Search Results (2,052)

The growing number of wearable electrocardiogram (ECG) users today, combined with the surge of artificial intelligence (AI) and machine learning (ML) in medical signal-processing, has led to a new age of wearable-enabled monitoring for cardiac conditions. With the development of advanced processing methods, wearables offer the opportunity to monitor and predict the probability of various cardiac conditions, from cardiac ischemia to arrhythmias, by collecting personalized data from the comfort of a user’s home. Although such technology has not yet entered the market, AI and ML research training specifically on wearable-based ECG data has grown significantly in the last decade. Despite this growing niche, there are few current articles reviewing the applications of these techniques in wearable ECG technology. To fill this gap, this article first primes the reader to the practical tools required to build models from ambulatory ECG, synthesizes the state of the field across major cardiac condition use-cases, and finally highlights recurring limitations in the current literature and outlines the need to improve reliability if this technology were to be widely utilized. As a result, we aim to help readers who otherwise may be unfamiliar with the specifics of these tools and their applications to form an interpretation of the current capabilities of AI/ML in wearable ECGs and identify key steps required for improvement based on the most current research. Full article

Research into conversationally explainable artificial intelligence (CXAI) aims to emulate the interactive and co-constructive nature of explanations. From the perspective of human-centredness, previous work has shown that AI users prefer conversational explanations over static ones. Various approaches for modelling and implementing CXAI solutions have also been proposed. However, as for concrete dialogue capabilities possessed by such systems, previous approaches have not been properly grounded in analogous dialogue patterns in human–human interaction. The present study bridges this gap in previous work by experimentally collecting human dialogues revolving around AI predictions concerning personality estimation. By distilling the collected interactions into the kind of interactions that would occur if the explainer was a dialogue system, the study identifies dialogue strategies which might be important for CXAI to support. The study reveals that some of the observed strategies—explaining predictions with reference to general rules or patterns and signalling presupposition violations in questions raised by explainees—have received very limited attention in previous work on CXAI. Overall, the study contributes a methodology for empirically identifying CXAI desiderata in human dialogues as well as concrete results with implications for future work. Full article

Cardiometabolic diseases, including hypertension, type 2 diabetes, dyslipidemia, and obesity, along with their cardiovascular complications, remain leading causes of morbidity and mortality worldwide, imposing significant public health, economic, and societal burdens. Conventional pharmacological therapies often show limited efficacy and increased adverse effects because they do not account for the body’s intrinsic circadian rhythms, which regulate organ function, drug absorption, and metabolism. Chronopharmacology, which aligns treatment timing with these biological rhythms, offers a strategy to enhance therapeutic outcomes. This review presents a comprehensive analysis of chronopharmacology principles applied to cardiometabolic disease management, integrating molecular, physiological, and clinical perspectives. It examines how core clock genes and tissue-specific circadian patterns influence drug action and absorption and summarizes evidence-based time-optimized interventions for hypertension, diabetes, dyslipidemia, obesity, and multimorbid patients. Furthermore, the review highlights emerging innovations, including artificial intelligence-guided dosing, circadian-biomarker-informed therapy selection, and wearable digital devices for real-time monitoring of biological rhythms. By synthesizing mechanistic and clinical insights, circadian-aligned treatment strategies are shown to improve drug efficacy, reduce adverse effects, and support the development of precision, rhythm-based therapeutics, offering a practical framework for personalized cardiometabolic disease care. Full article

As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI systems can generate fluent responses or accurate predictions, yet they often fail to clearly explain their decisions, reflect students’ cultural contexts, or give learners and educators meaningful control. This gap can reduce trust and limit the educational value of AI-supported learning. This paper introduces the PEARL framework, a human-centered approach for designing and evaluating explainable AI in education. PEARL is built around five core principles: Pedagogical Personalization (adapting support to learners’ levels and curriculum goals), Explainability and Engagement (providing clear, motivating explanations in everyday language), Attribution and Accountability (making AI decisions traceable and justifiable), Representation and Reflection (supporting fairness, diversity, and learner self-reflection), and Localized Learner Agency (giving learners control over how AI explains and supports them). Unlike many existing explainability approaches that focus mainly on technical performance, PEARL emphasizes how students, teachers, and administrators experience and make sense of AI decisions. The framework is demonstrated through simulated examples using an AI-based tutoring system, showing how PEARL can improve feedback clarity, support different stakeholder needs, reduce bias, and promote culturally relevant learning. The paper also introduces the PEARL Composite Score, a practical evaluation tool that helps assess how well educational AI systems align with ethical, pedagogical, and human-centered principles. This study includes a small exploratory mixed-methods user study (N = 17) evaluating example AI tutor interactions; no live classroom deployment was conducted. Together, these contributions offer a practical roadmap for building educational AI systems that are not only effective, but also trustworthy, inclusive, and genuinely supportive of human learning. Full article

