artificial intelligence

Architectural Design: Crafting Ideas, Shaping Place Principles, Methods, and Contemporary Design Strategies part 3.

This part of the book examines the intellectual, methodological, and technological transformations that have reshaped contemporary architectural design through three interconnected themes: contemporary architecture and its environmental and digital directions; design evaluation as a transition from impression-based judgment to evidence-informed decision-making; and the integrated architectural project methodology as a framework that brings together knowledge, analysis, generation, evaluation, and implementation within a coherent design process. The book adopts the view that architecture is no longer an isolated formal activity but a multidimensional process in which environmental, economic, social, cultural, functional, and technological considerations interact continuously. The eight chapter addresses the main features of contemporary architecture, with particular emphasis on sustainability, environmental and climatic design, digital architecture, computational and parametric design, and the growing role of artificial intelligence and generative design in exploring alternatives, analyzing performance, and improving design decisions. It argues that digital tools do not replace architectural expertise or critical judgment; rather, they enhance both when applied within a framework that remains attentive to human experience, cultural context, data quality, and limitations of algorithms. The second chapter examines design evaluation as an integral component of the design process rather than as a subsequent and isolated procedure. It discusses the functional, formal, spatial, environmental, economic, and structural dimensions of evaluation, together with digital simulation, multi-criteria assessment, and post-occupancy evaluation. This chapter demonstrates that effective evaluation is not limited to measuring performance. Its primary value lies in transforming analytical results into applicable knowledge that supports the development of alternatives, improves project quality, reduces risks, and strengthens the feasibility. The ten chapter presents an integrated methodology for architectural projects. The methodology begins by defining the problem and objectives, collecting data, and analyzing the context and users. It then proceeds to formulate the vision, concept, and design strategy; generate alternatives; organize functions and spaces; develop massing and form; integrate environmental considerations and digital technologies; test performance; and finally develop, document, present, and defend the design decisions. The methodology emphasizes the iterative nature of design, in which the architect moves between different stages in response to evaluation findings and emerging information, thereby achieving genuine integration among understanding, generation, formal development, evaluation, documentation and communication. This section concludes that the quality of contemporary architectural design depends on its ability to balance creativity with methodological rigor, technology with human needs, environmental responsiveness with spatial identity, and the rapid generation of alternatives with the accurate verification of their performance. Accordingly, an integrated architectural project should not be understood merely as a final product but as a conscious, evidence-informed, and continuously developing professional practice that respects the uniqueness and context of every project. Keywords: contemporary architectural design, sustainability, environmental and climatic design, digital architecture, generative design, artificial intelligence, design evaluation, digital simulation, multi-criteria assessment, integrated architectural project.

Karakteristik Guru Profesional Yang Tidak Tergantikan Oleh Artificial Intelligence

Perkembangan pesat teknologi kecerdasan buatan (Artificial Intelligence/AI) memberikan dampak besar dalam dunia pendidikan melalui penyediaan materi adaptif, efisiensi informasi, dan evaluasi pembelajaran otomatis. Namun, kemajuan ini memunculkan kekhawatiran terkait potensi pengikisan peran pendidik serta batas kemampuan teknologi dalam menggantikan fungsi mendasar guru, khususnya pada aspek pembentukan karakter, moral, dan nilai kemanusiaan peserta didik. Penelitian ini bertujuan untuk mengidentifikasi serta menganalisis karakteristik utama guru profesional yang tidak dapat digantikan oleh keberadaan AI. Metodologi yang digunakan adalah pendekatan kualitatif dengan jenis studi pustaka (library research) dan teknik analisis isi (content analysis) terhadap 16 sumber literatur berupa buku dan artikel ilmiah bereputasi terbitan tahun 2016–2026 yang relevan dengan profesionalisme guru, pembentukan karakter, dan teknologi AI. Hasil sintesis menemukan enam karakteristik utama guru profesional yang tidak tergantikan oleh AI, yaitu empati dan kepedulian terhadap peserta didik, keteladanan moral dan karakter, kemampuan membangun hubungan sosial, kreativitas dan inovasi pembelajaran, kemampuan menanamkan nilai dan budaya, serta kemampuan membimbing dan memotivasi peserta didik. Keterbatasan utama AI terletak pada ketiadaan pengalaman emosional, ikatan sosial timbal balik, pemahaman konteks kebudayaan yang mendalam, dan keteladanan nyata. Penelitian ini menyimpulkan bahwa AI tidak dapat menggantikan peran sentral guru profesional karena pendidikan tidak hanya berfokus pada efisiensi pemrosesan data, melainkan melibatkan dimensi relasional, moral, sosial, dan perkembangan manusia. AI sejatinya berfungsi sebagai teknologi pendukung untuk membantu tugas teknis-informasional. Sebagai rekomendasi, guru disarankan untuk terus mengasah literasi AI secara kritis dan etis guna mempermudah administrasi pembelajaran, sembari memperkuat kompetensi pedagogik, sosial, dan kepribadian. Selain itu, lembaga pendidikan dan pembuat kebijakan perlu memfasilitasi pelatihan integrasi teknologi yang berorientasi pada etika serta penguatan karakter pendidik.

