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        <title>Frontiers in Computer Science | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/computer-science</link>
        <description>RSS Feed for Frontiers in Computer Science | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-26T08:39:42.677+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1758333</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1758333</link>
        <title><![CDATA[Node-Sampling: adaptive multi-agent optimization in medical education]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lilly Marie Düsterbeck</author><author>Michael Größler</author><author>Graziella Credidio</author><author>Louis Bellmann</author><author>Layla Tabea Riemann</author>
        <description><![CDATA[IntroductionDifferences in prior knowledge among incoming medical students pose a persistent challenge for universities. To promote more individualized and equitable preparation, a large language model-based learning platform is being developed at the University Medical Center Hamburg-Eppendorf. A central component of this platform is the automated generation of multiple-choice questions (MCQs) from curated medical materials. However, ensuring their educational quality remains difficult, particularly when relying on smaller, locally deployed language models.MethodsThis study introduces Node-Sampling, a self-optimizing multi-agent approach for improving MCQ quality. The method identifies efficient refinement strategies by modeling agents as an adaptive sequence optimized through the REINFORCE algorithm.ResultsExpert evaluations showed that Node-Sampling enhances the quality of question stems significantly compared to a fixed baseline. Importantly, Node-Sampling achieved this performance using an effective three-agent configuration, requiring only 33% of the original resources. Results for answer options were less consistent.DiscussionThe results highlight the potential of adaptive multi-agent optimization to strengthen automated question refinement. Node-Sampling therefore presents a sustainable and promising approach to better MCQ quality and supports more effective and personalized preparation for medical students.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1845840</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1845840</link>
        <title><![CDATA[Editorial: Software specification and verification: models and tools]]></title>
        <pubdate>2026-04-22T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Vincenzo Arceri</author><author>Nabendu Chaki</author><author>Agostino Cortesi</author><author>Novarun Deb</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1772813</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1772813</link>
        <title><![CDATA[Trust rises, attention falls: divergent effects of exposure and education in driving automation]]></title>
        <pubdate>2026-04-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hanna Chouchane</author><author>Yuki Sakamura</author><author>Kenji Sato</author><author>Genya Abe</author><author>Makoto Itoh</author>
        <description><![CDATA[IntroductionDrivers supervising Level 2 automation must maintain situation awareness while the system controls steering and speed. Miscalibrated trust can contribute to overreliance and lapses in monitoring, whereas insufficient trust leads to disuse. Prolonged supervision is associated with increased mind-wandering, which can slow reactions to critical events. This study tested whether brief educational interventions affect trust, attention, and takeover readiness during Level 2 driving. Our focus on brief interventions reflects the short, time-constrained onboarding that drivers typically receive when adopting driving automation systems.MethodsFifty-five licensed drivers with no prior hands-on experience of Level 2 automation completed a 15-min automated highway drive. Participants received either minimal instruction (Basic), capability-focused education (Knowledge-based), or limitation-focused education (Rule-based). Trust was measured at four time points; additional measures captured self-reported mind-wandering, gaze behavior, and takeover reaction time.ResultsTrust increased significantly over time in all groups, and educational framing did not alter this trajectory. Capability-focused education enhanced monitoring of the human-machine interface on two false discovery rate corrected metrics and produced faster takeover reactions than limitation-focused education (no difference vs. Basic). Across participants, greater trust growth correlated with higher mind-wandering, while more structured gaze was associated with lower mind-wandering.DiscussionOverall, trust formation appeared to be primarily associated with direct experience with system performance, whereas targeted education refined what drivers monitored and how quickly they responded. Together, these results clarify how experience primarily builds trust while education selectively sharpens attention and response readiness in automated driving. These findings clarify distinct roles of experience and brief education in supervising automation and have implications for driver training, human-machine interface design, and gaze-based monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1770049</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1770049</link>
        <title><![CDATA[Redefining learning strategies in SRL for student’s achievements in flipped classrooms]]></title>