The integration of Generative AI (GAI) in education has opened new possibilities for personalized learning, yet its effectiveness in mitigating learning gaps remains underexplored. This study examines the impact of Personalized Learning Pathways (PLPs), generated through AI models (Gemini 2.5 Pro, ChatGPT 5), on secondary school students’ learning outcomes. Using a short-term longitudinal panel design, the research compares homogeneous instructional strategies with AI-driven personalized learning to assess differences in knowledge acquisition and cognitive skill development. Findings indicate that AI-generated PLPs significantly reduce lower-order learning gaps, though higher-order skills remain challenging. The study also reveals that learning styles influence student engagement with AI-driven education, suggesting that hybrid models combining AI and teacher mediation may optimize outcomes. These findings contribute to the ongoing discourse on AI in education, emphasizing the need for equitable, adaptive, and ethical AI applications in learning environments. Full article

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition. Full article

Over the past decades, remarkable progress in multimodal therapy has significantly improved survival outcomes for children with cancer. Yet, considerable variability in treatment response and toxicity persists, often driven by underlying genetic differences that affect the pharmacokinetics and pharmacodynamics of anticancer drugs. Pharmacogenomics, the study of genetic determinants of drug response, offers a powerful approach to personalize pediatric cancer therapy by optimizing efficacy while minimizing adverse effects. This review synthesizes current evidence on key pharmacogenetic variants influencing the response to major classes of antineoplastic agents used in children, including thiopurines, methotrexate, anthracyclines, alkylating agents, vinca alkaloids, and platinum compounds. Established gene–drug associations such as TPMT, NUDT15, DPYD, SLC28A3, and RARG are discussed alongside emerging biomarkers identified through genome-wide and multi-omics studies. The review also examines the major challenges that impede clinical implementation, including infrastructural limitations, cost constraints, population-specific variability, and ethical considerations. Furthermore, it highlights how integrative multi-omics, systems pharmacology, and artificial intelligence may accelerate the translation of pharmacogenomic data into clinical decision-making. The integration of pharmacogenomic testing into pediatric oncology protocols has the potential to transform cancer care by improving drug safety, enhancing treatment precision, and paving the way toward ethically grounded, personalized therapy for children. Full article

This article proposes an interpretable adaptive control model for dynamically regulating task difficulty in Artificial intelligence (AI)-augmented reading-comprehension learning systems. The model adjusts, on the fly, the level of task complexity associated with reading comprehension and post-text analytical tasks based on learner performance, with the objective of maintaining an optimal difficulty level. Grounded in adaptive control theory and learning theory, the proposed algorithm updates task difficulty according to the deviation between observed learner performance and a predefined target mastery rate, modulated by an adaptivity coefficient. A simulation study involving heterogeneous learner profiles demonstrates stable convergence behavior and a strong positive correlation between task difficulty and learning performance (r = 0.78). The results indicate that the model achieves a balanced trade-off between learner engagement and cognitive load while maintaining low computational complexity, making it suitable for real-time integration into intelligent learning environments. The proposed approach contributes to AI-supported education by offering a transparent, control-theoretic alternative to heuristic difficulty adjustment mechanisms commonly used in e-learning systems. Full article

Food allergy diagnosis remains challenging due to the difficulty of distinguishing true clinical allergy from asymptomatic sensitization. Inaccurate diagnosis may result in unnecessary dietary restrictions, reduced quality of life, or, conversely, failure to identify individuals at risk of severe allergic reactions. This review critically analyzes the efficacy, limitations, and clinical utility of currently available diagnostic tests for food allergy, with particular emphasis on their ability to predict true clinical reactivity. A comprehensive literature review was conducted to evaluate the sensitivity, specificity, and predictive values of both traditional and emerging diagnostic modalities. English-language guidelines, systematic reviews, and key clinical studies published primarily within the past 15 years (up to 2025) were identified through PubMed and Google Scholar. Classic diagnostic tools, including skin prick testing (SPT) and serum-specific IgE (sIgE), were assessed alongside novel approaches such as component-resolved diagnostics (CRD), basophil activation test (BAT), mast cell activation test (MAT), atopy patch testing (APT), cytokine profiling, and omics-based diagnostics. Particular attention was given to how these tests compare with the oral food challenge (OFC), which remains the diagnostic gold standard. The findings demonstrate that while conventional tests offer high sensitivity and are valuable for initial risk assessment, their limited specificity often leads to overdiagnosis. Emerging molecular and cellular assays show improved specificity and functional relevance, especially in complex cases involving polysensitization or unclear clinical histories and may reduce reliance on OFCs in the future. However, accessibility, cost, and lack of standardization currently limit their widespread clinical application. Advances in artificial intelligence and data integration hold promise for improving diagnostic accuracy through enhanced interpretation of complex immunological data. Based on the synthesized evidence, this review proposes an evidence-based, stepwise, and individualized diagnostic algorithm for food allergy. Integrating clinical history, targeted testing, and selective use of OFCs can improve diagnostic certainty, enhance food safety, minimize unnecessary dietary avoidance, and optimize patient outcomes. The review underscores the need for continued research, standardization, and validation of novel diagnostic tools to support personalized and precise food allergy management. Full article

