assessed within a transparent evaluation logic. The results further suggest that generative AI supports designers by externalizing design reasoning, facilitating the exploration, comparison, and justification of early-stage decisions. These results position generative AI as a viable component of next-generation design decision-support systems, particularly in contexts where rapid iteration, multi-criteria evaluation, and the articulation of design reasoning are critical.

Generative AI is paving its way into the research process. Among the plethora of available generative AI solutions, the generation of synthetic data is one of the most controversial. The current division of opinion and the lack of formal approach to AI use in research create a situation of conflicting bad practices and under-used potential. This work aims to add nuance and structure to this research practice by providing a general framework to evaluate the use of synthetic data in different stages of the research process, based on the objective and methods of generation. Relying on a breakout literature review, we explore the fields of Data quality management and Control theory to transfer method theories from these fields to help us build the framework. The resulting conceptual framework provides an iterative scheme where, based on the desired properties of the data and its comparison to the synthetic result, the researcher can improve the outcome of the generation process and, equivalently, formally present the properties that make this data suitable for research.

On the technical side, both traditional, shallow feature-based methods (e.g., Support Vector Machines and Random Forest) and deep neural network models (e.g., LSTM and CNN types) have been utilized (Khare et al., 2024;Pepa et al., 2023). A key focus has been the development of multimodal systems that integrate diverse data sources, such as facial expressions, voice tone, dialogue sentiment, and physiological signals (Geetha et al., 2024). Multiple signals enable the aggregation of complementary information, leveraging the strengths of various measurement techniques to enhance robustness and accuracy.Technology is only effective with users; thus, beyond developing methods, fostering adoption among professionals and citizens is essential. Alongside technical advancements, researchers have explored psychological and social factors influencing the acceptance and trust of AI systems, particularly in healthcare and education. Trust has proven pivotal for AI adoption, with studies investigating how to build it and what system attributes ensure trustworthiness (Li et al., 2024). Key trust factors include explainability, transparency in processes and data usage, and the credibility of the institutions behind AI development.This Research Topic explores novel theories, methodologies, and applications of Emotion AI in healthcare and education. The published works address technical aspects, models, and AI acceptance and literacy. Burgess et al. (2023) evaluated automated facial coding software for parent-infant interactions across five studies with fathers and mothers. Automated detection rates were low (~25%) compared to manual coding in naturalistic settings but strongly correlated with manual assessments, particularly for positive expressions, when successful. Key challenges included poor lighting, facial occlusion, and rapid movements, highlighting the need for greater robustness in real-world conditions. Despite these limitations, the study demonstrated the potential of automated systems for analyzing authentic emotional expressions in parent-child interactions. 2023) introduced an innovative semi-supervised learning approach for stress monitoring. Using physiological data from 14 participants across five experiments, they achieved 77% accuracy with label propagation and 76% with deep autoencoders despite utilizing only 17% labeled data. Their method matched the performance of fully supervised approaches while substantially reducing annotation requirements, offering a practical solution for continuous stress monitoring in real-world healthcare applications. Žvanut and Mihelič (2024) identified four distinct attitudes among older adults toward domestic social robots: Cautious Optimists, Skeptical Traditionalists, Positive Optimists, and Technophiles. Through interviews with 24 participants, they highlighted the influence of factors like technology familiarity, privacy concerns, and perceived utility on AI acceptance. Their findings offer valuable insights for designing emotionally intelligent robotic assistants tailored to diverse user needs and concerns. Shen and Cui (2024) investigated the link between psychological needs satisfaction and AI literacy in 445 university students. Their findings revealed that technical and teacher support positively impacted students' autonomy and competence, subsequently improving AI literacy. Notably, satisfying psychological needs proved more critical than direct support in enhancing AI literacy. These insights offer valuable guidance for designing AI-enhanced educational environments that foster better learning and engagement. Gong et al. (2024) examined patients' trust in AI-powered pharmacy intravenous admixture services across five studies. They found that patients generally trusted AI PIVAS less than human services, primarily due to a limited subjective understanding of AI systems. However, informed consent significantly improved trust by enhancing patients' understanding. This study underscores the critical role of transparent communication and psychological factors in adopting AI in healthcare.The articles in this Research Topic advance methods for analyzing real-world datasets and explore how Emotion AI can be effectively integrated into healthcare and education, addressing challenges related to trust, privacy, and user acceptance. While Emotion AI shows promise, its use in these fields raises technical and ethical concerns, including data privacy, surveillance, and potential misuse of personal information. Clinical applications are further constrained by small sample sizes, lack of control groups, limited real-world testing, and methodological variability, which hinder reproducibility and reliability (Pepa et al., 2023). These studies tackle these challenges by developing novel model-training approaches for limited and noisy labeled data and identifying factors influencing AI acceptance and trust.Future research should prioritize enhancing the robustness of emotion recognition in naturalistic settings, developing efficient learning methods to reduce dependence on labeled data, and addressing ethical considerations to support user acceptance of AI. Emphasis on cross-cultural validation and long-term evaluation in real-world applications is crucial. Recent generative AI (GenAI) foundation models, such as GPT-4, offer significant potential for advancing Emotion AI due to their versatility (Cheng et al., 2023). GenAI has already shown promise in recognizing emotional indicators (Elyoseph et al., 2024) and could help alleviate data scarcity challenges that hinder specialized model development. Additionally, there is growing interest in the emotional analysis of groups and crowds, broadening the field's scope (Veltmeijer et al., 2023;Li et al. 2024).

