<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <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>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-04-04T21:49:55.17+00:00</pubDate>
        <ttl>60</ttl>
        <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>
      </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>
      </item><item>
        <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.2026.1734298</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1734298</link>
        <title><![CDATA[A lightweight approach to software fault localization using static features of statements in cloud computing environments]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Peng Xiao</author><author>Xiuhong Shen</author><author>Sujun Wang</author>
        <description><![CDATA[With the pervasive adoption of cloud computing, modern software systems have evolved toward highly distributed, elastic, and microservice-based architectures, which significantly increase the difficulty of effective fault localization. Spectrum-Based Fault Localization (SBFL) techniques are widely used to assist automated program repair and manual debugging; however, their practical effectiveness remains limited. Specifically, existing SBFL methods largely overlook the static semantic characteristics of program statements and fail to fully exploit the abundant execution data and scalable computational resources available in cloud computing environments. To address these limitations, this paper proposes a lightweight fault localization approach based on learning to rank, explicitly designed for cloud computing scenarios. The proposed method employs a linear ranking Support Vector Machine (SVM) that jointly integrates traditional SBFL suspiciousness scores with static statement-level features, including variables, operators, and statement categories, to construct a more discriminative fault localization model. Furthermore, to better leverage resource coordination and large-scale data processing capabilities in cloud environments, a cross-project training strategy is adopted, and distributed cloud resources are utilized to enable efficient model training and validation. The proposed approach is evaluated on large-scale datasets comprising 19 Java, 19 C, and 2 C++ projects. Experimental results demonstrate that, under the EXAM metric with the worst-case evaluation strategy, the proposed method reduces the number of statements requiring inspection by 26.1% compared to the best-performing SBFL technique. These findings indicate that integrating static program features with cloud-enabled learning and resource coordination can substantially improve fault localization effectiveness in complex cloud-based software systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1822456</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1822456</link>
        <title><![CDATA[Editorial: AI innovations in education: adaptive learning and beyond]]></title>
        <pubdate>2026-03-30T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Luigi Gallo</author><author>Maria Concetta Carruba</author><author>Antonino Ferraro</author><author>Henrik Hautop Lund</author><author>Angelo Rega</author><author>Stefano Triberti</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1800319</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1800319</link>
        <title><![CDATA[When buildings learn how we move: embodied human–building interaction in the age of machine intelligence]]></title>
        <pubdate>2026-03-27T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Benoît G. Bardy</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1779065</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1779065</link>
        <title><![CDATA[From risk to resilience: addressing cybersecurity threats in Brazil’s government digital transformation]]></title>
        <pubdate>2026-03-26T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Bruno Baranda Cardoso</author>
        <description><![CDATA[This chapter analyzes Brazil’s journey in consolidating a resilient digital ecosystem in the public sector, covering both the advances and challenges faced in implementing the National Digital Government Strategy (ENGD). It first addresses the varying degrees of digital maturity among government bodies and agencies, highlighting the inequalities and factors contributing to the fragmentation of the technological environment within the government. Next, it discusses the urgent need to shift from a predominantly reactive posture to a proactive approach in managing cyber risks, emphasizing the importance of a culture of prevention and continuous training of public agents. Finally, it underscores the strategic role of public technology companies, which act not only as facilitators of digital transformation but also as key players in threat identification, comprehensive risk assessments, and the consolidation of cyber protection mechanisms within the Brazilian State.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1757509</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1757509</link>
        <title><![CDATA[Interaction design methods for data-intelligent museum exhibitions: an embodied cognition perspective]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Husheng Pan</author><author>Cuiting Kong</author><author>Lie Zhang</author>
