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Digital Cultural Heritage in Southeast Asia: Knowledge Structures and Resources in GLAM Institutions
Human-AI Symbiotic Theory (HAIST): Development, Multi-Framework Assessment, and AI-Assisted Validation in Academic Research
Explainable AI for Clinical Decision Support Systems: Literature Review, Key Gaps, and Research Synthesis
Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy Preserving and Real-Time Threat Detection Capabilities
Federated Learning Spam Detection Based on FedProx and Multi-Level Multi-Feature Fusion

Journal Description

Informatics

Informatics is an international, peer-reviewed, open access journal on information and communication technologies, human–computer interaction, and social informatics, and is published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, and other databases.
Journal Rank: CiteScore - Q1 (Communication)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 32.1 days after submission; acceptance to publication is undertaken in 4.2 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Information Systems and Technology: Analytics, Applied System Innovation, Cryptography, Data, Digital, Informatics, Information, Journal of Cybersecurity and Privacy and Multimedia.

Impact Factor: 2.8 (2024); 5-Year Impact Factor: 3.1 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2227-9709

Latest Articles

33 pages, 1025 KB

Open AccessArticle

Exploring Scientific Literature Using Topic Modeling: A Practical Framework for Discovery and Classification

by Amir Alipour Yengejeh, Larry Tang, Candice M. Bridge and Chandra Kundu

Informatics 2026, 13(2), 24; https://doi.org/10.3390/informatics13020024 - 30 Jan 2026

The increasing volume and diversity of scientific publications poses challenges for scalable and interpretable topic discovery and automated document categorization. This study proposes an integrated framework that combines probabilistic topic modeling with supervised classification to support large-scale scientific literature analysis. Using 3689 abstracts [...] Read more.

The increasing volume and diversity of scientific publications poses challenges for scalable and interpretable topic discovery and automated document categorization. This study proposes an integrated framework that combines probabilistic topic modeling with supervised classification to support large-scale scientific literature analysis. Using 3689 abstracts from the Journal of Forensic Sciences (2009–2022), Latent Dirichlet Allocation (LDA) is applied to uncover latent thematic structures, assess topic diagnosticity across forensic disciplines, and analyze temporal research trends. Bayesian model selection with repeated resampling identifies a stable topic resolution, with the number of topics T lying in the range

83 - - 88

, yielding semantically coherent and discipline-aligned topics. The resulting document–topic representations are then used for supervised abstract classification. Across multiple models and resampling scenarios, the strongest and most stable performance is achieved under a Grouped Category configuration. In particular, XGBoost attains an Accuracy of 0.754 and a Macro-averaged F1 score of 0.737 at

T = 88

, with comparable results at neighboring topic counts, indicating robustness to topic granularity. Overall, the proposed framework provides a reproducible, interpretable, and computationally efficient pipeline for literature organization, trend analysis, and metadata enhancement in scientific domains. Full article

(This article belongs to the Section Big Data Mining and Analytics)

9 pages, 1747 KB

Open AccessBrief Report

Leveraging Informatics to Manage Lifelong Monitoring in Childhood Cancer Survivors

by Kimberly Ann Davidow, Renee Gresh, E. Anders Kolb, Ellen Guarnieri and Mary R. Cooper

Informatics 2026, 13(2), 23; https://doi.org/10.3390/informatics13020023 - 29 Jan 2026

Background: Electronic health records (EHR) have long held promise for sharing information efficiently, but this remains challenging. This quality improvement initiative sought to improve the accurate documentation of anthracycline and radiation therapy exposures in pediatric oncology patients who were treated at different [...] Read more.

Background: Electronic health records (EHR) have long held promise for sharing information efficiently, but this remains challenging. This quality improvement initiative sought to improve the accurate documentation of anthracycline and radiation therapy exposures in pediatric oncology patients who were treated at different institutions through a quality improvement methodology and EHR tools. Methods: A custom-built EHR smartform was previously created. Modifications were made to the smartform, and quality improvement methods were utilized to improve receipt of radiation summaries from other institutions and documentation of chemotherapeutic doses. Results: Three months after interventions, including clinician education and smartform updates, accurate anthracycline documentation improved from ≤60% to 100%. At 12 months post-intervention, accurate anthracycline documentation remained > 90%. Documentation of radiation therapy improved similarly at 3 months post-intervention, with sustained improvement to 81% at 12 months post-intervention. Conclusions: Accurate documentation of radiation and chemotherapeutic exposures for pediatric oncology patients improved with education and changes to an EHR smartform. A central data location with quality assurance tools to ensure accuracy is one solution enabling accurate tracking of exposures and care plans for children with chronic illnesses. Full article

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25 pages, 362 KB

Open AccessArticle

Generative AI in Developing Countries: Adoption Dynamics in Vietnamese Local Government

by Phu Nguyen Duy, Charles Ruangthamsing, Peerasit Kamnuansilpa, Grichawat Lowatcharin and Prasongchai Setthasuravich

Informatics 2026, 13(2), 22; https://doi.org/10.3390/informatics13020022 - 28 Jan 2026

Generative Artificial Intelligence (GenAI) is rapidly reshaping public-sector operations, yet its adoption in developing countries remains poorly understood. Existing research focuses largely on traditional AI in developed contexts, leaving unanswered questions about how GenAI interacts with institutional, organizational, and governance constraints in resource-limited [...] Read more.