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated art is reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths.

This paper introduces generative AI for the graphic design of student presentation aids in a university public-speaking classroom. Students learn to use generative AI as an efficient enhancement to the creative process while preserving the integrity of the content. Students’ slide presentations show improvement resulting from the activity, and students report that the activity saves time and relieves stress. Courses: Public Speaking, Advanced Public Speaking, Business and Professional Communication, face-to-face and online. Objectives: Students learn how to improve the visual design of their presentation aids through use of generative AI. Students will reflect on the ethics of using AI as an enhancement to human agency and not as a replacement for it.

Objectives/Goals: To explore how generative AI and chatbot technologies can transform clinical research administration by improving operational efficiency, reducing administrative burden, and thereby enhancing overall productivity and accuracy in clinical research environments. Methods/Study Population: This explores AI’s application in enhancing clinical research administration. We specifically address AI’s role in QCT/MCA activities, charge master data cleaning, and generating IRB consent forms from award documents. AI algorithms optimize charge master data for accuracy and compliance. Generative AI models are employed to produce IRB consent forms efficiently, incorporating key grant documents. AI also conducts thematic analyses of historical CTSA aims to identify trends and recurring themes. Furthermore, AI-assisted tools enhance study design through innovative approaches to hypothesis generation, sample size calculation, and protocol development. Integrating these AI methods aims to significantly improve efficiency, accuracy, and overall quality in clinical research administration. Results/Anticipated Results: Incorporating AI into clinical research administration will yield improvements in efficiency and accuracy. AI-driven QCT/MCA steps are expected to reduce human error and enhance data integrity. Chargemaster data cleaning via AI prompts will likely result in optimized, error-free data, ensuring compliance with regulations. The use of genAI for creating IRB consent forms from grant documents should significantly streamline the IRB approval process, reducing preparation time and administrative burdens. Thematic analysis of CTSA aims by AI will provide deep insights into historical trends and recurring themes, aiding in strategic planning. AI-assisted study design tools are anticipated to optimize sample estimation, protocol development, and advance the quality of clinical research administration. Discussion/Significance of Impact: The significance lies in enhancing efficiency, accuracy, and quality in clinical research administration. By streamlining processes, reducing errors, and providing strategic insights, AI supports the CTSA mission to accelerate translational research, thus improving public health outcomes and scientific innovation.