        <description><![CDATA[Addressing current practical challenges in exhibitions in the data-intelligent era—such as an overemphasis on form over content and insufficient emotional resonance—as well as the lack of a systematic theoretical framework to guide practice, this paper draws on embodied cognition and dynamical systems theory to explore the internal mechanisms of interaction design for museum exhibitions in the data-intelligent era from the perspective of cognitive generation. It identifies four core elements of such interaction design, namely the Body Perception Layer, the Body Action Layer, the Environmental Construction Layer and the Meaning Construction Layer, and on this basis constructs a cyclic embodied interaction design framework for museums (the PCAE model) that reveals the dynamic flow of information between visitors and the data-intelligent exhibition environment. Using the data-intelligent interactive exhibit “Dialogue With the Master” at the Confucius Museum in China as a case study, the paper further validates the feasibility and scientific soundness of the proposed framework. This framework introduces a new embodied cross-disciplinary theoretical perspective for research on interaction design in museums in the data-intelligent era and provides an operational design tool that offers designers a clear guiding pathway for optimizing interactive experiences, thereby holding substantial practical value for design practice and theoretical exploration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1795045</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1795045</link>
        <title><![CDATA[Phishing 2.0: exploring the capabilities and risks of agentic AI-enabled attacks]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pankaj Chandre</author><author>Pallavi Bhujbal</author><author>Reetika Kerketta</author><author>Jyoti Nandimath</author><author>Bhagyashree Shendkar</author><author>Rohini Bhosale</author>
        <description><![CDATA[IntroductionPhishing attacks have evolved rapidly with the integration of artificial intelligence, posing serious threats to digital trust and cybersecurity. Traditional and AI-assisted phishing techniques still rely on partial human intervention, limiting their adaptability and scalability. Recent advances in agentic artificial intelligence have enabled fully autonomous, goal-driven phishing campaigns capable of planning, personalizing, and executing attacks across multiple communication channels.MethodsThis study investigates the capabilities of agentic AI–enabled phishing by examining its core functional components and operational characteristics. A conceptual architectural perspective is presented to illustrate how autonomous planning, contextual intelligence, multi-modal content generation, and adaptive feedback mechanisms interact to support automated phishing campaigns.ResultsThe analysis demonstrates that agentic AI significantly enhances phishing capabilities by enabling continuous optimization, contextual personalization, and adaptive decision-making during attack execution. The interaction of these architectural components allows phishing systems to dynamically refine strategies and potentially evade conventional detection mechanisms.DiscussionThe study highlights the increasing detection challenges posed by agentic AI–driven phishing systems and examines the associated technical, organizational, and societal risks. Emerging defense strategies and future research directions are discussed to address the evolving threat landscape. Overall, the findings emphasize the urgent need for adaptive and AI-driven countermeasures to effectively mitigate next-generation phishing attacks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1813431</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1813431</link>
        <title><![CDATA[The role of AI-driven personalised learning in enhancing mathematics problem-solving skills: a systematic review]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Nthabeleng Eti</author><author>Moeketsi Mosia</author><author>Felix O. Egara</author>
        <description><![CDATA[AI-driven personalised learning is increasingly shaping mathematics education, yet evidence remains fragmented regarding its role in developing learners’ mathematical problem-solving skills. This systematic review examined how AI-driven personalised learning influences students’ mathematical problem-solving skills. A structured search of recent empirical studies (2019–2025) identified 20 eligible investigations, which were analysed thematically. Findings show that AI tools, such as adaptive learning systems, intelligent tutoring systems, and chatbots, can enhance mathematical problem solving by providing tailored feedback, adaptive challenges, and scaffolded support that align with learners’ needs. These benefits were observed across primary, secondary, and tertiary settings, contributing to enhanced conceptual understanding, improved strategic reasoning, and increased learner engagement. At the same time, the review highlights notable variations in effectiveness. Some studies have reported an over-reliance on AI hints, misaligned adaptivity, platform complexity, and limited teacher readiness, which have constrained learners’ development of independent problem-solving skills. Infrastructure disparities and data privacy concerns also emerged as persistent challenges. Despite the growing number of studies on AI in mathematics education, limited systematic evidence exists that focuses specifically on AI-driven personalised learning and its influence on mathematical problem-solving processes. This review addresses this gap by synthesising recent empirical studies and identifying the key mechanisms through which AI personalisation supports or constrains learners’ problem-solving development. In general, the review suggests that AI-driven personalised learning holds meaningful potential for strengthening mathematics instruction when grounded in sound pedagogy and supported by adequate technological and instructional resources. This synthesis contributes evidence-based insights for educators and policymakers aiming to integrate AI responsibly and effectively in mathematics education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1766830</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1766830</link>