Artificial Intelligence and tourism in APEC economies

The Impact of Artificial Intelligence (AI) in Content Personalization on Customer Experience and Purchase Intention among Generation Z Users of Shopee Indonesia: A Study of Aerostreet Footwear Consumers

The advancement of digital technologies and the adoption of artificial intelligence (AI) on e-commerce platforms have transformed consumer–business interactions through increasingly personalized content delivery. However, the effectiveness of AI-driven personalization in enhancing customer experience and stimulating purchase intention among Generation Z e-commerce users in Indonesia remains insufficiently understood. This study examines the influence of AI-based content personalization on customer experience and purchase intention and investigates the mediating role of customer experience in this relationship. A quantitative approach with a causal-associative research design was employed. Data were collected through questionnaires administered to 384 Generation Z users of Shopee Indonesia who had interacted with Aerostreet footwear products and were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that AI-based content personalization positively influences both customer experience and purchase intention. Customer experience also positively influences purchase intention and mediates the relationship between AI-based content personalization and purchase intention. These findings demonstrate that customer experience represents an important mechanism through which AI-driven personalization shapes consumers’ purchase intentions. The study contributes to the digital marketing literature by clarifying the relationship among AI-based content personalization, customer experience, and purchase intention in the context of Generation Z consumers on an Indonesian e-commerce platform. Practically, the findings highlight the importance of relevant product recommendations and customer experience optimization in developing effective AI-driven digital marketing strategies.

EL-RAKHAWI DOCTRINE: Quantum Jurisprudence and the Architecture of Legal Entanglement From Binary Law to Probabilistic Justice in the Age of Complexity, Artificial Intelligence, and Networked Reality

EL-RAKHAWI DOCTRINE: Quantum Jurisprudence and the Architecture of Legal Entanglement From Binary Law to Probabilistic Justice in the Age of Complexity, Artificial Intelligence, and Networked Reality

Artificial intelligence in cellular senescence research: a systematic review assessing methodological quality and reporting standards using PROBAST + AI and TRIPOD + AI

Cellular senescence is a fundamental mechanism of biological ageing that has emerged as a critical target for therapeutic intervention in age related diseases. The coalesce of artificial intelligence and senescence research provides unprecedented opportunities in advancing our knowledge and treatment approaches. This systematic review study addresses the gap across diverse AI model and the heterogeneity in senescence, by conducting the extensive evaluation of the performance outcomes and methodological rigor of AI models using Prediction model Risk of Bias Assessment Tool + Artificial Intelligence (PROBAST + AI) and reporting completeness using Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis + Artificial Intelligence extension (TRIPOD + AI), providing the insights into AI models robustness and generalizability. For the articles, released between 2019 to 2025, across major databases 18 eligible studies was used for review, following PRISMA guideline. Quality and applicability were assessed by PROBAST + AI (4 domain) and reporting via TRIPOD + AI (27 items). The quantitative synthesis indicates that deep learning architectures, especially Convolutional Neural Networks (CNNs), are dominant which appeared in about 50% of the studies. These CNNs consistently outperform traditional machine learning methods in the analysis of morphological heterogeneity. While reported performance metrics were high, with accuracy ranging from 83.55% to 99.79%, the PROBAST + AI assessment indicates a high risk of bias in 83.33% (15/18) of studies, primarily driven by Analysis domain due to improper data splitting (data leakage) and lack of external validation. As well as adherence to TRIPOD + AI reporting standards was suboptimal with average of 62% ‘YES’; notably, with the major gap in 0% of studies pre-registered a protocol and only 44.4% made analytical code publicly available, severely limiting reproducibility. Evidently AI demonstrates immense potential to accelerate biomarker discovery and senolytic drug screening, particularly through label-free morphological analysis by DL models, despite of high quality concern and poor reproducibility limit reliability; also standardization, shared benchmarks, multi-omics integration, and explainable AI are essential concerns for clinical translation in aging research.