Generative Artificial Intelligence (GenAI) is rapidly reshaping public-sector operations, yet its adoption in developing countries remains poorly understood. Existing research focuses largely on traditional AI in developed contexts, leaving unanswered questions about how GenAI interacts with institutional, organizational, and governance constraints in resource-limited settings. This study examines the organizational factors shaping GenAI adoption in Vietnamese local government using 25 semi-structured interviews analyzed through the Technology–Organization–Environment (TOE) framework. Findings reveal three central dynamics: (1) the emergence of informal, voluntary, and bottom-up experimentation with GenAI among civil servants; (2) significant institutional capacity constraints—including absent strategies, limited budgets, weak integration, and inadequate training—that prevent formal adoption; and (3) an “AI accountability vacuum” characterized by data security concerns, regulatory ambiguity, and unclear responsibility for AI-generated errors. Together, these factors create a state of governance paralysis in which GenAI is simultaneously encouraged and discouraged. The study contributes to theory by extending the TOE framework with an environment-specific construct—the AI accountability vacuum—and by reframing resistance as a rational response to structural gaps rather than technophobia. Practical implications highlight the need for capacity-building, regulatory guidance, accountable governance structures, and leadership-driven institutional support to enable safe and effective GenAI adoption in developing-country public sectors. Full article

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19 pages, 1132 KB

Open AccessArticle

A Highly Robust Approach to NFC Authentication for Privacy-Sensitive Mobile Payment Services

by Rerkchai Fooprateepsiri and U-Koj Plangprasopchoke

Informatics 2026, 13(2), 21; https://doi.org/10.3390/informatics13020021 - 28 Jan 2026

The rapid growth of mobile payment systems has positioned Near Field Communication (NFC) as a core enabling technology. However, conventional NFC protocols primarily emphasize transmission efficiency rather than robust authentication and privacy protection, which exposes users to threats such as eavesdropping, replay, and [...] Read more.

The rapid growth of mobile payment systems has positioned Near Field Communication (NFC) as a core enabling technology. However, conventional NFC protocols primarily emphasize transmission efficiency rather than robust authentication and privacy protection, which exposes users to threats such as eavesdropping, replay, and tracking attacks. In this study, a lightweight and privacy-preserving authentication protocol is proposed for NFC-based mobile payment services. The protocol integrates anonymous authentication, replay resistance, and tracking protection while maintaining low computational overhead suitable for resource-constrained devices. A secure offline session key generation mechanism is incorporated to enhance transaction reliability without increasing system complexity. Formal security verification using the Scyther tool (version 1.1.3) confirms resistance against major attack vectors, including impersonation, man-in-the-middle, and replay attacks. Comparative performance analysis further demonstrates that the proposed scheme achieves superior efficiency and stronger security guarantees compared with existing approaches. These results indicate that the protocol provides a practical and scalable solution for secure and privacy-aware NFC mobile payment environments. Full article

20 pages, 1381 KB

Open AccessSystematic Review

AI-Enhanced Skill Assessment in Higher Vocational Education: A Systematic Review and Meta-Analysis

by Xia Sun and Haoheng Tian

Informatics 2026, 13(2), 20; https://doi.org/10.3390/informatics13020020 - 28 Jan 2026

This study synthesizes empirical evidence on AI-supported skill assessment systems in higher vocational education through a systematic review and meta-analysis. Despite growing interest in generative AI within higher education, empirical research on AI-enabled assessment remains fragmented and methodologically uneven, particularly in vocational contexts. [...] Read more.

