artificial intelligence

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

Abstract 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 R 2 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.

‘Dream a little before you think’ : an open invitation to reconsider human agency and the purpose of arts and humanities higher education through the labour of radical imagination

Following the long inertia of Enlightenment influence and decades of neoliberal propaganda, generative AI is hailed as the most recent revolutionary force that is prophesied to redefine not only what education and knowledge production mean, but also what human civilization could potentially look like. This thesis critically examines the ideological narratives and forces that propel the “inevitability” of deploying generative artificial intelligence in higher education. Building on Theodor Adorno and Max Horkheimer’s foundational critique of technological progress, Walter Benjamin’s examination of historical progress, and Hannah Arendt’s analysis of human agency, it challenges the linear, technologically solutionist vision of social and human progress. Inspired by Toni Morrison’s instruction on the purpose of humanities education and Ruha Benjamin’s elaboration on technological justice through the framework of radical imagination, this thesis argues that to reveal alternative educational futures – ones that aren’t grounded in massive ecological, economic, cognitive, social, and cultural dispossessions – will require a revolution of imagination.

What are we teaching the machine? : toward responsible AI and rigorous data science for farm animal welfare

Technology is increasingly integrated into dairy cattle management to monitor health and productivity. The rise of artificial intelligence (AI) has led to a plethora of claims that AI could improve animal welfare, yet much of this discussion lacks critical reflection. The overall aim of my thesis was to critically evaluate how data are collected, analysed and interpreted to understand animal welfare and how are farm animals portrayed in general-purpose AI. I addressed four overarching questions: 1) Are we interpreting existing data streams in biologically meaningful ways? 2) Are the methods used to analyse these data reproducible and reliable? 3) Do we truly understand what “ground truth” means when training machines to detect health problems? 4) As generative AI becomes mainstream, how is it shaping public perception of livestock farming? In Chapter 2, I challenged the common practice of using agonistic interactions recorded right after fresh feed delivery to calculate dominance hierarchies in indoor-housed dairy cows, showing that these interactions likely reflect motivation to access fresh feed rather than dominance per se. In Chapter 3, I developed moo4feed, an open-source R package for reliably extracting biologically meaningful behavioural variables, including agonistic interactions, non-nutritive visits, and feeding strategies, from electronic feeder and drinker data, revealing feeding strategies among different individuals. In Chapters 4 and 5, I challenged the reliability of traditional lameness scoring and proposed a new approach: asking untrained observers to judge which cow is ‘more lame’ while watching two cows walk side by side. Even untrained observers could do this reliably, and these pairwise comparisons were used to rank cows from healthy (not lame) to most lame. In Chapter 6, I found that, despite being aware of the realities of modern livestock farming, generative AI uses an internal mechanism called ‘prompt revision’ to silence that reality, reflecting the pastoral ideal that dairy cows graze on pasture and pigs root happily in mud. Together, these findings demonstrate the importance of critically examining how animal data are collected, analysed, and interpreted, and reveal how generative AI encodes implicit assumptions about how animals are raised and understood.