        <pubdate>2026-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Monica Maiti</author><author>M. Priyaadharshini</author>
        <description><![CDATA[Flipped classrooms require learners to actively regulate their learning processes, yet the relationships among self-regulated learning (SRL), engagement, social–emotional intelligence (SEI), and academic performance remain insufficiently integrated within learning analytics research. This study examined 96 third-year Computer Science and Engineering students enrolled in a semester-long Database Management Systems course implementing a flipped SRL framework. A mixed-methods approach was used to analyse academic performance across Continuous Assessment Tests 1 and 2 (CAT 1, CAT 2) and Final Assessment Test (FAT), engagement analytics from Microsoft Teams, SEI survey responses, affective indicators through Reflect app and machine learning models to explore associative and predictive relationships among these constructs. Results indicated strong positive associations between SRL behaviours, engagement, and final assessment outcomes, with engagement partially explaining the relationship between SRL and performance. Correlation and clustering analyses revealed alignment among self-regulation, cognitive engagement, and emotional competencies, while predictive modelling (XGBoost, R2 = 0.83) demonstrated that SRL-related indicators effectively model academic performance patterns. Overall, the findings provide theoretically informed evidence of meaningful associations among cognitive, behavioural, and emotional regulation processes in flipped learning environments, highlighting the value of integrating SRL theory with learning analytics for data-informed instructional design in higher education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1805171</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1805171</link>
        <title><![CDATA[Systematic review: inclusivity and sustainability in educational spaces through technology]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Sebastian Auquilla Clavijo</author><author>Darwin Chuqui-Calle</author><author>Gabriel Cabrera-Coraisaca</author><author>Fabián López-Morocho</author><author>Andrea Paulina Rodríguez Zúñiga</author><author>Priscila Cedillo</author>
        <description><![CDATA[IntroductionThis systematic review investigates the role of technology in advancing inclusivity and sustainability in educational spaces.MethodsA structured methodology was applied to analyze 20 studies published between 2015 and 2025, sourced from IEEE Xplore, ACM Digital Library, and ScienceDirect.ResultsThe findings reveal that technologies such as artificial intelligence (AI), the Internet of Things (IoT), virtual and augmented reality (VR/AR), adaptive platforms, and the Edu-Metaverse enhance inclusion by personalizing learning, overcoming physical and cognitive barriers, and improving access for disadvantaged communities. Sustainability is supported through smart infrastructure, neuroarchitecture principles, and alignment with Sustainable Development Goals (SDGs) 4, 10, and 17. However, limited integration between neuroarchitecture, sustainability, and inclusion was identified, along with a lack of long-term impact assessment.DiscussionThe results highlight the potential of technology to transform educational spaces into more inclusive and sustainable environments. Nevertheless, challenges related to scalability, equitable access, and interdisciplinary integration remain. Future research should focus on developing holistic frameworks and culturally adaptive solutions to bridge these gaps.Systematic review registrationhttps://osf.io/f5rxq]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1821454</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1821454</link>
        <title><![CDATA[From pictorial space to tactile form: a comparative evaluation of AI-based 2.5D reconstruction from modern artwork paintings]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rocco Furferi</author><author>Lapo Governi</author><author>Yary Volpe</author><author>Michaela Servi</author><author>Francesco Buonamici</author>
        <description><![CDATA[IntroductionThe translation of paintings into tactile 2.5D models (i.e., bas-reliefs) represents a significant advancement in improving accessibility for blind and visually impaired individuals. However, reconstructing spatial structure from a single painted image without explicit perspective is inherently ill-posed, particularly in modern and contemporary artworks where perspective, illumination, and geometry deviate from physical realism.MethodsThis study presents a comparative evaluation of three AI-based reconstruction paradigms: Monocular Depth Estimation, Large Language Models, and Large Reconstruction Models. These approaches are applied to a selected corpus of photographic, realist, and abstract artworks from the CSAC collection (Parma, Italy). An assessment framework is introduced, combining expert-based qualitative evaluation by art historians, formal geometric verification (including integrability and topological consistency), and manufacturability analysis conducted by additive manufacturing specialists.ResultsThe results indicate that Large Language Model-based methods generate semantically rich and perceptually plausible bas-reliefs but lack geometric integrability and topological robustness. Monocular