This study synthesizes empirical evidence on AI-supported skill assessment systems in higher vocational education through a systematic review and meta-analysis. Despite growing interest in generative AI within higher education, empirical research on AI-enabled assessment remains fragmented and methodologically uneven, particularly in vocational contexts. Following PRISMA 2020 guidelines, 27 peer-reviewed empirical studies published between 2010 and 2024 were identified from major international and Chinese databases and included in the analysis. Using a random-effects model, the meta-analysis indicates a moderate positive association between AI-supported assessment systems and skill-related learning outcomes (Hedges’ g = 0.72), alongside substantial heterogeneity across study designs, outcome measures, and implementation contexts. Subgroup analyses suggest variation across regional and institutional settings, which should be interpreted cautiously given small sample sizes and diverse methodological approaches. Based on the synthesized evidence, the study proposes a conceptual AI-supported skill assessment framework that distinguishes empirically grounded components from forward-looking extensions related to generative AI. Rather than offering prescriptive solutions, the framework provides an evidence-informed baseline to support future research, system design, and responsible integration of generative AI in higher education assessment. Overall, the findings highlight both the potential and the current empirical limitations of AI-enabled assessment, underscoring the need for more robust, theory-informed, and transparent studies as generative AI applications continue to evolve. Full article

(This article belongs to the Special Issue Generative AI in Higher Education: Applications, Implications, and Future Directions)

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38 pages, 6181 KB

Open AccessArticle

An AIoT-Based Framework for Automated English-Speaking Assessment: Architecture, Benchmarking, and Reliability Analysis of Open-Source ASR

by Paniti Netinant, Rerkchai Fooprateepsiri, Ajjima Rukhiran and Meennapa Rukhiran

Informatics 2026, 13(2), 19; https://doi.org/10.3390/informatics13020019 - 26 Jan 2026

The emergence of low-cost edge devices has enabled the integration of automatic speech recognition (ASR) into IoT environments, creating new opportunities for real-time language assessment. However, achieving reliable performance on resource-constrained hardware remains a significant challenge, especially on the Artificial Internet of Things [...] Read more.

The emergence of low-cost edge devices has enabled the integration of automatic speech recognition (ASR) into IoT environments, creating new opportunities for real-time language assessment. However, achieving reliable performance on resource-constrained hardware remains a significant challenge, especially on the Artificial Internet of Things (AIoT). This study presents an AIoT-based framework for automated English-speaking assessment that integrates architecture and system design, ASR benchmarking, and reliability analysis on edge devices. The proposed AIoT-oriented architecture incorporates a lightweight scoring framework capable of analyzing pronunciation, fluency, prosody, and CEFR-aligned speaking proficiency within an automated assessment system. Seven open-source ASR models—four Whisper variants (tiny, base, small, and medium) and three Vosk models—were systematically benchmarked in terms of recognition accuracy, inference latency, and computational efficiency. Experimental results indicate that Whisper-medium deployed on the Raspberry Pi 5 achieved the strongest overall performance, reducing inference latency by 42–48% compared with the Raspberry Pi 4 and attaining the lowest Word Error Rate (WER) of 6.8%. In contrast, smaller models such as Whisper-tiny, with a WER of 26.7%, exhibited two- to threefold higher scoring variability, demonstrating how recognition errors propagate into automated assessment reliability. System-level testing revealed that the Raspberry Pi 5 can sustain near real-time processing with approximately 58% CPU utilization and around 1.2 GB of memory, whereas the Raspberry Pi 4 frequently approaches practical operational limits under comparable workloads. Validation using real learner speech data (approximately 100 sessions) confirmed that the proposed system delivers accurate, portable, and privacy-preserving speaking assessment using low-power edge hardware. Overall, this work introduces a practical AIoT-based assessment framework, provides a comprehensive benchmark of open-source ASR models on edge platforms, and offers empirical insights into the trade-offs among recognition accuracy, inference latency, and scoring stability in edge-based ASR deployments. Full article

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30 pages, 11051 KB

Open AccessArticle

Investigating the Impact of Education 4.0 and Digital Learning on Students’ Learning Outcomes in Engineering: A Four-Year Multiple-Case Study

by Jonathan Álvarez Ariza and Carola Hernández Hernández

Informatics 2026, 13(2), 18; https://doi.org/10.3390/informatics13020018 - 23 Jan 2026

Education 4.0 and digital learning have led to a technology-driven transformation in educational methodologies and the roles of teachers, primarily at Higher Education Institutions (HEIs). From an educational standpoint, the extant literature on Education 4.0 highlights its technological features and benefits; however, there [...] Read more.

Education 4.0 and digital learning have led to a technology-driven transformation in educational methodologies and the roles of teachers, primarily at Higher Education Institutions (HEIs). From an educational standpoint, the extant literature on Education 4.0 highlights its technological features and benefits; however, there is a lack of studies that assess its impact on students’ learning outcomes. Seemingly, Education 4.0 features are taken for granted, as if the technology in itself were enough to guarantee students’ learning, self-efficacy, and engagement. Seeking to address this lack, this study describes the implications of tailoring Education 4.0 tenets and digital learning in an engineering curriculum. Four case studies conducted in the last four years with 119 students are presented, in which technologies such as digital twins, a Modular Production System (MPS), low-cost robotics, 3D printing, generative AI, machine learning, and mobile learning were integrated. With these case studies, an educational methodology with active learning, hands-on activities, and continuous teacher support was designed and deployed to foster cognitive and affective learning outcomes. A mixed-methods study was conducted, utilizing students’ grades, surveys, and semi-structured interviews to assess the approach’s impact. The outcomes suggest that including Education 4.0 tenets and digital learning can enhance discipline-based skills, creativity, self-efficacy, collaboration, and self-directed learning. These results were obtained not only via the technological features but also through the incorporation of reflective teaching that provided several educational resources and oriented the methodology for students’ learning and engagement. The results of this study can help complement the concept of Education 4.0, helping to find a student-centered approach and conceiving a balance between technology, teaching practices, and cognitive and affective learning outcomes. Full article

