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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 25553

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Special Issue Editors

Dr. Alexandros Kleftodimos

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Guest Editor

Department of Communication and Digital Media, University of Western Macedonia, 52100 Kastoria, Greece
Interests: educational technologies; digital media in communication; virtual and augmented reality; audiovisual content production

Dr. Maria Matsiola

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Guest Editor

Department of Communication and Digital Media, University of Western Macedonia, 52100 Kastoria, Greece
Interests: communication studies; new media technologies and studies; audiovisual content management
Special Issues, Collections and Topics in MDPI journals

Dr. Michalis Vrigkas

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Guest Editor

Department of Communication and Digital Media, University of Western Macedonia, 52100 Kastoria, Greece
Interests: virtual and augmented reality; computer vision; image and video processing; image analysis; machine learning; pattern recognition with applications in medical image analysis and biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digital media landscape is undergoing a deep transformation, driven by rapid technological advancements. In today's world, digital media is an integral part of our lives, shaping the way we conduct a broad range of daily activities, including communication, information consumption, entertainment, and interaction with one another. The constant evolution of social media platforms, the proliferation of audiovisual streaming services, the advancements in immersive technologies, and the appearance of generative AI tools profoundly impact the way we create, distribute, and engage with digital content.

This Special Issue aims to explore the cutting-edge technologies shaping the future of digital media. It will investigate how innovations such as artificial intelligence, virtual and augmented reality, big data analytics, 5G technology, IoT, and blockchain are being applied to enhance and revolutionize the digital media experience. Furthermore, it will also examine current advancements in audiovisual production, such as virtual production, interactive media, transmedia storytelling, AI in content creation, etc.

This Special Issue will investigate these advancements to provide a comprehensive understanding of their impact on content creation, distribution strategies, user engagement, and overall media consumption.

Topics of interest include, but are not limited to, the following:

Artificial Intelligence and Machine Learning: Applications in content creation, personalization, and predictive analytics.
Augmented Reality (AR) and Virtual Reality (VR): Innovations in immersive media experiences and their implications for entertainment, news consumption, and education.
Big Data and Analytics: Utilization of large datasets in data-driven media strategies (e.g., audience segmentation, content personalization, marketing and promotion, predicting media success, script development in audiovisual productions, etc.)
Blockchain Technology: Enhancing transparency, security, and rights management in digital media transactions.
Internet of Things (IoT): Integration of smart devices in audiovisual content production environments and digital communication platforms.
5G and Beyond: Impact of next-generation connectivity on media distribution, live streaming, and real-time interaction.
Human–Computer Interaction: Advancements in interfaces, user experience design, and accessibility in digital media platforms.
Digital Ethics and Privacy: Addressing ethical considerations and privacy concerns in the deployment of advanced technologies.

Dr. Alexandros Kleftodimos
Dr. Maria Matsiola
Dr. Michalis Vrigkas
Guest Editors

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Keywords

digital media
artificial intelligence
augmented reality
virtual reality
Internet of Things (IoT)
big data analytics
digital content creation
multimedia systems
interactive media
streaming technologies
blockchain
5G technology
user experience

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Published Papers (10 papers)

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Research

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35 pages, 3992 KB

Open AccessArticle

Extended Reality Applications in Environmental Education: A Field Learning Approach to Understanding Lake Ecosystems