Depth Models perform well in capturing depth hierarchies but tend to oversmooth fine details. Large Reconstruction Models demonstrate strong structural coherence and fabrication readiness, though they often struggle with stylistic reinterpretation.DiscussionThese findings highlight the trade-offs among current AI-based reconstruction approaches for tactile bas-relief generation. While each paradigm excels in specific aspects, none achieves a complete balance between perceptual fidelity, geometric soundness, and manufacturability. Future work should focus on hybrid strategies that integrate semantic understanding with geometric consistency to better support accessible cultural heritage applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1774796</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1774796</link>
        <title><![CDATA[Graph-based multimodal affect recognition in children using prototypical networks]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kavita Choudhary</author><author>Gend Lal Prajapati</author>
        <description><![CDATA[IntroductionAlthough physiological signals such as heart rate, perspiration, and facial muscle activity are recognized as markers of emotional events, precisely classifying affective states from these data remains a significant challenge. Addressing this issue is fundamental for developing advanced human-computer interaction and assistive technologies. While emotion recognition in adults has been extensively studied, it is less understood in children, necessitating focused research.MethodsThis study introduces a multimodal framework tailored for the emotion recognition of children. We used prototypical networks to learn discriminative embeddings from each physiological modality. These embeddings were then used to construct an adaptive k-nearest-neighbors (KNN) graph that models the interrelationships among affective conditions across the modalities. A graph neural network (GNN) leverages this structural representation for the final classification, improving performance by capturing the intrinsic relational context.ResultsOur proposed framework improved classification performance by 8%–10% compared to single-modality baselines and existing fusion approaches, achieving an overall accuracy of 83%.DiscussionThese results show that multimodal fusion and graph-based learning can accurately capture the complex interplay of biological signals in children, providing a more accurate approach to pediatric affective computing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1740606</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1740606</link>
        <title><![CDATA[A new Gaussian Black-winged Kite Algorithm for task scheduling optimization of industrial IoT applications in fog computing environment]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rania Mahmoud Eisa</author><author>Hussein Karam Abd El-Sattar</author><author>Haitham Farouk</author><author>Fatma A. Omara</author>
        <description><![CDATA[As a result of the increase in industrial Internet of Things (IoT) applications, fog computing (FC) has become a major area of research. A decentralized computing system called fog computing extends cloud computing to the network’s edge. The cloud allows for real-time insights and analysis by processing and storing enormous volumes of data produced by IoT devices. Consequently, the task scheduling technique in cloud computing is crucial. A number of metrics, such as makespan, resource utilization, and energy consumption, must be optimized for FC to function efficiently. This paper proposes a novel metaheuristic optimization technique called the Gaussian Black-winged Kite Algorithm (GBKA) to address task scheduling optimization of industrial IoT applications in a fog computing environment. The proposed algorithm employs Gaussian mutation, and the migration patterns and attack style of the black-winged kite serve as the inspiration for the proposed GBKA. The algorithm is designed to balance exploration of the search space and exploitation of the best solutions, avoiding local optima and improving energy efficiency. The Google Cloud Jobs dataset (GoCJ) with varying task sizes is used to validate the proposed algorithm. An analysis has been conducted to compare the performance of the proposed algorithm with the standard Black-winged Kite Algorithm (BKA) and metaheuristic algorithms like Dragonfly Algorithm (DA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). Experimental results show that GBKA reduces energy and makespan by an average of 7.26 and 9.32%, respectively. Additionally, it attains optimal resource utilization with an average overall improvement of 8.54%.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1798475</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1798475</link>
        <title><![CDATA[Artificial intelligence in physical examination teaching in Latin America: a critical narrative review and conceptual model proposal]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ariel Torres</author><author>Paloma González</author><author>Martha Fors</author><author>Gisselle Trujillo</author>