The rapid expansion of artificial intelligence (AI) and digitalization in contemporary education has intensified global debates on sustainable education, frequently framed around efficiency, personalization, and technological innovation. At the same time, these developments have accelerated processes of technologization and commodification, raising concerns about the erosion of educational values and human-centered purposes. This tension calls for a critical reassessment of what sustainability should mean in AI-mediated educational contexts. The objective of this study is to examine under what conditions AI contributes to sustainable education as a value-based and human-centered project, and under what conditions it undermines it. Methodologically, the article adopts a qualitative, value-critical analysis of contemporary scholarly literature and policy-oriented debates, employing the distinction between sustainable education, sustainability in education, and education for sustainable development as a heuristic entry point within a broader theoretical dialogue. The analysis demonstrates that AI does not exert a uniform or inherently progressive influence on education. While AI can enhance access, personalization, and instructional support in ethically grounded and well-governed contexts, it may also intensify educational inequalities, reinforce the commodification of knowledge, weaken academic integrity, and marginalize the formative and human dimensions of education under market-driven and weakly regulated conditions. These dynamics are particularly visible in culturally and religiously grounded educational contexts, where AI reshapes epistemic authority and educational meaning. The study concludes that achieving sustainable education in the digital age depends not on AI adoption per se, but on subordinating AI and digitalization to coherent normative, ethical, and governance frameworks that prioritize educational purpose, social justice, and human dignity. Full article

The integration of artificial intelligence into education has driven growing interest in predicting student learning styles and developing recommendation systems that personalize learning pathways. While previous reviews examined these domains, most focus on pre-2023 research, overlooking recent methodological shifts. We conduct a systematic literature review of 40 studies published between 2017 and 2025, with emphasis on publications from 2023 to 2025 (70% of reviewed studies). Our analysis identifies three qualitative shifts: adoption of ensemble and deep learning methods over single classifiers, emergence of multimodal inputs including physiological signals, and evolution from isolated prediction to integrated adaptive systems. Beyond methodological synthesis, this review critically examines factors underlying observed trends and barriers to deployment. The Felder-Silverman Learning Style Model dominates research (58.3%) due to historical path dependency and instrument availability rather than demonstrated pedagogical superiority. While ensemble methods achieve high reported accuracy (87–98%), methodological concerns emerge: 65% of studies employ random rather than temporal validation, potentially inflating performance, and only 23% address production-level requirements, including privacy, scalability, and integration. We systematically analyze implementation barriers spanning computational requirements, LMS integration, educator acceptance, ethical considerations, and scalability—revealing that the gap between research prototypes and deployable systems remains substantial. Our contributions include a stakeholder impact framework, evaluation metrics taxonomy, critical analysis of reported performance claims, and identification of five research gaps with actionable recommendations. This review offers researchers and practitioners both a comprehensive synthesis of advances and a critical roadmap for bridging the implementation gap in AI-powered personalized education. Full article

Sensitization to non-specific lipid transfer proteins (nsLTPs) is highly prevalent in Mediterranean countries. Pru p 3 from peach is a major allergen responsible for IgE-mediated food allergies. As a panallergen, Pru p 3 shows high sequence homology with nsLTPs from other Rosaceae fruits but also from botanically unrelated sources, including nuts and pollens, leading to extensive cross-reactivity complicating diagnosis and management. Given the worldwide prevalence of peanut and tree nut allergies, this study aimed to investigate sensitization patterns in Pru p 3-sensitized patients with tree nut allergy, using artificial intelligence (AI) to identify predictors of clinical reactivity and severity. Data from Pru p 3–sensitized patients with symptoms to peach and/or nuts were analyzed. Sensitization profiles were modeled using an XGBoost algorithm to explore associations with symptoms and severity. Patients sensitized to Pru p 3 and symptomatic for peach and nuts showed predominant sensitization to peanut and hazelnut, but AI revealed stronger associations between clinical reactivity and sensitization to hazelnut, walnut, and almond. Among patients with nut allergy and peach-asymptomatic, peanut and hazelnut sensitization were most frequent, while peach-symptomatic ones, walnut and almond sensitization predominated. Overall, walnut sensitization emerged as the main predictor of clinical severity and increasing number of sensitizations correlated with higher severity. The XGBoost algorithm identified specific allergen combinations associated with symptoms and severity, highlighting walnut sensitization as the strongest severity predictor. Machine learning approaches represent a promising tool for refining risk stratification and personalizing management in nsLTP-related food allergy. Full article