by Athanasios Evagelou and Alexandros Kleftodimos

Appl. Sci. 2026, 16(8), 3651; https://doi.org/10.3390/app16083651 - 8 Apr 2026

Viewed by 205

Abstract

This study examines the design and pedagogical evaluation of Extended Reality (XR) applications, with a primary focus on location-based Augmented Reality (AR). The XR applications were implemented within an environmental education program delivered by the Education Center for the Environment and Sustainability (E.S.E.C.) of Kastoria, aiming to enhance students’ understanding of lake ecosystems and environmental awareness through immersive, situated learning experiences. The development followed the ADDIE instructional design framework and was grounded in principles of experiential and situated learning. The educational intervention was conducted in an authentic field setting along the shoreline of Lake Kastoria and combined location-based AR activities with complementary immersive VR experiences. Evaluation data were collected through a structured questionnaire administered to 271 primary and secondary school students, employing XR-relevant constructs including Challenge/Satisfaction/Enjoyment, Ease of Use, Usefulness/Knowledge, Experiential and Situated Learning, Interaction/Collaboration, and Intention to Reuse. In addition, accompanying teachers provided supplementary qualitative feedback to support the interpretation of the findings under authentic field conditions. Descriptive statistical analysis indicated consistently high scores across all constructs (M = 3.27–4.40, SD = 0.41–0.64). Pearson correlation analysis revealed strong associations between Experiential/Situated Learning and Usefulness/Knowledge (r = 0.737), Experiential/Situated Learning and Challenge/Satisfaction/Enjoyment (r = 0.642), Intention to Reuse and Challenge/Satisfaction/Enjoyment (r = 0.635), as well as Usefulness/Knowledge and Challenge/Satisfaction/Enjoyment (r = 0.619). Multiple regression analyses further supported key relationships, including Usefulness/Knowledge as a predictor of Experiential/Situated Learning (β = 0.57, p < 0.001), Experiential/Situated Learning as a predictor of Challenge/Satisfaction/Enjoyment (β = 0.47, p < 0.001), and Interaction/Collaboration as a predictor of Intention to Reuse (β = 0.31, p < 0.001). Intention to reuse was mainly associated with interaction and collaboration, enjoyment and motivation, perceived usefulness/knowledge, and ease of use. Overall, the findings indicate that XR-supported outdoor learning is positively associated with key experiential, emotional, social, and perceived learning dimensions when embedded within a coherent pedagogical framework. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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14 pages, 1329 KB

Open AccessArticle

Differential Effects of Desktop and Immersive Virtual Reality on Learning, Cognitive Load and Attitudes of University Students

by Julio Cabero-Almenara, Mª Victoria Fernández-Scagliusi, Antonio Palacios-Rodríguez and Rocío Piñero-Virué

Appl. Sci. 2026, 16(7), 3595; https://doi.org/10.3390/app16073595 - 7 Apr 2026

Viewed by 624

Abstract

Virtual reality (VR) has emerged as a technology with growing presence in education, driven by its potential to increase motivation, promote learning, and offer immersive experiences that are challenging to replicate in traditional settings. However, the literature shows contradictory results regarding its impact on academic performance, cognitive load, and student attitudes, particularly when comparing immersive and non-immersive (desktop) modalities. Against this backdrop, this study aimed to examine whether interaction with VR-based learning objects improves knowledge acquisition, whether differences exist between immersive and desktop versions, what cognitive load is associated with each modality, and what attitudes students develop toward VR. A total of 136 Education students participated, randomly assigned to either the immersive (n = 70) or non-immersive (n = 66) condition, following a pretest–posttest experimental design. Data were collected using a performance test, the NASA-TLX questionnaire, and a semantic differential scale. Results indicated significant improvements in learning across both modalities with no statistically significant differences between them, a slightly higher—yet low-to-moderate—cognitive load in the immersive condition, and highly positive attitudes in both groups. These findings suggest that both modalities are effective and well accepted, although immersive VR requires somewhat greater cognitive effort. The discussion highlights the need to clarify the factors that moderate these effects and to advance theoretical frameworks for instructional design in VR environments. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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18 pages, 1475 KB

Open AccessArticle

Defining Abusive News Categories: Proposing a Detection Model for Digital Media Integrity

by Munsu Choi, Dohwan Kim and Jonghyuk Kim

Appl. Sci. 2026, 16(7), 3190; https://doi.org/10.3390/app16073190 - 26 Mar 2026

Viewed by 299

Abstract

Abusive news refers to digital content designed to maximize clicks and advertising revenue through sensational headlines, repetitive postings, or emotionally charged language, rather than upholding journalistic integrity. Despite growing concerns about its impact on media credibility and public trust, existing detection approaches lack systematic categorization and type-specific methodologies. This study addresses this gap by proposing a six-type typology of abusive news—content recycling, keyword insertion, title–body inconsistency, commercial promotion, emotionally stimulating headline, and automatically generated types—based on five analytical dimensions: content structure, authenticity, algorithmic manipulability, sensationalism, and information-ecosystem impact. We developed type-specific detection pipelines combining BERT-based embeddings, TF-IDF features, and rule-based indicators and evaluated them using a large-scale Korean clickbait corpus. Results demonstrate that BERT achieves higher F1-scores (0.89) for automatically generated content, while TF-IDF with SVM provides more stable precision (0.60) for emotionally charged articles under class imbalance. Cross-domain experiments confirm that models trained on diverse, balanced topic sets generalize better than volume-focused models, with diversity improving F1-scores by up to 0.07. BERT models show higher false positive rates on repetitive legitimate content compared to TF-IDF approaches, highlighting the importance of type-adaptive architectures and diversity-aware data design in abusive news detection systems. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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28 pages, 1665 KB