        <title><![CDATA[Educational games as a tool for teaching programming in digital extracurricular computer science education]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bolat Tassuov</author><author>Ainur Nadyrbekova</author><author>Karakoz Ibragimova</author><author>Zhazira Taszhurekova</author><author>Nadira Niyetbayeva</author><author>Aigerim Abzhapparova</author><author>Zarina Aidaraliyeva</author>
        <description><![CDATA[This study explores how university students experience learning programming through educational games in extracurricular computer science education. A qualitative phenomenological design was employed to interpret the meanings students attribute to game-based learning rather than to evaluate instructional effectiveness. Data were collected through semi-structured interviews with 60 undergraduate students who participated in supervised extracurricular programming activities involving educational coding games. The analysis revealed that educational games transform programming from a formal academic subject into an exploratory problem-solving activity supported by immediate feedback. Students described a reduction of anxiety and perceived the environment as psychologically safe for experimentation. However, gameplay alone produced an intuitive yet fragmented understanding, requiring instructor explanation for conceptual structuring. The experience of learning was therefore characterized as a transition from experiential discovery to reflective comprehension. The findings indicate that educational games function as an experiential entry point through which students begin to interpret programming as a meaningful activity, while instruction supports conceptual articulation. This study contributes to understanding the pedagogical role of educational games not as substitutes for teaching but as mediating environments shaping students' perception of programming.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1678653</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1678653</link>
        <title><![CDATA[ICT tools for autism spectrum disorder interventions linked with parental involvement in children’s education and support]]></title>
        <pubdate>2026-03-20T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Sevasti Kapsi</author>
        <description><![CDATA[Parental involvement οr engagement is essential for the holistic development of children, especially for children with autism spectrum disorder (ASD). Several ASD interventions integrate parental involvement, with positive outcomes. ICT tools affect children with ASD in their daily lives by empowering social–emotional, communicational, and educational skills. This literature review aims to examine the relationship between ICTs and parental involvement (PI) for children with ASD. Specifically, it describes the most frequently reported theoretical models of PI and identifies effective interventions that integrate ICTs with parental involvement. Combining powerful interventions could lead to better therapeutic and educational outcomes. Results from studies show that, despite methodological limitations, ICTs may support parental engagement in ASD interventions, helping both children with ASD and their parents. Future research could test new synthesized protocols for effectiveness in ASD treatment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1762332</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1762332</link>
        <title><![CDATA[Explainable AI: enhancing decision-making in the detection of cyber threats]]></title>
        <pubdate>2026-03-20T00:00:00Z</pubdate>
        <category>Review</category>
        <author>P. W. C. Prasad</author><author>Md Shohel Sayeed</author><author>Duc-Man Nguyen</author><author>Daniel Patricko Hutabarat</author><author>Golam Md Mohiuddin</author>
        <description><![CDATA[The rapid growth of the Internet and the increasing reliance on digital systems have significantly expanded the global digital footprint, creating new challenges for cybersecurity. Artificial Intelligence (AI) technologies, particularly Machine Learning (ML) and Deep Learning (DL), have become central to addressing these challenges by enabling the automation of complex and data-intensive tasks across antivirus solutions, intrusion prevention systems, threat intelligence platforms, and email security tools. While these technologies provide high levels of accuracy in detecting anomalies, malware, and other forms of malicious activity, they are often criticized for operating as “black-box” systems. The lack of interpretability in their decision-making processes limits the ability of cybersecurity professionals to fully understand, validate, and trust the outcomes of AI-driven models, thereby restricting their practical adoption in high-stakes environments. To mitigate these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising paradigm that aims to make AI outputs transparent, interpretable, and actionable. By providing human-understandable explanations of automated decisions, XAI can bridge the gap between technical performance and practitioner usability, enabling analysts to make informed decisions, improve incident response, and strengthen organizational resilience against both known and emerging threats. This paper reviews recent state-of-the-art developments in XAI for cybersecurity, with a particular emphasis on anomaly detection a critical area for identifying insider threats, zero-day exploits, and atypical system behavior. The review follows a structured literature analysis of peer-reviewed studies published between 2018 and 2025, identified