        <description><![CDATA[Artificial intelligence (AI) is reshaping medical education, particularly in the teaching of physical examination and the development of clinical judgement in digitally mediated contexts. This study presents a critical narrative review examining the ethical, pedagogical, and humanistic implications of AI integration into physical examination training in Latin America. A structured search of literature published between 2018 and 2025 was conducted across PubMed, Scopus, Web of Science, SciELO, and Google Scholar. Thirty-one peer-reviewed studies and three institutional documents met predefined relevance criteria and were analyzed through thematic synthesis. Four thematic domains emerged: (1) AI-assisted clinical simulation and automated feedback, (2) curricular integration and institutional implementation strategies, (3) governance and ethical supervision frameworks, and (4) emerging challenges related to digital literacy, technological dependence, and preservation of clinical judgement. Evidence suggests that AI enhances procedural precision and formative feedback; however, its educational value remains complementary and dependent on structured human-in-the-loop supervision. Based on these findings, the Modelo Educativo Digital basado en Inteligencia Artificial (or Medical Education with Artificial Intelligence) (MED-IA) conceptual model is proposed, framing clinical competence development across three interconnected levels: technical execution, experiential patient interaction, and reflective judgement. The model integrates technological mediation with ethical oversight and humanistic formation. These findings highlight the need for transparent governance frameworks, teacher digital literacy, and context-sensitive institutional policies to ensure responsible AI implementation in Latin American medical education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1773479</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1773479</link>
        <title><![CDATA[Eudaimonic HCI: a research agenda for designing technologies that support purpose, growth, and meaning]]></title>
        <pubdate>2026-04-09T00:00:00Z</pubdate>
        <category>Conceptual Analysis</category>
        <author>Khaled Tarmissi</author><author>Amine Marref</author>
        <description><![CDATA[The field of Human-Computer Interaction (HCI) has increasingly turned its attention to “digital wellbeing," yet the discourse remains narrowly focused. A significant portion of current research concentrates on mitigating the negative effects of technology—such as addiction, anxiety, and the harms of excessive screen time—or on a limited set of wellbeing domains, primarily social connection and physical health. This paper identifies a critical research gap: the need to move beyond a fragmented, nascent focus on eudaimonic wellbeing toward a systematic research agenda. Eudaimonia encompasses deeper aspects of human flourishing such as purpose, personal growth, reflection, and meaning. Through an extensive literature review, this paper confirms that while pioneering efforts exist, these eudaimonic domains remain significantly under-researched within mainstream HCI. In response, this paper proposes a new research agenda aimed at establishing “Eudaimonic HCI” as a critical sub-field. It articulates key open research questions concerning measurement, design patterns, human-AI collaboration, and the specific needs of vulnerable populations, aiming to unify and build upon current foundational work. Finally, it introduces a preliminary design framework to guide the creation of technologies that move beyond optimizing for engagement and instead aim to actively support users in living more meaningful and fulfilling lives.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1832170</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1832170</link>
        <title><![CDATA[Editorial: Reliable and secure system software in emerging cloud and distributed environments]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Xiaoguang Wang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780150</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780150</link>
        <title><![CDATA[Multimodal AI in education: an avatar-based intelligent learning system for the Kazakh language]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Aru Ukenova</author><author>Gulmira Bekmanova</author><author>Banu Yergesh</author><author>Sadok Ben Yahia</author><author>Mamyr Altaibek</author><author>Aizhan Nazyrova</author><author>Zhanar Lamasheva</author>
        <description><![CDATA[This article describes the development of a multimodal learning system for the Kazakh language intended for digital educational environments. The study focuses on the lack of avatar-based learning systems adapted to the linguistic properties of the Kazakh language and the limited integration of verbal and non-verbal components in existing solutions. The proposed system combines syntactic and morphological text analysis with sentiment processing and intonation control. Speech synthesis, gesture generation, facial expression control, and lip synchronization are implemented within a single system architecture. Prosodic parameters are formed based on sentence structure and sentence-level emotional indicators, while visual articulation is synchronized with audio output. The system was tested in speech synthesis scenarios relevant to interactive educational use. The results show that the system can be used for automated lecture narration, voice-over of instructional materials, and basic learner interaction in avatar-based educational settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1779096</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1779096</link>
