Search Results (313)

Artificial Intelligence (AI) literacy and competency in pre-service teacher education refer to a programme-level implementation that enables teachers to work with AI systems effectively, critically, and ethically across university coursework, school placements, and early-career practice. This includes not only capability, but also professional enactment, where teachers apply AI-related knowledge in context-sensitive and pedagogically grounded ways. AI literacy refers to a shared knowledge base for understanding how AI systems generate outputs, how to evaluate and verify AI-supported information, and how to reason about task–tool fit in relation to fairness, privacy, transparency, accountability, academic integrity, equity, and environmental sustainability. AI competency refers to the application of this literacy in routine professional tasks, such as designing and justifying AI-informed teaching, learning, and assessment, protecting students’ and school data, documenting decisions, and revising AI-supported materials after checking for reliability, transparency, accountability, and equity. Together, literacy and competency extend beyond personal use of AI by preparing future teachers to support students’ creative, critical, and ethical engagement with AI, while keeping classroom practice aligned with educational goals, objectives, and values. Full article

In the context of digitalization and growing demands for environmental responsibility, creative industries are seeking ways to reduce their material footprint. The purpose of this study is to evaluate the role of digital technologies, such as virtual prototyping and digital fashion, in shaping new sustainability standards. To achieve this, a systemic multidisciplinary approach was applied, combining comparative analysis, quantitative assessment of key indicators (MIRR, CFCI, VSR), and the calculation of the Integral Sustainability Index (ISI).The results show that virtual prototyping reduces material costs by 45–65% and the number of physical prototypes by 3–5 times; however, its energy efficiency depends on project complexity and is achieved only after the ‘energy break-even point.’ Digital fashion practices demonstrate the potential to reduce the carbon footprint, but only when utilizing energy-efficient digital infrastructure. The integrated assessment revealed an increase in the overall level of sustainability (with $ISI$ rising from 0.52 to 0.71) during the transition to digital processes. The main conclusion is that digital technologies establish new sustainability standards, yet their positive impact is realized only through the conscious design of technological systems, business models, and institutional environments focused on balancing environmental, economic, and social goals. Full article

This research investigates the capacity of Artificial Intelligence (AI) to serve as a generative and interpretative framework for revitalizing Al-Qatt Al-Asiri art. By developing sustainable design solutions for temporary exhibitions, the study seeks to reinforce Saudi Arabia’s cultural and touristic identity through a synthesis of heritage and technology. The study adopts a descriptive–analytical and applied methodology to examine the potential of AI to support creative design processes that integrate authenticity and innovation while preserving local heritage and meeting environmental sustainability requirements. Utilizing this descriptive–analytical and applied methodology. the study evaluates the efficacy of AI in augmenting creative design processes. The primary objective is to reconcile cultural authenticity with modern innovation, ensuring the preservation of local heritage while adhering to rigorous environmental sustainability standards. A controlled design experiment was executed for a temporary heritage exhibition, employing AI applications to simulate the complex decorative motifs of Al-Qatt Al-Asiri art. These technologies were used to generate sustainable exhibition units constructed from reusable local materials, bridging the gap between the digital generation and physical sustainability. This study presents a theoretical framework, a review of previous studies, the research methodology, quantitative and qualitative evaluation results, and an expert panel assessment. It involved three expert reviewers who evaluated the proposed design models based on eight sustainability criteria. This study also utilized a structured evaluation tool and AI applications, including ChatGPT-5.2, OpenAI and Gemini 3 Pro—Nano Banana. The results of the exploratory study indicate that the use of AI contributes to achieving a balance between preserving traditional aesthetic identity and promoting sustainable design practices derived from the characteristics of Al-Qatt Al-Asiri art. It also enhances cultural and tourism engagement by integrating AI applications into artistic design processes. The findings also revealed no statistically significant differences among the experts’ evaluations regarding the sustainability criteria of the implemented models. This study recommends integrating AI technologies into art and design education programs at Saudi universities and developing ethical and technical guidelines that ensure the preservation of heritage and cultural identity when applying AI in design practices. Full article