through systematic searches in major academic databases including IEEE Xplore, Scopus, Web of Science, and ACM Digital Library. After applying predefined inclusion and exclusion criteria focused on XAI applications in cybersecurity, 53 relevant studies were analysed to synthesize methodological trends, application domains, and evaluation practices. Drawing on these findings, the paper consolidates fragmented research contributions, identifies current gaps, and provides recommendations for advancing the design and adoption of explainable, trustworthy AI systems in cybersecurity. The analysis further highlights a critical deployment challenge: the integration of explainability mechanisms often introduces trade-offs between predictive accuracy, computational efficiency, and real-time scalability factors that are essential in operational cybersecurity environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1768435</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1768435</link>
        <title><![CDATA[Lay belief about AI and its decision-making]]></title>
        <pubdate>2026-03-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Suhas Vijayakumar</author><author>W. Yuna Yang</author><author>David DeFranza</author>
        <description><![CDATA[This paper examines people’s lay belief concerning the mind of an artificial intelligence (AI) as a decision-making agent and how this belief shapes an individual’s own decision-making style in response. People perceive AI as more rational and reason-driven, in contrast to viewing humans as emotionally driven. Two studies confirm these beliefs, showing participants consistently judge AI as reason-based and humans as emotion-driven in decision-making. In a subsequent study, participants engage in an economic ultimatum game. When participants thought they were interacting with an AI (vs. a human) competitor, they adopted a more economically rational decision-making style, moving closer to the game-theoretic optimum. This shift in decision-making style was mediated by participants’ belief in the rational nature of AI. The findings suggest that perceptions of AI’s decision-making tendencies can influence the cognitive strategies that are adopted in response, with potential implications for human-AI interactions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1686763</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1686763</link>
        <title><![CDATA[The data-driven voice-body in performance: AI voices as materials, mediators, and gifts]]></title>
        <pubdate>2026-03-17T00:00:00Z</pubdate>
        <category>Conceptual Analysis</category>
        <author>Jonathan Chaim Reus</author>
        <description><![CDATA[Data-driven, realistic and identity-bearing AI voice technologies have proliferated in recent years. Voice, a multiply embodied phenomenon situated within and across human bodies in space and time, is deeply disrupted by the disembodying tendencies of AI voice technologies and their processes of data collection and data creation, resulting in the need for a re-evaluation of perceptual, cognitive and cultural factors. This article addresses this need by synthesizing ideas from embodied cognition, voice studies, and material anthropology to analyze real-time, AI-mediated voice as a form of embodied cognition that is an intersubjective, extended, materially and socially distributed phenomenon. Through the case study of the live performance iː ɡoʊ weɪ, this article makes three contributions: (1) it articulates AI-mediated vocal identity as a process of continual reconfiguration across human and machine agencies; (2) it foregrounds audience perception as an active force in stabilising and destabilising emergent voice–body assemblages; and (3) it proposes a speculative ethical framework for vocal data practice grounded in the notion of voice as gift.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1652190</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1652190</link>
        <title><![CDATA[Explainable AI framework for psilocybin depression treatment optimization]]></title>
        <pubdate>2026-03-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Akey Sungheetha</author><author>R. Rajesh Sharma</author><author>Oluwasegun Julius Aroba</author><author>Sheila Mahapatra</author><author>P. D. Mahendhiran</author>
        <description><![CDATA[IntroductionThis computational modeling study introduces a novel Explainable Artificial Intelligence (XAI) framework for optimizing single-dose psilocybin treatment protocols through personalized intervention modeling using publicly available mental health datasets. All results presented are derived from novel simulated data and predictive modeling only, not from real-time clinical implementations or actual patient treatments.MethodsThe mathematical optimization model integrates digital twin technologies, multimodal depression detection systems, and Bayesian optimization algorithms to create comprehensive computational patient profiles with temporal resolution processing capabilities at 250 Hz sampling frequency. Validation employed three publicly available datasets: (1) the Psilocybin Precision Functional Mapping dataset from OpenNeuro containing neuroimaging data from 7 participants, (2) the MODMA multimodal mental disorder dataset with 53 participants including electroencephalography and audio signals, and (3) a meta-analytic psilocybin therapy outcomes dataset containing aggregated results from 10 clinical trials. The framework incorporates pharmacokinetic modeling with an absorption rate constant of 0.45 per hour and an elimination rate constant of 0.23 per hour, receptor occupancy dynamics based on a dissociation constant of 6.3 nanomolar, and simulated real-time monitoring protocols processing