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40 pages, 1387 KB

Open AccessArticle

Digital Skills and Employer Transparency: Two Key Drivers Reinforcing Positive AI Attitudes and Perception Among Europeans

by Dharan Bharti, Cristian Balducci and Salvatore Zappalà

Informatics 2026, 13(1), 17; https://doi.org/10.3390/informatics13010017 - 22 Jan 2026

Using 2024 Eurobarometer survey data from 26,415 workers in 27 EU countries, this study examines how digital skills and employer transparency shape workers’ attitudes toward and perception of artificial intelligence (AI). Drawing on information systems and behavioral theories, regression analyses reveal that digital [...] Read more.

Using 2024 Eurobarometer survey data from 26,415 workers in 27 EU countries, this study examines how digital skills and employer transparency shape workers’ attitudes toward and perception of artificial intelligence (AI). Drawing on information systems and behavioral theories, regression analyses reveal that digital skills strongly predict augmentation-dominant attitude. Workers with higher digital skills view AI as complementary rather than threatening, with an augmentation attitude mediating 56% of the skills–perception relationship. Adjacently, employer transparency attenuates the translation of replacement attitude into a negative perception of AI in the workplace. Organizations and policymakers should prioritize digital upskilling and ensure workplace AI transparency requirements to foster a positive attitude and perception, recognizing that skills development and organizational communication are equally vital for the successful integration of AI in the workplace. Full article

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21 pages, 1961 KB

Open AccessArticle

Design and Evaluation of a Generative AI-Enhanced Serious Game for Digital Literacy: An AI-Driven NPC Approach

by Suepphong Chernbumroong, Kannikar Intawong, Udomchoke Asawimalkit, Kitti Puritat and Phichete Julrode

Informatics 2026, 13(1), 16; https://doi.org/10.3390/informatics13010016 - 21 Jan 2026

The rapid proliferation of misinformation on social media underscores the urgent need for scalable digital-literacy instruction. This study presents the design and evaluation of a Generative AI-enhanced serious game system that integrates Large Language Models (LLMs) to drive adaptive non-player characters (NPCs). Unlike [...] Read more.

The rapid proliferation of misinformation on social media underscores the urgent need for scalable digital-literacy instruction. This study presents the design and evaluation of a Generative AI-enhanced serious game system that integrates Large Language Models (LLMs) to drive adaptive non-player characters (NPCs). Unlike traditional scripted interactions, the system employs role-based prompt engineering to align real-time AI dialogue with the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) framework, enabling dynamic scaffolding and authentic misinformation scenarios. A mixed-method experiment with 60 undergraduate students compared this AI-driven approach to traditional instruction using a 40-item digital-literacy pre/post test, the Intrinsic Motivation Inventory (IMI), and open-ended reflections. Results indicated that while both groups improved significantly, the game-based group achieved larger gains in credibility-evaluation performance and reported higher perceived competence, interest, and effort. Qualitative analysis highlighted the HCI trade-off between the high pedagogical value of adaptive AI guidance and technical constraints such as system latency. The findings demonstrate that Generative AI can be effectively operationalized as a dynamic interface layer in serious games to strengthen critical reasoning. This study provides practical guidelines for architecting AI-NPC interactions and advances the theoretical understanding of AI-supported educational informatics. Full article

(This article belongs to the Special Issue Generative AI in Higher Education: Applications, Implications, and Future Directions)

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17 pages, 7858 KB

Open AccessArticle

Sensor-Drift Compensation in Electronic-Nose-Based Gas Recognition Using Knowledge Distillation

by Juntao Lin and Xianghao Zhan

Informatics 2026, 13(1), 15; https://doi.org/10.3390/informatics13010015 - 20 Jan 2026

Environmental changes and sensor aging can cause sensor drift in sensor array responses (i.e., a shift in the measured signal/feature distribution over time), which in turn degrades gas classification performance in real-world deployments of electronic-nose systems. Previous studies using the UCI Gas Sensor [...] Read more.