Generative artificial intelligence tools are reshaping design by enabling novice designers to produce professional-quality user interfaces rapidly. However, for novice designers, exposure to AI-generated outputs that are far beyond their capabilities can inhibit creative growth. In this work, we investigate AI overperformance, when superior AI outputs lower the creative confidence of novices, and explore whether human-centred and explainable AI interfaces can mitigate such effects while sustaining creative agency. We conducted a within-subjects experiment with 75 novice designers using a web-based research platform. Participants completed mobile app design tasks under three conditions: Human-Only (baseline), AI Overmatch (exposure to superior AI outputs), and XAI-Enhanced (exposure to AI outputs with an embedded explainable interface). A repeated-measures ANOVA indicated that creative self-efficacy varied significantly, F = 24.67, p < 0.001, η² = 0.18. While creative self-efficacy was significantly decreased in the AI Overmatch condition, M = −1.18, SD = 0.32, when compared to the Human-Only conditions, M = 0.08, SD = 0.15, this was significantly increased in the XAI-Enhanced condition, M U= 0.42, SD = 0.18. This also led to a rise in creative performance across both ideation and output quality. The results showed that the AI Overmatch condition significantly reduced creative self-efficacy and originality; however, this negative effect was mitigated by the XAI-Enhanced interface, which enhanced confidence and idea quality. Mediation analysis demonstrated that expectancy disconfirmation explains the negative impact of AI overperformance on human creativity. These findings provide constructive design principles for educational AI tools and contribute to HCI theory by demonstrating that pedagogically oriented, transparent AI supports human–AI collaboration without diminishing human agency. Full article

The interaction between humans and artificial intelligence has become a critical channel for information exchange, yet no quantitative, theoretically grounded framework exists for measuring information efficiency in human–AI communication. This study empirically validated an info-metrics framework operationalizing information efficiency through three dimensions—information density (D), relevance (R), and redundancy (Q)—synthesized into an information efficiency metric (IEM). We analyzed 60 AI responses from ChatGPT 5.2 and Claude Opus 4.5 across factual, analytical, and creative question types using combined coding, automated structural measures, and human evaluation of informational units. The results showed that information density and relevance positively contributed to IEM, while redundancy had a negative contribution. Efficiency varied by task type, with factual prompts showing the highest variability across models and highest efficiency. Contrary to expectations, creative responses did not exhibit higher redundancy, suggesting that expressive diversity does not necessarily constitute informational noise. The framework offers a task-sensitive, theoretically grounded approach to evaluating human–AI information exchange beyond correctness or subjective quality judgment, supporting systems-oriented optimization of conversational AI protocols. Full article

Recent work on the evaluation of large language models emphasizes that the relevant unit of intelligence is not the artificial system alone but the human–AI hybrid. In parallel, topological and dynamical models of cognition based on Painlevé equations and non-semisimple topology propose that consciousness, intelligence, and creativity emerge from constrained long-horizon dynamics near criticality. This perspective article argues that these two research directions are deeply compatible. We show that the empirical framework for human–AI collaboration can be interpreted as a fusion process between complementary cognitive sectors: exploration (AI) and selection (human cognition). The dynamical mechanism underlying this fusion is identified with noisy phase locking between cognitive oscillators. Two independent routes to a universal $1/f$ spectral signature are developed: a geometric route through the WKB/Stokes analysis of Painlevé V confluence, and an arithmetic route through the Mangoldt function and harmonic interactions in phase-locked loops. We connect these results to the Bost–Connes quantum statistical model, whose phase transition at the pole of the Riemann zeta function provides an exact mathematical framework for the lock-in phase hypothesis of identity consolidation in AI systems. This synthesis suggests a unified research program for hybrid intelligence grounded in topology, dynamical systems, number theory, and real-world AI evaluation. Full article

The integration of Artificial Intelligence (AI) into Architecture, Engineering, and Construction (AEC) practice is reshaping building design processes and collaborative workflows. However, AI’s role as a design collaborator remains poorly understood across educational and professional contexts. To address this gap, this study conducts an empirical survey of built environment students, academics, and professionals. Collectively, the study develops a comprehensive view of AI’s role in building design collaboration. The survey findings (n = 155) show widespread use of Large Language Models (LLMs) and image-generation tools across design education and practice, especially for creative and documentation-related tasks. While AI is valued for enhancing productivity and streamlining workflows, respondents also express concerns around technology dependency, data privacy, bias, and trust. This study contributes dual-perspective insights—encompassing both theoretical foundations and contemporary perceptions—into AI’s evolution towards transparent and multimodal design collaboration. The findings support a more structured and context-aware integration of AI tools into building design practice. Full article