physiological parameters including heart rate variability, blood pressure measurements, and cortisol levels at a 1 Hz frequency.ResultsThe simulated computational model demonstrates significant improvements in prediction accuracy, reaching 94.7%, and therapeutic transparency, achieving 89.3% explainability scores. Simulated validation demonstrates computational precision of 92.8% in predicting treatment response patterns across diverse patient populations in silico. The proposed computational methodology addresses key challenges in psychedelic-assisted therapy modeling through interpretable artificial intelligence models, achieving 96.2% computational safety index scores and 91.5% algorithmic compliance metrics in simulation environments. Energy-efficient computational architecture achieves 73.4% carbon footprint reduction through optimized algorithm design and sparse matrix representations.DiscussionThis study presents a theoretical computational framework for modeling therapeutic outcomes through simulation and prediction, establishing a foundation for future clinical validation through prospective randomized controlled trials. The framework supports sustainable digital mental healthcare delivery systems compatible with renewable energy infrastructure. All findings represent computational predictions and simulated scenarios requiring extensive clinical validation before any practical application.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1799323</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1799323</link>
        <title><![CDATA[Next-Gen orientation: supporting international students with generative AI NPCs in VR]]></title>
        <pubdate>2026-03-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Santiago Berrezueta-Guzman</author><author>Stefan Wagner</author>
        <description><![CDATA[Educational Virtual Reality (VR) provides immersive learning environments, yet most contemporary applications rely on pre-scripted Non-Player Characters (NPCs) that offer limited personalization and rigid interaction paths. This study presents the technical implementation and evaluation of TUMSphere, a VR orientation platform designed to facilitate the academic and cultural transition of international students. We propose a modular architecture that integrates Large Language Models (LLMs) with Unreal Engine via the Conversational AI (Convai) platform, enabling embodied NPCs to provide real-time speech recognition, context-aware dialogue, and autonomous spatial navigation. To validate this approach, a mixed-methods user study (N = 24) was conducted with international students to assess system latency, usability, and pedagogical efficacy. Results demonstrate a high System Usability Scale (SUS) score of 76.4 (SD = 12.5) and robust task completion rates, reaching 100% for spatial navigation and 96% for information retrieval. While technical benchmarking revealed an average end-to-end latency of 2.90s for complex, retrieval-heavy queries, qualitative findings indicate that users find this “latency-presence trade-off” acceptable in exchange for the pedagogical benefits. Crucially, participants reported a significant reduction in social anxiety when practicing language and administrative queries with AI agents compared to human interlocutors. These findings suggest that embodied, generative AI NPCs can serve as a scalable, low-pressure “social sandbox” that effectively redefines student support systems and orientation strategies in higher education.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1735253</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1735253</link>
        <title><![CDATA[RoLLMRec: a robust LLM-based recommender system for defending against shilling and prompt injection attacks]]></title>
        <pubdate>2026-03-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sarama Shehmir</author><author>Rasha Kashef</author>
        <description><![CDATA[Large Language Models (LLMs) are increasingly being integrated into recommender systems, offering contextual reasoning, cross-domain adaptability, and natural language interaction. However, their adoption also introduces vulnerabilities such as prompt injection, semantic poisoning, and shilling attacks, which can distort recommendations and erode user trust. Addressing these risks is essential for the safe deployment of LLM-based recommenders. We propose RoLLMRec, a defense oriented architectural framework and evaluation methodology for LLM-based recommender systems that integrates prompt filtering, retrieval augmented grounding, trust aware scoring, and an auditing feedback loop. RoLLMRec improves robustness under the evaluated prompt level and semantic adversarial settings, while multimodal support is included at the architectural level only and is not empirically evaluated in the current experimental setup.RoLLMRec unifies five core components: (1) prompt shielding and input filtering to detect and block adversarial instructions; (2) retrieval-augmented generation to enrich factual grounding and reduce hallucination; (3) multimodal LLM encoding for text, metadata, and image inputs; (4) trust-aware scoring and Top-K ranking; and (5) adaptive feedback loops for continual learning. Evaluations on benchmark datasets such as Yelp, MovieLens, and Amazon Books show that RoLLMRec surpasses BERT4Rec, RecVAE, and LightGCN, improving NDCG@10 and HR@10 by up to 6% and 5%, respectively. Under a 10% prompt-injection attack, it maintains a Robust Hit Rate (RHR@10) above 0.63 and a Perturbation Sensitivity Index (PSI) below 0.135, achieving 15%–25% higher resilience. It also sustains a Semantic Stability Score (SSS) above 0.60 in zero-shot cross-domain transfer, confirming stable semantic intent.]]></description>
      </item>
      </channel>
    </rss>