Environmental changes and sensor aging can cause sensor drift in sensor array responses (i.e., a shift in the measured signal/feature distribution over time), which in turn degrades gas classification performance in real-world deployments of electronic-nose systems. Previous studies using the UCI Gas Sensor Array Drift Dataset as a benchmark reported promising drift compensation results but often lacked robust statistical validation and may overcompensate for drift by suppressing class-discriminative variance. To address these limitations and rigorously evaluate improvements in sensor-drift compensation, we designed two domain adaptation tasks based on the UCI electronic-nose dataset: (1) using the first batch to predict remaining batches, simulating a controlled laboratory setting, and (2) using Batches 1 through

n - 1

to predict Batch n, simulating continuous training data updates for online training. Then, we systematically tested three methods—our semi-supervised knowledge distillation method (KD) for sensor-drift compensation; a previously benchmarked method, Domain-Regularized Component Analysis (DRCA); and a hybrid method, KD–DRCA—across 30 random test-set partitions on the UCI dataset. We showed that semi-supervised KD consistently outperformed both DRCA and KD–DRCA, achieving up to 18% and 15% relative improvements in accuracy and F1-score, respectively, over the baseline, proving KD’s superior effectiveness in electronic-nose drift compensation. This work provides a rigorous statistical validation of KD for electronic-nose drift compensation under long-term temporal drift, with repeated randomized evaluation and significance testing, and demonstrates consistent improvements over DRCA on the UCI drift benchmark. Full article

(This article belongs to the Section Machine Learning)

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38 pages, 8329 KB

Open AccessReview

The Validation–Deployment Gap in Agricultural Information Systems: A Systematic Technology Readiness Assessment

by Mary Elsy Arzuaga-Ochoa, Melisa Acosta-Coll and Mauricio Barrios Barrios

Informatics 2026, 13(1), 14; https://doi.org/10.3390/informatics13010014 - 19 Jan 2026

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols [...] Read more.

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80–92%, while blockchain implementations (15.2%) show fast transaction times (<2 s) but limited real-world adoption. Machine learning models using Random Forest, Gradient Boosting, and CNNs reach 85–95% predictive accuracy, and IoT systems report >95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL ≤ 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an “efficiency paradox”: strong technical performance (75–97/100) contrasts with weak economic validation (≤20% include cost–benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation–deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions. Full article

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23 pages, 1548 KB

Open AccessArticle

New Concept of Digital Learning Space for Health Professional Students: Quantitative Research Analysis on Perceptions

by Joshua Mincheol Kim, Provides Tsing Yin Ng, Netaniah Kisha Pinto, Kenneth Chung Hin Lai, Evan Yu Tseng Wu, Olivia Miu Yung Ngan, Charis Yuk Man Li and Florence Mei Kuen Tang

Informatics 2026, 13(1), 13; https://doi.org/10.3390/informatics13010013 - 15 Jan 2026

The Immersive Decentralized Digital space (IDDs), derived from blockchain technology and Massively Multiplayer Online Games (MMOGs), enables real-time multisensory interactions that support social connection under metaverse concepts. Although recognized as a technology with significant potential for educational innovation, IDDs remain underutilized in health [...] Read more.

The Immersive Decentralized Digital space (IDDs), derived from blockchain technology and Massively Multiplayer Online Games (MMOGs), enables real-time multisensory interactions that support social connection under metaverse concepts. Although recognized as a technology with significant potential for educational innovation, IDDs remain underutilized in health professions education. Health profession students are often unaware of how IDDs’ features can be applied to their learning through in- or after-classroom activities. This study employs a quantitative research design to evaluate students’ perceptions of next-generation digital learning without any prior exposure to IDDs. An electronic survey was developed to examine four dimensions of learning facilitation: “Remote Learning” for capturing past experiences with digital competence during the COVID-19 era; “Digital Evolution,” reflecting preferences in utilizing digital spaces; “Interactive Communication” and “Knowledge Application” for applicability of IDDs in the health professions education. Statistical analyses revealed no significant differences in perceptions based on gender or major on all factors. Nevertheless, significant differences emerged based on nationality in “Digital Evolution”, “Interactive Communication”, and “Knowledge Application”, highlighting the influence of cultural and educational backgrounds on receptiveness to virtual learning environments. By recognizing the discrepancies and addressing barriers to digital inclusion, IDDs hold strong potential to enhance health professional learning experiences and educational outcomes. Full article

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21 pages, 4132 KB

Open AccessArticle

Can Location-Based Augmented Reality Support Cultural-Heritage Experience in Real-World Settings? Age-Related Engagement Patterns and a Field-Based Evaluation

by Phichete Julrode, Darin Poollapalin, Sumalee Sangamuang, Kannikar Intawong and Kitti Puritat

Informatics 2026, 13(1), 12; https://doi.org/10.3390/informatics13010012 - 15 Jan 2026

The Wua-Lai silvercraft community in Chiang Mai is experiencing a widening disconnect with younger visitors, raising concerns about the erosion of intangible cultural heritage. This study evaluates “Silver Craft Journey,” a location-based augmented reality (LBAR) system designed to revitalize cultural engagement and enhance [...] Read more.