This study investigates how integrating the 4PADAFE instructional design methodology with generative artificial intelligence (GAI) tools helps develop innovative, pedagogically sound digital learning environments in higher education. To meet the demand for scalable and flexible instructional models, 4PADAFE offers a seven-phase, iterative framework that connects pedagogical goals with the creative use of AI-powered tools. Using a qualitative exploratory approach, 20 Systems Engineering students applied the methodology to collaboratively create a four-week Massive Open Online Course (MOOC) titled “Generative Artificial Intelligence Tools for University Teaching.” They utilized ChatGPT, DALL·E, and Gamma to produce educational materials without direct input from subject-matter experts. Data collection included semi-structured interviews, non-participant observation, and analysis of student-created artifacts. The findings revealed increased learner autonomy, creativity, and digital skills, along with more efficient instructional design processes supported by prompt engineering and real-time feedback. The structured 4PADAFE framework helped participants align AI-generated content with specific learning outcomes while maintaining ethical safeguards. This study concludes that, with proper guidance and a systematic framework, students with technical backgrounds can serve as effective instructional designers, demonstrating the potential of combining structured methodologies and GAI to democratize high-quality course development in digital higher education. Full article

Rapid developments in artificial intelligence (AI) have prompted increasing attention to human–AI collaboration across various fields. This study focuses on the architectural design process and examines collaboration with generative AI (GenAI) within the context of architectural education. Creative cognition in design-based learning and the process of collaboration with AI are crucial. Insights on AI usage and design process perceptions are gathered from semi-structured interviews of architecture students. The data were analyzed using primarily inductive thematic analysis to understand their experiences in architectural design education. It aims to construct a conceptual framework to interpret the creative cognition during human–AI collaboration in the architectural design process through algorithmic thinking strategies and existing theories. The literature review acted as the foundation for the theoretical background, adopting existing models and theoretical perspectives to support the conceptual framework generation. The study contributes to human–AI collaboration in architectural design contexts. Additionally, the conceptual framework proposal derived from the empirical insights and relevant literature can serve as the basis for conducting further explorations of a potential model in architectural design education. Full article

As generative artificial intelligence (AI) technologies become increasingly present in creative and professional domains, examining public discourse surrounding these tools is important for understanding their broader social implications. This study conducts a two-part analysis of the initial public reaction to Sora, the generative video model developed by OpenAI, by analyzing 23,543 English-language comments posted on YouTube between February and April 2024. Rather than relying on traditional positive–negative sentiment classifications, this study integrates fine-grained emotion detection with topic modeling to examine the relationship between emotions and topics in the discourse. Based on the residual analysis, the overall association between topics and emotions was weak; however, certain topics were associated with specific emotions. For instance, ethical discussions were more likely to be associated with sadness and anger, artistic settings were associated with fear, and benchmark discussions were associated with joy. Methodologically, this study utilizes an emotion–topic coupling through residual deviation with a hierarchical LDA-BERTopic approach, bringing together computational modeling and theories of emotion. This study provides a structured and theory-based way to explore the affective and thematic patterns in the public’s discourse surrounding Sora. Full article

Artificial intelligence (AI) is rapidly reshaping firms’ production and organisational processes, yet whether it can serve as a driving force for corporate green transformation remains an open question. Using a sample of Chinese listed manufacturing firms from 2012 to 2023, this study systematically examines the relationship between AI and firms’ green total factor productivity (GTFP), and explores potential underlying mechanisms. At the theoretical level, drawing on the task-driven nature of AI as a form of technological innovation, this study proposes that AI may enhance GTFP through two channels, namely the structural labour reallocation effect and the managerial dissipation reduction effect. The empirical results show the following: (1) Firms’ AI technical level is significantly associated with improvements in GTFP. (2) Mechanism tests indicate that AI is significantly related to an increasing share of creative task employees and a declining share of structural task employees, thereby providing empirical evidence for the structural labour reallocation effect. Moreover, from four dimensions, including information dissipation, resource allocation dissipation, process coordination dissipation, and incentive and learning dissipation, this study provides supportive evidence that AI is linked to reduced managerial dissipation. (3) Heterogeneity analysis suggests that this association is more pronounced among firms with greater scope for green improvement, such as non-heavily polluting firms and those characterised by managerial myopia. Overall, this study deepens the understanding of the relationship between AI and GTFP from the perspectives of labour structure and corporate organisation, and emphasises that AI’s contribution to firms’ GTFP is more likely to arise as a systemic facilitation embedded in production and organisational processes, rather than through the direct substitution of specialised green technologies. Full article