        <title><![CDATA[Scalable multi-metric association rule learning for explainable book recommendations]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adel Hidri</author><author>Suleiman Ali AlSaif</author><author>Eman AlShehri</author><author>Minyar Sassi Hidri</author>
        <description><![CDATA[Digital reading platforms have grown rapidly, increasing information overload and highlighting the need for efficient and transparent recommendation systems. This study presents a scalable hybrid framework that combines multi-metric association rule learning (ARL) with intelligent filtering strategies to provide clear, high-quality book recommendations at scale. Unlike traditional ARL-based recommenders that depend on a single metric or small datasets, our approach combines support, confidence, and lift measures to identify strong behavioral patterns while maintaining computational efficiency. The framework uses data-reduction strategies that select active users and high-impact items, transforming a sparse rating matrix into a dense, computationally tractable representation. Extensive experiments on a real-world dataset demonstrated that our method significantly outperforms collaborative filtering, neural models, and rule-mining baselines in precision, recall, and normalized discounted cumulative gain (NDCG). The resulting rules are inherently interpretable, enabling clear explanations for recommendations, which is a critical feature of modern personalized systems. This study demonstrates that ARL remains viable when designed with modern scalability constraints in mind, providing an explainable, efficient solution for digital libraries, online platforms, and large-scale recommender systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1746674</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1746674</link>
        <title><![CDATA[Beyond the looking glass: multimodal LLM-based depth-sensing for spatial behavior modeling in media architecture]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhikun Wu</author><author>Ava Fatah gen. Schieck</author>
        <description><![CDATA[Large media façades are reshaping interactions in buildings and public spaces into immersive environments, yet empirical knowledge of how pedestrians behave inside these media spcaes is still limited. This study introduces a fully automated pipeline for in-the-wild behavior analysis that integrates a system which consists of a stereo-depth camera, an object detection model with multi-target tracking algorithm, and GPT-4o with visual reasoning. Deployed at London's immersive media building Now Arcade, the system captured 2 h of depth-enhanced video and produced more than six hundred anonymised visitor trajectories without any manual annotation. It reliably identified three recurrent behaviors: passing-by, lingering, and shooting (photographing or filming). To reveal where these actions occur, we propose Behavior Instance Density (BiD) heat-maps that project frame-level behavior instances onto a floor-plan grid of 0.5m × 0.5m squares. A comparative BiD study of 2 h-long content loops with static high-contrast imagery and dynamic low-contrast animation, shows clear content-driven behavior differences. Static saturated graphics encourage longer stays and more filming at both buildings entrance and exit thresholds, while dynamic darker visuals maintain a predominantly transit-oriented flow through the corridor.The proposed pipeline uses a compact, cost-effective sensing setup, safeguards privacy by discarding raw images after processing, and can be scaled for long-term or multi-site deployments. The resulting behavioral insights offer concrete guidance for media-architecture design and lay the groundwork for responsive façades that can update their digital content in real time according to observed human engagement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1754308</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1754308</link>
        <title><![CDATA[Digital dating abuse: a Grounded Theory study]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tiago Rocha-Silva</author><author>Conceição Nogueira</author><author>Liliana Rodrigues</author>
        <description><![CDATA[IntroductionOver the past decade, research on digital dating abuse (DDA) has expanded considerably, resulting in the development of multiple constructs and measurement instruments. Despite this progress, a key theoretical question remains unresolved: how should the behavioral multidimensionality of DDA be conceptualized? Moreover, little research has examined how DDA manifests in long-distance romantic relationships, where partners rely almost exclusively on information and communication technologies to interact and maintain their relationship.MethodsIn response to calls for more in-depth qualitative inquiry, we employed a constructivist Grounded Theory approach to develop a model accounting for the behavioral multidimensionality of DDA. Specifically, we collected and analyzed 1434 online posts published in Reddit (r/LongDistance) between January 2021 and June 2022, in which individuals described their experiences as perpetrators and/or victims of DDA.ResultsFindings indicate that DDA can be conceptualized as a multidimensional behavioral phenomenon encompassing two overarching dimensions: covert DDA and overt DDA. Covert DDA includes behaviors such as major changes in communication, deception, and passive control, which may be normalized within romantic relationships yet can function as precursors to more explicit forms of abuse. Overt DDA encompasses active control, hostility, and sexual coercion. The analysis revealed a continuum between covert and overt forms of DDA.DiscussionThis study contributes to the literature by extending conceptualizations of DDA to the context of LDRRs and by emphasizing the analytical and clinical relevance of covert abusive behaviors.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780814</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780814</link>