The Wua-Lai silvercraft community in Chiang Mai is experiencing a widening disconnect with younger visitors, raising concerns about the erosion of intangible cultural heritage. This study evaluates “Silver Craft Journey,” a location-based augmented reality (LBAR) system designed to revitalize cultural engagement and enhance cultural-heritage experience through context-aware, gamified exploration. A quasi-experimental field study with 254 participants across three age groups examined the system’s impact on cultural-heritage experience, knowledge acquisition, and real-world engagement. Results demonstrate substantial knowledge gains, with a mean increase of 7.74 points (SD = 4.37) and a large effect size (Cohen’s d = 1.77), supporting the effectiveness of LBAR in supporting tangible and intangible heritage understanding. Behavioral log data reveal clear age-related engagement patterns: older participants (41–51) showed declining mission completion rates and reduced interaction times at later points of interest, which may reflect increased cognitive and physical demands during extended AR navigation under real-world conditions. These findings underscore the potential of location-based AR to enhance cultural-heritage experience in real-world settings while highlighting the importance of age-adaptive interaction and route-design strategies. The study contributes a replicable model for integrating digital tourism, embodied AR experience, and community-based heritage preservation. Full article

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12 pages, 216 KB

Open AccessBrief Report

Enhancing Interactive Teaching for the Next Generation of Nurses: Generative-AI-Assisted Design of a Full-Day Professional Development Workshop

by Su-I Hou

Informatics 2026, 13(1), 11; https://doi.org/10.3390/informatics13010011 - 15 Jan 2026

Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for [...] Read more.

Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for nursing faculty, senior clinical nurses, and nurse leaders, developed using a design-thinking approach supported by generative AI. Methods: The workshop comprised four thematic sessions: (1) Learning styles across generations, (2) Interactive teaching methods, (3) Application of interactive teaching strategies, and (4) Lesson planning and transfer. Generative AI was used during planning to create icebreakers, discussion prompts, clinical teaching scenarios, and application templates. Design decisions emphasized low-tech, low-prep strategies suitable for spontaneous clinical teaching, thereby reducing barriers to adoption. Activities included emoji-card introductions, quick generational polls, colored-paper reflections, portable whiteboard brainstorming, role plays, fishbowl discussions, gallery walks, and movement-based group exercises. Participants (N = 37) were predominantly female (95%) and represented multiple generations of X, Y, and Z. Mid- and end-of-workshop reflection prompts were embedded within Sessions 2 and 4, with participants recording their responses on colored papers, which were then compiled into a single Word document for thematic analysis. Results: Thematic analysis of 59 mid- and end-workshop reflections revealed six interconnected themes, grouped into three categories: (1) engagement and experiential learning, (2) practical applicability and generational awareness, and (3) facilitation, environment, and motivation. Participants emphasized the workshop’s lively pace and hands-on design. Experiencing strategies firsthand built confidence for application, while generational awareness encouraged reflection on adapting methods for younger learners. The facilitator’s passion, personable approach, and structured use of peer learning created a psychologically safe and motivating climate, leaving participants recharged and inspired to integrate interactive methods. Discussion: The workshop illustrates how AI-assisted, design-thinking-driven professional development can model effective strategies for next-generation learners. When paired with skilled facilitation, AI-supported planning enhances engagement, fosters reflective practice, and promotes immediate transfer of interactive strategies into diverse teaching settings. Full article

(This article belongs to the Special Issue Generative AI in Higher Education: Applications, Implications, and Future Directions)

26 pages, 1167 KB

Open AccessReview

A Review of Multimodal Sentiment Analysis in Online Public Opinion Monitoring

by Shuxian Liu and Tianyi Li

Informatics 2026, 13(1), 10; https://doi.org/10.3390/informatics13010010 - 14 Jan 2026

With the rapid development of the Internet, online public opinion monitoring has emerged as a crucial task in the information era. Multimodal sentiment analysis, through the integration of multiple modalities such as text, images, and audio, combined with technologies including natural language processing [...] Read more.