Precision agriculture employs state-of-the-art technologies to improve the economic viability, sustainability, and efficiency of agricultural practices. This paper offers a thorough review of precision agriculture, with an emphasis on real-time systems as a foundation for understanding the integration and impact of major technologies. We examine technologies such as digital twins, mobile applications, autonomous systems, location-aware technologies, edge computing, and Wireless Sensor Networks (WSN) that are revolutionizing agricultural processes. We also discuss the potential of other sensing techniques to enhance precision farming, including image analysis, sensory and chemical analysis, and physical state detection. Additionally, the roles that data transmission protocols, artificial intelligence (AI), and machine learning play in maximizing real-time data processing and decision-making are examined. We emphasize the main challenges and limitations in precision agriculture, such as data interoperability, scalability, and system integration. With a focus on market trends and local issues, we examine how AI, real-time systems, sensor technologies, and financial constraints impact the growth of precision agriculture. These advancements have an impact on precise monitoring, post-harvest management, and human health. Lastly, we provide suggestions for successful integration and future developments in precision agriculture, emphasizing design, engineering, and creative approaches to assist the field’s ongoing development. Full article

Generative Artificial Intelligence (AI) has transitioned from a research milestone to a general-purpose technology with wide-ranging implications for organizations, labor markets, and information systems. Thanks to improvements in deep learning, generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, transformer-based language models, and reinforcement learning from human feedback (RLHF), generative AI can now create high-quality text, images, audio, code, and other types of content. This review synthesizes the core technical foundations and best practices for training, evaluation, and governance, with an emphasis on scalability and human oversight. The paper examines applications across customer service, marketing, software development, healthcare, finance, law, logistics, and the creative industries, and assesses the labor implications of generative AI using a sociotechnical lens. This study also develops a disruption index that integrates task exposure, adoption rates, time savings, and skill complementarity. The paper concludes with actionable recommendations for policymakers, organizations, and workers, emphasizing the importance of reskilling, algorithmic transparency, and inclusive innovation. Taken together, these contributions situate generative AI within broader debates about automation, augmentation, and the future of work. Full article

The increasing integration of artificial intelligence (AI) in education highlights the need for teacher preparation programs to support pre-service teachers in developing pedagogically grounded and ethically responsible AI competencies. This study designed and preliminarily examined an Experiential Design Learning model within a Digital Learning Ecosystem (EDL–DLE) to support the development of AI competencies and instructional innovation in pre-service science teacher education. A four-phase research and development framework was employed, including conceptual synthesis, model design and expert validation, implementation, and evaluation. Participants were 19 second-year pre-service science teachers from a university in Bangkok. Research instruments included a 40-item AI competency assessment and an instructional innovation evaluation rubric. Paired-sample t-test results indicated statistically significant pre–post difference across all AI competency dimensions, with large effect sizes (Cohen’s d = 0.82–1.59), reflecting notable within-group changes observed within the EDL–DLE learning context. The instructional innovation lesson plans were evaluated as generally strong across multiple dimensions, particularly in learner-centered pedagogy, creativity, and collaboration, while relatively lower performance was observed in appropriate AI technology selection and ethical use. Overall, the findings provide preliminary evidence supporting the feasibility of the EDL–DLE model as an exploratory instructional approach for fostering foundational AI-related pedagogical competencies in pre-service science teacher education. Full article

The use of generative artificial intelligence (AI) is rapidly transforming video game development, enabling the creation of dynamic dialogues, procedural environments, and customized in-game experiences. However, this paradigm shift also introduces significant cybersecurity challenges. This paper explores the integration of generative AI in game design and the associated risks, including prompt injection, generation of harmful content, and potential data leakage. We propose a secure generative framework for video games that incorporates input sanitization, content moderation, and sandboxed model execution to mitigate these threats. Our methodology aims to balance creativity and security, enabling safe deployment of generative systems in modern game environments. Full article