        <title><![CDATA[Digital anxiety: psychological effects of social media on women and a human-centered AI framework for mental health support]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Aizhan Nazyrova</author><author>Muslim Sergaziyev</author><author>Assel Omarbekova</author><author>Latifa Sautbayeva</author><author>Zhandos Akimjan</author><author>Zhanar Lamasheva</author>
        <description><![CDATA[This article examines the psychological effects of social media use and explores gender-related differences, with particular attention to issues reported by women. The analysis is informed by social comparison theory and self-determination theory to explain how digital environments influence behavior and self-perception. The study focuses on psychological outcomes such as anxiety, depressive symptoms, body image dissatisfaction, and patterns of compulsive platform use. In parallel, social media platforms generate extensive behavioral data that may support the identification of mental health risks. From a computational perspective, artificial intelligence methods – including content analysis, sentiment analysis, and machine learning classification – are examined as tools for early screening of psychological distress within digital environments. A hybrid methodological approach is applied to integrate psychological analysis with data-driven AI (artificial intelligence) techniques. The results indicate that social media use is associated with higher levels of self-reported psychological vulnerability among women, while AI-based methods demonstrate the capacity to detect mental health-related signals in digital data. From a computer science perspective, the study contributes to human-centered and responsible artificial intelligence by proposing an interdisciplinary computational framework that links multimodal digital data with psychologically grounded constructs. The article concludes by outlining possible applications of AI in digital well-being initiatives and discussing ethical considerations related to privacy, autonomy, and transparency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1802727</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1802727</link>
        <title><![CDATA[The dark side of autonomous intelligence: a survey on data leakage and privacy failures in agentic AI]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rohini Bhosale</author><author>Pankaj Chandre</author><author>Sushma Mehetre</author><author>Swati Powar</author><author>Shubhra Mathur</author><author>Arun Ghandat</author>
        <description><![CDATA[IntroductionThe rapid evolution of artificial intelligence from static large language models to autonomous, agentic AI systems has introduced capabilities such as persistent memory, tool-augmented reasoning, and multi-agent collaboration. While these advancements significantly enhance real-world applicability, they also create a new and underexplored class of privacy risks, including unintended retention, propagation, and amplification of sensitive information across tasks, users, and execution cycles. Existing research predominantly focuses on stateless or single-inference models, leaving the privacy implications of agentic systems insufficiently understood.MethodsThis study presents a comprehensive architectural analysis of data leakage in agentic AI systems. The proposed framework models the end-to-end agent workflow and systematically examines how sensitive information can traverse key components, including persistent memory modules, planning and reasoning processes, tool invocation layers, inter-agent communication channels, and feedback-driven autonomy loops. Based on this architecture, a structured taxonomy of leakage pathways is developed and mapped to realistic threat models and attack vectors observed in practical deployments.ResultsThe analysis identifies multiple leakage pathways unique to agentic AI systems, demonstrating how data can persist, propagate, and be unintentionally exposed across system components and operational cycles. The findings reveal that these leakage mechanisms are more complex and pervasive than those observed in traditional large language model settings, particularly due to the integration of memory, tools, and multi-agent interactions.DiscussionThe study highlights the limitations of existing LLM-centric privacy and security defenses when applied to autonomous agentic systems. It emphasizes the need for lifecycle-aware, component-level mitigation strategies that address privacy risks across the entire agent workflow. The proposed architectural perspective provides a foundation for designing privacy-by-design agentic AI systems and supports safer deployment in sensitive and regulated domains.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1652980</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1652980</link>