With the rapid development of the Internet, online public opinion monitoring has emerged as a crucial task in the information era. Multimodal sentiment analysis, through the integration of multiple modalities such as text, images, and audio, combined with technologies including natural language processing and computer vision, offers novel technical means for online public opinion monitoring. Nevertheless, current research still faces many challenges, such as the scarcity of high-quality datasets, limited model generalization ability, and difficulties with cross-modal feature fusion. This paper reviews the current research progress of multimodal sentiment analysis in online public opinion monitoring, including its development history, key technologies, and application scenarios. Existing problems are analyzed and future research directions are discussed. In particular, we emphasize a fusion-architecture-centric comparison under online public opinion monitoring, and discuss cross-lingual differences that affect multimodal alignment and evaluation. Full article

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30 pages, 4733 KB

Open AccessArticle

Knowledge Organization of Buddhist Learning Resources for Tourism: Virtual Tour of Wat Phra Pathom Chedi

by Bulan Kulavijit, Wirapong Chansanam, Kannikar Intawong and Kitti Puritat

Informatics 2026, 13(1), 9; https://doi.org/10.3390/informatics13010009 - 13 Jan 2026

This study curates and structures knowledge concerning Buddhist learning resources for tourism, presenting it through a virtual tour of Wat Phra Pathom Chedi Ratchaworamahawihan in Nakhon Pathom Province. Employing a mixed-methods approach that integrates both qualitative and quantitative methodologies, the research first establishes [...] Read more.

This study curates and structures knowledge concerning Buddhist learning resources for tourism, presenting it through a virtual tour of Wat Phra Pathom Chedi Ratchaworamahawihan in Nakhon Pathom Province. Employing a mixed-methods approach that integrates both qualitative and quantitative methodologies, the research first establishes a structured knowledge base. This involves developing a comprehensive metadata schema for cataloging the temple’s diverse resources, including both sacred sites and artifacts, to enhance their searchability and accessibility. Subsequently, this knowledge is rendered into a virtual tour, which serves as an exemplary model of a Buddhist digital learning resource for tourism. The findings reveal the extensive diversity of resources within the temple. The developed virtual tour platform allows users an immersive exploration of the site via 360-degree panoramic views. This research presents significant implications for relevant agencies, offering a scalable model for the digital dissemination of cultural heritage. It is anticipated that this initiative will expand global access to and appreciation of the temple’s cultural value, thereby fostering international interest in visitation. Such engagement is poised to stimulate the local economy and bolster Thailand’s image as a premier cultural tourism destination. Full article

(This article belongs to the Special Issue Real-World Applications and Prototyping of Information Systems for Extended Reality (VR, AR, and MR))

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16 pages, 947 KB

Open AccessArticle

Depression Detection Method Based on Multi-Modal Multi-Layer Collaborative Perception Attention Mechanism of Symmetric Structure

by Shaorong Jiang, Chengjun Xu and Xiuya Fang

Informatics 2026, 13(1), 8; https://doi.org/10.3390/informatics13010008 - 12 Jan 2026

Depression is a mental illness with hidden characteristics that affects human physical and mental health. In severe cases, it may lead to suicidal behavior (for example, among college students and social groups). Therefore, it has attracted widespread attention. Scholars have developed numerous models [...] Read more.

Depression is a mental illness with hidden characteristics that affects human physical and mental health. In severe cases, it may lead to suicidal behavior (for example, among college students and social groups). Therefore, it has attracted widespread attention. Scholars have developed numerous models and methods for depression detection. However, most of these methods focus on a single modality and do not consider the influence of gender on depression, while the existing models have limitations such as complex structures. To solve this problem, we propose a symmetric-structured, multi-modal, multi-layer cooperative perception model for depression detection that dynamically focuses on critical features. First, the double-branch symmetric structure of the proposed model is designed to account for gender-based variations in emotional factors. Second, we introduce a stacked multi-head attention (MHA) module and an interactive cross-attention module to comprehensively extract key features while suppressing irrelevant information. A bidirectional long short-term memory network (BiLSTM) module enhances depression detection accuracy. To verify the effectiveness and feasibility of the model, we conducted a series of experiments using the proposed method on the AVEC 2014 dataset. Compared with the most advanced HMTL-IMHAFF model, our model improves the accuracy by 0.0308. The results indicate that the proposed framework demonstrates superior performance. Full article

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30 pages, 1341 KB

Open AccessArticle

A Novel MBPSO–BDGWO Ensemble Feature Selection Method for High-Dimensional Classification Data

by Nuriye Sancar

Informatics 2026, 13(1), 7; https://doi.org/10.3390/informatics13010007 - 12 Jan 2026

In a high-dimensional classification dataset, feature selection is crucial for improving classification performance and computational efficiency by identifying an informative subset of features while reducing noise, redundancy, and overfitting. This study proposes a novel metaheuristic-based ensemble feature selection approach by combining the complementary [...] Read more.