EL-RAKHAWI DOCTRINE: Quantum Jurisprudence and the Architecture of Legal Entanglement From Binary Law to Probabilistic Justice in the Age of Complexity, Artificial Intelligence, and Networked Reality

EL-RAKHAWI DOCTRINE: Quantum Jurisprudence and the Architecture of Legal Entanglement From Binary Law to Probabilistic Justice in the Age of Complexity, Artificial Intelligence, and Networked Reality

Empathy and Artificial Intelligence

As artificial intelligence chatbots offer increasingly sophisticated emotional support, society faces a profound question: can a machine truly empathize? Empathy and Artificial Intelligence provides the first comprehensive roadmap for this pivotal moment. Moving beyond simple binaries of 'hype' or 'doom,' this interdisciplinary volume unites leading psychologists, philosophers, and engineers to explore the tangled web of synthetic care. Key chapters investigate the 'AI Advantage' – where machines often outperform humans in perceived empathy – alongside the 'AI Penalty,' where discovering the artifice corrodes trust. The text navigates the distinct landscapes of text-based LLMs and embodied robots, addressing urgent ethical dilemmas and exploring whether reliance on AI risks the atrophy of our moral capacities or enables synthetic agents to scaffold stronger human relationships. Essential for researchers, students, and curious observers, this book investigates whether outsourcing our emotional labor saves us time, or costs us our humanity.

Practical Capacities, Empathy, and Human-Centered Artificial Intelligence

This chapter explores some conceptual connections between human-centered artificial intelligence (HCAI) research and empathic AI. First, I argue that HCAI is best understood as a framework that centers human well-being in AI development and deployment. Second, I argue a fruitful way to make the framework more precise is by focusing on human practical capacities. I use empathy as a case study, examining recent positive accounts of empathetic AI from the perspective of a well-being-focused HCAI. Finally, I note how this approach relates to recent calls for a sociotechnical perspective on AI.

Research Bulletin 2026, Vol 3, Issue 1

“Technology is not a distant vision—it is the reality we are shaping today.” The Research Bulletin 2026 (Vol. 3, Issue 1) from the Department of Computer Application, Integral University presents the department’s dynamic engagement with cutting‑edge research and innovation through the work of its faculty members and research scholars. Key Highlights: Artificial Intelligence & Machine Learning Advances in intelligent algorithms, predictive models, and adaptive systems designed to solve complex challenges across industries. Cybersecurity & Digital Resilience Innovative frameworks and defense mechanisms ensuring trust, privacy, and security in the digital era. Healthcare Analytics & Smart Applications AI‑enabled solutions and intelligent platforms transforming diagnostics, patient care, and healthcare decision‑making. IoT & Emerging Technologies Research driving smart agriculture, connected systems, and next‑generation computing paradigms for sustainable growth. Books, Patents & Collaborations Showcasing scholarly publications, intellectual property, and collaborative initiatives that reinforce the department’s commitment to impactful knowledge creation.