Artificial intelligence (AI) is transforming customer experience management (CXM) by enabling real-time, data-driven, and personalized interactions across digital touchpoints, including chatbots, voice assistants, generative AI, and immersive platforms. This study presents a PRISMA-based systematic literature review of 59 peer-reviewed studies published between 2021 and 2026, examining how AI-enabled personalization, privacy concerns, and customer value interact within AI-mediated customer experiences. Drawing on the Personalization–Privacy–Value (PPV) framework, the review synthesizes evidence on how AI-driven personalization enhances utilitarian, hedonic, experiential, relational, and emotional value, thereby strengthening satisfaction, engagement, loyalty, and behavioral intentions. At the same time, the findings reveal persistent tensions, as privacy concerns, perceived surveillance, algorithmic bias, and contextual moderators—including generational differences, cultural expectations, and technological literacy—frequently constrain value creation and erode trust. The review highlights that personalization benefits are highly contingent on transparency, perceived control, and ethical alignment, rather than personalization intensity alone. The study contributes by integrating ethical AI considerations into CXM research and clarifying conditions under which AI-enabled personalization leads to value creation versus value destruction. Managerially, the findings underscore the importance of ethical governance, transparent data practices, and customer-centered AI design to sustain trust and long-term customer relationships. Future research should prioritize longitudinal analyses of trust development, demographic heterogeneity, and cross-sector comparisons of AI governance as AI technologies become increasingly embedded in service ecosystems. Full article

With the increasingly prominent demand for building energy efficiency and occupant-centric design, accurate and reliable personal thermal comfort models (PTCMs) are playing an important role in various residential and energy applications (e.g., building energy-saving design, indoor environmental regulation, and health and well-being improvement). In recent years, data-driven and artificial intelligence (AI) technologies have attracted considerable attention in the field of personal thermal comfort modeling. This study systematically reviews recent progress in data-driven personal thermal comfort modeling, emphasizing contact-based and non-contact data collection ways, correlation analysis of feature data, modeling methods based on machine learning and deep learning. Considering the high cost and limited scale of collection experiments, as well as noise, ambiguity, and individual differences in subjective feedback, special attention is put on the data-efficient thermal comfort modeling in data scarcity scenarios using a transfer learning (TL) strategy. Characteristics and suitable occasions of four transfer methods (model-based, instance-based, feature-based, and ensemble methods) are summarized to provide a deep perspective for practical applications. Furthermore, integration of PTCM into building environment control is summarized from aspects of the integration framework, modeling method, control strategy, actuator, and control effect. Performance of the integrated systems is analyzed in terms of improving personal thermal comfort and promoting building energy efficiency. Finally, several challenges faced by PTCMs and future directions are discussed. Full article

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed teaching, learning, and research practices within higher education institutions (HEIs). Although a growing body of literature has examined the application of AI in higher education, existing studies remain fragmented, often focusing on isolated tools or outcomes, with limited synthesis of best practices, core functionalities, and implementation challenges across diverse contexts. To address this gap, this systematic review aims to comprehensively examine the best practices, functionalities, and challenges associated with the integration of AI in HEIs. A comprehensive literature search was conducted across major academic databases, including Google Scholar, Scopus, Taylor & Francis, and Web of Science, resulting in the inclusion of 35 peer-reviewed studies published between 2014 and 2024. The findings suggest that effective AI integration is supported by best practices, including promoting student engagement and interaction, providing language support, facilitating collaborative projects, and fostering creativity and idea generation. Key AI functionalities identified include adaptive learning systems that personalize educational experiences, predictive analytics for identifying at-risk students, and automated grading tools that improve assessment efficiency and accuracy. Despite these benefits, significant challenges persist, including limited knowledge and skills, ethical concerns, inadequate infrastructure, insufficient institutional and management support, data privacy risks, inequitable access to technology, and the absence of standardized evaluation metrics. This review provides evidence-based insights to inform educators, institutional leaders, and policymakers on strategies for leveraging AI to enhance teaching, learning, and research in higher education. Full article