Open AccessArticle

The Use of Social Media as Bibliographic Citations in Open Access Education Journals

by Dimitris Rousidis, Emmanouel Garoufallou, Paraskevas Koukaras, Ilias Nitsos and Christos Tjortjis

Appl. Sci. 2026, 16(6), 3095; https://doi.org/10.3390/app16063095 - 23 Mar 2026

Viewed by 318

Abstract

There has been a recent increase in the use of social media platforms (SMPs), as well as a large increase in scientific journals and academic article publications. We need to study if and how much academics, scholars and researchers trust SMPs as sources, i.e., citations, for writing their research articles. The purpose of this research is to explore the relationship between SMPs and bibliographic article citations for ten years between 2010 and 2019, with 31 December marking the official identification of COVID-19, a milestone that affected the whole world, including academic publishing. By using a citation retrieval tool written in Java, the citations referring to the URLs of 6432 articles from 14 Q1 open access education journals ranked by the SCImago platform were extracted. The retrieved URLs were stored in a relational database, preprocessed and cleaned, and analyzed using SQL queries to identify and quantify citations originating from SMPs. The findings showed that there were 112 instances, which corresponds to 1.8% of the articles, of an SMP post being used as a citation. Out of the 17 SMPs checked, eight were used, with the most popular being YouTube, having a percentage of 68% of the aforementioned 112 citations, followed by Twitter (now X) with approximately 13.5% and then by Facebook with around 7%. Most of these in-text citations were found at the Introduction and the Design/Methodology sections of the papers. Other important findings of this study were that about 2% of the URL citations referred to blogs and wikis and that one in 100 articles used Wikipedia in the bibliography. Also, for a 26-year period from 1999 to 2024, it was observed that the number of journals increased by 82.8%, while the number of open access journals showed an impressive 552.14% increase. The findings of this study could lead to changes in the metadata design of bibliographic databases, like the way of searching them, and to a review of the life cycle duration of sustainable access to the content of the cited SMPs. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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14 pages, 1535 KB

Open AccessArticle

Artificial Intelligence, Algorithmic Ethics, and Digital Inequality: A Bibliometric Mapping in the Digital Media Era

by Soledad Zabala, José Javier Galán Hernández, Jesús Cáceres-Tello, Eloy López-Meneses and María Belén Morales Cevallos

Appl. Sci. 2026, 16(6), 3056; https://doi.org/10.3390/app16063056 - 22 Mar 2026

Viewed by 505

Abstract

The accelerated expansion of advanced technologies—particularly artificial intelligence, intelligent systems, and interactive digital environments—is influencing contemporary media ecosystems and contributing to changes in educational practices. This study provides a systematic and descriptive bibliometric mapping of recent scientific production on artificial intelligence in education, algorithmic ethics, and digital inequality. A total of 229 Scopus-indexed documents published between 2021 and 2026 were analyzed using Biblioshiny and VOSviewer to examine publication patterns, influential authors and sources, and the conceptual structure of the field. Results indicate a marked increase in research output since 2024, with an annual growth rate of 47.58%, an average of 8.68 citations per document, and an international co-authorship rate of 24.45%. These indicators reflect an expanding and increasingly collaborative research landscape, accompanied by a diversification of thematic priorities within the field. The analysis identifies five thematic clusters: (1) the technical foundations of AI and digital transformation; (2) intelligent and immersive learning environments; (3) personalized and adaptive learning systems; (4) AI literacy and pedagogical integration; and (5) ethical considerations, including algorithmic bias and educational robotics. The findings highlight the need for explicit justification of database selection, strengthened critical AI literacy, and context-sensitive strategies that address disparities in access, skills, and institutional capacity. Overall, this study offers a coherent overview of a research area that is currently expanding and undergoing conceptual reorganization, providing evidence-informed insights for future research, policy development, and the design of equitable AI-driven educational technologies. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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32 pages, 2529 KB