KI - Quo vadis? Von ChatGPT über neuronale Netze zu neuromorpher KI

Praktisch nutzbare Künstliche Intelligenz entstand aus dem Bündnis kluger Köpfe mit den rechentechnischen Fähigkeiten Boolescher Automaten – natürliche Intelligenz dagegen aus Selbstorganisation und Evolution. Dieser zweite Weg rückt mit dem neuromorphen Rechnen zunehmend wieder in das Blickfeld von Wissenschaft und Praxis: Spikende neuronale Netze (SNN) stellen dem wachsenden Energiehunger moderner KI-Systeme eine reale Alternative entgegen und erweitern das Methodenrepertoire der KI um Elemente der Selbstorganisation. Das vorliegende Werk erläutert dieses Spannungsfeld allgemeinverständlich und zugleich mathematisch nachvollziehbar. Am Beispiel des Texterzeugungsmoduls von ChatGPT werden Konzept, Eleganz und Aufwand der modernen Transformer-basierten KI Schritt für Schritt sichtbar gemacht und mit den Kodierungs-, Lern- und Netzgestaltungsmethoden spikender neuronaler Netze verglichen. Den Hintergrund bilden ausgewählte Wirkungsmechanismen des menschlichen Nervensystems – die kybernetischen Vorbilder des neuromorphen Rechnens. Behandelt werden u. a. Spike-Timing-Dependent Plasticity, Populations- und Zeitkodierung, das Neural Engineering Framework, neuromorphe Hardware von Loihi 2 bis zu kommerziellen Edge-Prozessoren sowie die Perspektiven der wechselseitigen Befruchtung klassischer und neuromorpher KI – bis hin zum optischen Rechnen. (ca. 90 Seiten, 20 Abbildungen) English abstract: Practically usable artificial intelligence arose from the alliance of ingenious minds with the computational power of Boolean automata – natural intelligence, by contrast, from self-organization and evolution. With neuromorphic computing, this second path is moving back into the focus of science and engineering: spiking neural networks (SNN) offer a real alternative to the growing energy appetite of modern AI systems and fundamentally extend the methodological repertoire of AI by elements of self-organization. This monograph (in German) explains this field of tension in a generally accessible yet mathematically traceable way. Using the text-generation module of ChatGPT as a representative example, the concept, elegance and computational cost of modern transformer-based AI are made visible step by step and compared with the coding, learning and network-design methods of spiking neural networks. The background is provided by selected mechanisms of the human nervous system – the cybernetic archetypes of neuromorphic computing. Topics include spike-timing-dependent plasticity, population and temporal coding, the Neural Engineering Framework, neuromorphic hardware from Loihi 2 to commercial edge processors, and the prospects of mutual enrichment of classical and neuromorphic AI – up to optical computing. (approx. 90 pages, 20 figures)

An explainable AI framework for crack width and crack spacing prediction with serviceability-oriented design optimization of reinforced and prestressed concrete members

Reliable prediction of cracking behavior is essential for the serviceability assessment of reinforced and prestressed concrete members, where crack development depends on interacting material, geometric, reinforcement, prestressing, and loading parameters. This study presents an explainable and uncertainty-aware artificial-intelligence framework for predicting crack width and mean crack spacing while supporting serviceability-oriented reinforcement-detailing optimization. A quality-controlled experimental database comprising 19,863 observations from 30 independent experimental programs was transformed into physics-informed engineering features. Five ensemble-learning algorithms were evaluated using five-fold GroupKFold cross-validation to reduce information leakage between experimental programs. Model interpretation, predictive uncertainty, robustness assessment, and multi-objective optimization were incorporated using SHAP, split conformal prediction, Monte Carlo simulation, and NSGA-II, respectively. The optimized LightGBM and CatBoost models achieved R2 values of 0.808 and 0.672 for crack-width and mean crack-spacing prediction, respectively. SHAP analysis identified reinforcement stress, normalized bending demand, reinforcement ratio, bending moment, and the stress-to-yield-strength ratio among the most influential predictors. Split conformal prediction achieved empirical coverage probabilities of 96.79% and 98.07%, while Monte Carlo simulation indicated that 6.96% of realizations exceeded the 0.30 mm crack-width limit under the investigated perturbation scenario. The representative optimization case reduced the predicted crack width and mean crack spacing by 33.63% and 37.80%, respectively, while increasing reinforcement demand by 46.72%. Within the scope of the compiled experimental database and the adopted validation framework, the proposed framework provides an interpretable and uncertainty-aware engineering decision-support tool for serviceability assessment and reinforcement-detailing optimization. Independent validation using external experimental datasets would further strengthen confidence in its broader engineering application.