In a high-dimensional classification dataset, feature selection is crucial for improving classification performance and computational efficiency by identifying an informative subset of features while reducing noise, redundancy, and overfitting. This study proposes a novel metaheuristic-based ensemble feature selection approach by combining the complementary strengths of Modified Binary Particle Swarm Optimization (MBPSO) and Binary Dynamic Grey Wolf Optimization (BDGWO). The proposed MBPSO–BDGWO ensemble method is specifically designed for high-dimensional classification problems. The performance of the proposed MBPSO–BDGWO ensemble method was rigorously evaluated through an extensive simulation study under multiple high-dimensional scenarios with varying correlation structures. The ensemble method was further validated on several real datasets. Comparative analyses were conducted against single-stage feature selection methods, including BPSO, BGWO, MBPSO, and BDGWO, using evaluation metrics such as accuracy, the F1-score, the true positive rate (TPR), the false positive rate (FPR), the AUC, precision, and the Jaccard stability index. Simulation studies conducted under various dimensionality and correlation scenarios show that the proposed ensemble method achieves a low FPR, a high TPR/Precision/F1/AUC, and strong selection stability, clearly outperforming both classical and advanced single-stage methods, even as dimensionality and collinearity increase. In contrast, single-stage methods typically experience substantial performance degradation in high-correlation and high-dimensional settings, particularly BPSO and BGWO. Moreover, on the real datasets, the ensemble method outperformed all compared single-stage methods and produced consistently low MAD values across repetitions, indicating robustness and stability even in ultra-high-dimensional genomic datasets. Overall, the findings indicate that the proposed ensemble method demonstrates consistent performance across the evaluated scenarios and achieves higher selection stability compared with the single-stage methods. Full article

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17 pages, 1585 KB

Open AccessReview

Second-Opinion Systems for Rare Diseases: A Scoping Review of Digital Workflows and Networks

by Vinícius Lima, Mariana Mozini and Domingos Alves

Informatics 2026, 13(1), 6; https://doi.org/10.3390/informatics13010006 - 10 Jan 2026

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe [...] Read more.

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe how second-opinion services for rare diseases are organized and governed, to characterize technological and workflow models, to summarize benefits and barriers, and to identify priority evidence gaps for implementation. Methods: Using a population–concept–context approach, we included peer-reviewed studies describing implemented second-opinion systems for rare diseases and excluded isolated case reports, purely conceptual proposals, and work outside this focus. Searches in August 2025 covered PubMed/MEDLINE, Scopus, Web of Science Core Collection, Cochrane Library, IEEE Xplore, ACM Digital Library, and LILACS without date limits and were restricted to English, Portuguese, or Spanish. Two reviewers screened independently, and the data were charted with a standardized, piloted form. No formal critical appraisal was undertaken, and the synthesis was descriptive. Results: Initiatives were clustered by scale (European networks, national programs, regional systems, international collaborations) and favored hybrid models over asynchronous and synchronous ones. Across settings, services shared reproducible workflows and provided faster access to expertise, quicker decision-making, and more frequent clarification of care plans. These improvements were enabled by transparent governance and dedicated support but were constrained by platform complexity, the effort required to assemble panels, uneven incentives, interoperability gaps, and medico-legal uncertainty. Conclusions: Systematized second-opinion services for rare diseases are feasible and clinically relevant. Progress hinges on usability, aligned incentives, and pragmatic interoperability, advancing from registries toward bidirectional electronic health record connections, alongside prospective evaluations of outcomes, equity, experience, effectiveness, and costs. Full article

(This article belongs to the Section Health Informatics)

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15 pages, 5247 KB

Open AccessArticle

Visual Harmony Between Avatar Appearance and On-Avatar Text: Effects on Self-Expression Fit and Interpersonal Perception in Social VR

by Yang Guang, Sho Sakurai, Takuya Nojima and Koichi Hirota

Informatics 2026, 13(1), 5; https://doi.org/10.3390/informatics13010005 - 7 Jan 2026

In social virtual reality (VR) and metaverse platforms, users express their identity through both avatar appearance and on-avatar textual cues, such as speech balloons. However, little is known about how the harmony between these cues influences self-representation and social impressions. We propose that [...] Read more.

In social virtual reality (VR) and metaverse platforms, users express their identity through both avatar appearance and on-avatar textual cues, such as speech balloons. However, little is known about how the harmony between these cues influences self-representation and social impressions. We propose that when avatar appearance and text design, including color, font, and tone, are consistent, users experience a stronger self-expression fit and elicit greater interpersonal affinity. A within-subject study (

N = 21

) in VRChat manipulated the social context, color harmony between avatar hair and text, and style or content consistency between tone and font. Questionnaires provided composite indices for perceived congruence, self-expression fit, and affinity. Analyses included repeated-measures ANOVA, linear mixed-effects models, and mediation tests. Results showed that congruent pairings increased both self-expression fit and affinity compared to mismatches, with mediation analyses indicating that self-expression fit fully mediated the effect. These findings integrate theories of avatar influence and computer-mediated communication into a framework for metaverse design, highlighting the value of consistent avatar and text styling. Full article

(This article belongs to the Section Human-Computer Interaction)

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