Open AccessArticle

Cloud Adoption in the Digital Era: An Interpretable Machine Learning Analysis of National Readiness and Structural Disparities Across the EU

by Cristiana Tudor, Margareta Florescu, Persefoni Polychronidou, Pavlos Stamatiou, Vasileios Vlachos and Konstadina Kasabali

Appl. Sci. 2025, 15(14), 8019; https://doi.org/10.3390/app15148019 - 18 Jul 2025

Cited by 1 | Viewed by 3790

Abstract

As digital transformation accelerates across Europe, cloud computing plays an increasingly central role in modernizing public services and private enterprises. Yet adoption rates vary markedly among EU member states, reflecting deeper structural differences in digital capacity. This study employs explainable machine learning to uncover the drivers of national cloud adoption across 27 EU countries using harmonized panel datasets spanning 2014–2021 and 2014–2024. A methodological pipeline combining Random Forests (RF), XGBoost, Support Vector Machines (SVM), and Elastic Net regression is implemented, with model tuning conducted via nested cross-validation. Among individual models, Elastic Net and SVM delivered superior predictive performance, while a stacked ensemble achieved the best overall accuracy (MAE = 0.214, R² = 0.948). The most interpretable model, a standardized RF with country fixed effects, attained MAE = 0.321, and R² = 0.864, making it well-suited for policy analysis. Variable importance analysis reveals that the density of ICT specialists is the strongest predictor of adoption, followed by broadband access and higher education. Fixed-effect modeling confirms significant national heterogeneity, with countries like Finland and Luxembourg consistently leading adoption, while Bulgaria and Romania exhibit structural barriers. Partial dependence and SHAP analyses reveal nonlinear complementarities between digital skills and infrastructure. A hierarchical clustering of countries reveals three distinct digital maturity profiles, offering tailored policy pathways. These results directly support the EU Digital Decade’s strategic targets and provide actionable insights for advancing inclusive and resilient digital transformation across the Union. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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17 pages, 604 KB

Open AccessArticle

Prediction of the Use of Generative Artificial Intelligence Through ChatGPT Among Costa Rican University Students: A PLS Model Based on UTAUT2

by Julio Cabero-Almenara, Antonio Palacios-Rodríguez, Hazel de los Ángeles Rojas Guzmán and Victoria Fernández-Scagliusi

Appl. Sci. 2025, 15(6), 3363; https://doi.org/10.3390/app15063363 - 19 Mar 2025

Cited by 20 | Viewed by 5480

Abstract

The rise in generative artificial intelligence (GenAI) is transforming education, with tools like ChatGPT enhancing learning, content creation, and academic support. This study analyzes ChatGPT’s acceptance among Costa Rican university students using the UTAUT2 model and partial least squares structural equation modeling (PLS-SEM). The research examines key predictors of AI adoption, including performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and actual usage. The findings from 194 students indicate that performance expectancy (β = 0.596, p < 0.001) is the strongest predictor of behavioral intention, followed by effort expectancy (β = 0.241, p = 0.005), while social influence (β = 0.381, p < 0.001) and facilitating conditions (β = 0.217, p = 0.008) play a smaller role. Behavioral intention significantly influences actual usage (β = 0.643, p < 0.001). Gender and age differences emerge, with male students and those aged 21–30 years showing higher acceptance levels. Despite positive attitudes toward ChatGPT, the students report insufficient training for effective use, underscoring the need for AI literacy programs and structured pedagogical strategies. This study calls for further research on AI training programs and their long-term impact on academic performance to foster responsible GenAI adoption in higher education. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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38 pages, 1609 KB