        <title><![CDATA[Explainable AI digital twin framework for early lung disease detection]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Akey Sungheetha</author><author>Rajesh Sharma R.</author><author>Oluwasegun Julius Aroba</author>
        <description><![CDATA[IntroductionDigital twin technology creates virtual replicas of physical systems, enabling real-time monitoring and predictive analytics through continuous data synchronization. This study presents an explainable artificial intelligence-enhanced digital twin framework specifically designed for the early detection of chronic lung abnormalities in urban young adults aged 20–35 years.MethodsAnalysis of 4,247 patients from the Delhi metropolitan area revealed a 29.3% prevalence of structural lung damage, including bronchiectasis, emphysema, and fibrosis. The framework integrates multimodal physiological sensors, environmental pollution monitoring, and lifestyle data through advanced fusion algorithms. Mathematical modeling incorporates bronchial resistance Rb = 2.34 ± 0.45 cmH2O/L/s, lung compliance CL = 0.187 ± 0.032 L/cmH2O, and deterioration rate λdet = 0.0156 ± 0.0023 per month from longitudinal monitoring. Blockchain integration ensures data security with hash validation efficiency ηhash = 0.987 and real-time processing latency τresp = 127.3 ± 15.7 ms. Environmental factor integration, including the air quality index AQI = 247 ± 67, enables personalized risk stratification accuracy βrisk = 0.876 ± 0.045.ResultsCore performance metrics demonstrate explainability coefficient ξexp = 0.847 ± 0.023, prediction accuracy αpred = 0.923 ± 0.034, and early detection capability extending tearly = 6.7 ± 1.2 months before clinical symptoms. Validation across 1,847 test subjects achieved sensitivity, Searly = 0.891, specificity, Spearly = 0.876, and positive predictive value (PPV) = 0.834. Environmental factor integration, including the air quality index AQI = 247 ± 67, enables personalized risk stratification accuracy βrisk = 0.876 ± 0.045. Statistical analysis confirmed significant improvements in diagnostic timing (p < 0.001), intervention effectiveness (p < 0.001), and patient outcomes compared to conventional approaches.DiscussionClinical implementation demonstrates 68.4% reduction in diagnostic delays, 73.6% improvement in intervention timing, and annual healthcare cost savings of ΔC = $2, 847 per patient.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1824259</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1824259</link>
        <title><![CDATA[Correction: An improved contrastive learning loss function for automated clock-drawing test grading with implications for cognitive impairment screening]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Ning Liu</author><author>Qian Sun</author><author>Xiaoyin Xu</author><author>Haifeng Mou</author><author>Xinhai Liao</author><author>Bokai Rong</author><author>Lingxing Wang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1789829</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1789829</link>
        <title><![CDATA[An experimental study of structured generative AI integration to mitigate pedagogical, cognitive, and ethical barriers in programming education]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jemimah Nathaniel</author><author>Solomon Sunday Oyelere</author><author>Jarkko Suhonen</author><author>Matti Tedre</author>
        <description><![CDATA[Generative artificial intelligence (GenAI) is used in programming education; however, its adoption can introduce pedagogical misalignment, shallow cognitive engagement, and ethical risks that threaten the sustenance of programming skills of students. This study evaluated the GenAI programming education framework’s ability to sustain higher-order thinking skills (HOTS) and programming logic while mitigating pedagogical, cognitive, and ethical barriers in Java programming. A between-group mixed-methods experiment was conducted amongst 124 undergraduate students (62 in the control group and 62 in the experimental group) over 7 weeks. Learning outcomes were assessed using pretests and posttests, analyzed with baseline-adjusted ANCOVA and MANCOVA, and supplemented with trace-based learning analytics from GenAI logs collected at time points (Weeks 3 and 7). The experimental group showed a baseline-adjusted advantage on HOTS (adjusted mean difference = 0.29; p < 0.001; adjusted Hedges’ g = 0.80) and a smaller but significant improvement in programming logic (adjusted mean difference = 0.21; p = 0.047; adjusted Hedges’ g = 0.36), alongside a multivariate group effect across domains. Log-derived indices also showed larger gains in pedagogical alignment and cognitive engagement, reflected in more frequent task decomposition and debugging behaviors. Ethical engagement has also increased, indicating consistent hallucination and data sensitivity awareness. Path modelling indicated that the intervention increased changes in pedagogical, cognitive, and ethical engagement. Pedagogical alignment and cognitive engagement were positively associated with post-test HOTS and programming logic, whereas ethical engagement was negatively associated with HOTS but not significantly associated with programming logic. Overall, the findings suggest that GenAI becomes more educationally beneficial in programming when guided by a structured approach.]]></description>
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