Open AccessArticle

Advanced Technologies from and Through a Prototype Radio Lesson with Budding Journalists: The Views and Attitudes of Xers, Millennials, and Zoomers

by Constantinos Nicolaou

Appl. Sci. 2025, 15(2), 588; https://doi.org/10.3390/app15020588 - 9 Jan 2025

Viewed by 2481

Abstract

In this research article, budding journalists’ views and attitudes toward a prototype radio lesson that was designed and implemented utilizing advanced technologies are explored. These budding journalists hail from Greece, were studying in higher education or adult education at the time of the survey, and belong to one of the three youngest generational cohorts within adults (i.e., 18 years old and older); Generation X where their members are now known as Xers, Generation Y where their members are already known as Millennials, and Generation Z where their members are also known as Zoomers—XYZ Generations. The survey’s research data were collected after six interactive teachings from and through advanced technologies, via a specially designed written questionnaire, as case study experiments that applied qualitative action research with quasi-experiments. Analysis, descriptive, and inductive statistics were then applied to them from and through Internet applications and services. This is essentially empirical qualitative research that incorporates the approach of the quantitative analysis process. The resulting survey findings and results mainly contribute to the quality of higher education and adult education, especially in the field of media studies. Likewise, they align with or confirm previous contemporary empirical studies and research papers on the genealogical characteristics, habits, and ethos of the XYZ Generations, and particularly regarding the similarities among Xers and Zoomers, as well as that Generation Z can also be characterized as a “Sound Generation”. An important conclusion of this research article is that the success of a lesson or even a lecture seems to depend (a) on the good design and/or development of the lesson plan itself, as well as (b) on the communication classroom where the educational praxis takes place. Finally, these survey findings and results are also considered encouraging and could be utilized (a) for any audiovisual-supported teaching–learning procedure for adult learners; (b) across various educational levels and disciplines; or even (c) for members other generational cohorts. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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Review

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19 pages, 4128 KB

Open AccessReview

When Robots Learn: A Bibliometric Review of Artificial Intelligence in Engineering Applications of Robotics

by Eduardo García-Sardón, Pablo Fernández-Arias, Antonio del Bosque and Diego Vergara

Appl. Sci. 2026, 16(5), 2466; https://doi.org/10.3390/app16052466 - 4 Mar 2026

Viewed by 500

Abstract

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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38 pages, 6313 KB

Open AccessReview

Learning Analytics and Educational Data Mining in Augmented Reality, Virtual Reality, and the Metaverse: A Systematic Literature Review, Content Analysis, and Bibliometric Analysis

by Georgios Lampropoulos and Georgios Evangelidis

Appl. Sci. 2025, 15(2), 971; https://doi.org/10.3390/app15020971 - 20 Jan 2025

Cited by 20 | Viewed by 9715

Abstract

This study aims to examine the combination of educational data mining and learning analytics with virtual reality, augmented reality, mixed reality, and the metaverse, its role in education, and its impact on teaching and learning. Therefore, a systematic literature review, a bibliometric and scientific mapping analysis, and a content analysis are carried out based on 70 relevant documents identified from six databases, namely, ACM, ERIC, IEEE, ScienceDirect, Scopus, and Web of Science (WoS) following the PRISMA framework. The documents were separated into the following three categories, (i) Theoretical and Review studies, (ii) Proposal and Showcase studies, and (iii) Experimental and Case studies and were examined from different dimensions through an in-depth content analysis using both quantitative and qualitative approaches. The documents were further analyzed using scientometric tools, such as Bibliometrix and VOSviewer and topic modeling through Latent Dirichlet Allocation (LDA). The most prominent topics, areas, and themes were revealed and the outcomes regarding the influence of this combination on learning and teaching were summarized. Based on the results, this combination can effectively enrich education, positively affect learning and teaching, offer deep and meaningful learning, and support both students and teachers. Additionally, it can support different educational approaches and strategies, various learning styles, and special education and be utilized in both formal and informal learning environments. The real-time identification, tracking, monitoring, analysis, and visualization of multimodal learning data of students’ behavior, emotions, cognitive and affective states and the overall learning and teaching processes emerged as a significant benefit that contributes greatly to the realization of adaptive and personalized learning. Finally, it was revealed that the combination of extended reality technologies with learning analytics and educational data mining can support collaborative learning and social learning, improve students’ self-efficacy and self-regulated learning, and increase students’ learning gains, academic achievements, knowledge retention, motivation, and engagement. Full article

(This article belongs to the Special Issue Advanced Technologies Applied in Digital Media Era)

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