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        <title>Frontiers in Computer Science | Digital Education section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/computer-science/sections/digital-education</link>
        <description>RSS Feed for Digital Education section in the Frontiers in Computer Science journal | New and Recent Articles</description>
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        <pubDate>2026-05-11T10:04:34.216+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1770049</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1770049</link>
        <title><![CDATA[Redefining learning strategies in SRL for student’s achievements in flipped classrooms]]></title>
        <pubdate>2026-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Monica Maiti</author><author>M. Priyaadharshini</author>
        <description><![CDATA[Flipped classrooms require learners to actively regulate their learning processes, yet the relationships among self-regulated learning (SRL), engagement, social–emotional intelligence (SEI), and academic performance remain insufficiently integrated within learning analytics research. This study examined 96 third-year Computer Science and Engineering students enrolled in a semester-long Database Management Systems course implementing a flipped SRL framework. A mixed-methods approach was used to analyse academic performance across Continuous Assessment Tests 1 and 2 (CAT 1, CAT 2) and Final Assessment Test (FAT), engagement analytics from Microsoft Teams, SEI survey responses, affective indicators through Reflect app and machine learning models to explore associative and predictive relationships among these constructs. Results indicated strong positive associations between SRL behaviours, engagement, and final assessment outcomes, with engagement partially explaining the relationship between SRL and performance. Correlation and clustering analyses revealed alignment among self-regulation, cognitive engagement, and emotional competencies, while predictive modelling (XGBoost, R2 = 0.83) demonstrated that SRL-related indicators effectively model academic performance patterns. Overall, the findings provide theoretically informed evidence of meaningful associations among cognitive, behavioural, and emotional regulation processes in flipped learning environments, highlighting the value of integrating SRL theory with learning analytics for data-informed instructional design in higher education.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1798475</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1798475</link>
        <title><![CDATA[Artificial intelligence in physical examination teaching in Latin America: a critical narrative review and conceptual model proposal]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ariel Torres</author><author>Paloma González</author><author>Martha Fors</author><author>Gisselle Trujillo</author>
        <description><![CDATA[Artificial intelligence (AI) is reshaping medical education, particularly in the teaching of physical examination and the development of clinical judgement in digitally mediated contexts. This study presents a critical narrative review examining the ethical, pedagogical, and humanistic implications of AI integration into physical examination training in Latin America. A structured search of literature published between 2018 and 2025 was conducted across PubMed, Scopus, Web of Science, SciELO, and Google Scholar. Thirty-one peer-reviewed studies and three institutional documents met predefined relevance criteria and were analyzed through thematic synthesis. Four thematic domains emerged: (1) AI-assisted clinical simulation and automated feedback, (2) curricular integration and institutional implementation strategies, (3) governance and ethical supervision frameworks, and (4) emerging challenges related to digital literacy, technological dependence, and preservation of clinical judgement. Evidence suggests that AI enhances procedural precision and formative feedback; however, its educational value remains complementary and dependent on structured human-in-the-loop supervision. Based on these findings, the Modelo Educativo Digital basado en Inteligencia Artificial (or Medical Education with Artificial Intelligence) (MED-IA) conceptual model is proposed, framing clinical competence development across three interconnected levels: technical execution, experiential patient interaction, and reflective judgement. The model integrates technological mediation with ethical oversight and humanistic formation. These findings highlight the need for transparent governance frameworks, teacher digital literacy, and context-sensitive institutional policies to ensure responsible AI implementation in Latin American medical education.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780150</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1780150</link>
        <title><![CDATA[Multimodal AI in education: an avatar-based intelligent learning system for the Kazakh language]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Aru Ukenova</author><author>Gulmira Bekmanova</author><author>Banu Yergesh</author><author>Sadok Ben Yahia</author><author>Mamyr Altaibek</author><author>Aizhan Nazyrova</author><author>Zhanar Lamasheva</author>
        <description><![CDATA[This article describes the development of a multimodal learning system for the Kazakh language intended for digital educational environments. The study focuses on the lack of avatar-based learning systems adapted to the linguistic properties of the Kazakh language and the limited integration of verbal and non-verbal components in existing solutions. The proposed system combines syntactic and morphological text analysis with sentiment processing and intonation control. Speech synthesis, gesture generation, facial expression control, and lip synchronization are implemented within a single system architecture. Prosodic parameters are formed based on sentence structure and sentence-level emotional indicators, while visual articulation is synchronized with audio output. The system was tested in speech synthesis scenarios relevant to interactive educational use. The results show that the system can be used for automated lecture narration, voice-over of instructional materials, and basic learner interaction in avatar-based educational settings.]]></description>
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        <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.2026.1813431</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.2026.1798362</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1798362</link>
        <title><![CDATA[Correction: AI-driven framework for automated competency formalization: from professional standards to adaptive learning outcomes]]></title>
        <pubdate>2026-02-24T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Ainur Mukashova</author><author>Jamalbek Tussupov</author><author>Sandugash Serikbayeva</author><author>Ayagoz Mukhanova</author><author>Muslim Sergaziyev</author><author>Madina Sambetbayeva</author><author>Aigerim Yerimbetova</author><author>Zhanar Lamasheva</author><author>Zhanna Sadirmekova</author><author>Valiya Ramazanova</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1729059</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1729059</link>
        <title><![CDATA[Artificial intelligence in educational assignments: issues of academic integrity]]></title>
        <pubdate>2026-02-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maxim Logachev</author>
        <description><![CDATA[BackgroundThis article examines the challenges associated with students’ use of artificial intelligence (AI)-based software tools in the educational process. Advances in information technology enable the automatic generation of new content of various types (text, graphics, and audio) without direct human input. While offering considerable opportunities, such technologies also pose potential risks for maintaining academic integrity in the course of mastering educational programs. The aim of this study was to assess the influence of AI technologies on students’ responses when completing assignments related to theoretical knowledge acquisition.Materials and methodsThe research was conducted between 2023 and 2025 among second-year students enrolled in the “Applied Informatics” program. Stylistic, morphological, semantic, and syntactic analysis methods were applied to identify key errors in responses to different types of tasks. An anonymous survey conducted among students after submission of their completed work enabled us to identify the principles, methods, and tools that were used to obtain the results.ResultsThe key feature of the study sample was the lack of prior work experience and limited professional background among these students, which made it possible to determine the extent to which AI-generated information influenced the final content of their responses. The study identified the most typical structural and semantic patterns characteristic of AI-assisted student answers. On this basis, a methodology was developed to support instructors in assessing the degree of AI involvement in student work.ConclusionThe findings may be applied to the modernization of the educational process and in the designing personalized educational trajectories.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1721093</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1721093</link>
        <title><![CDATA[A rapid review of using AI-generated instructional videos in higher education]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Tran Trieu Hai</author><author>Duong Thi Thuy Mai</author><author>Nguyen Van Hanh</author>
        <description><![CDATA[IntroductionGenerative artificial intelligence (AI) has enabled the rapid emergence of AI-generated instructional videos (AIGIVs) as a new form of learning material in higher education. However, evidence on how they are produced, applied, and the reported benefits and risks remains fragmented, highlighting the need for a systematic synthesis.MethodsThis study conducted a rapid review following PRISMA principles. Studies published from 2023 onward were searched on the Web of Science, Scopus, IEEE Xplore, and Google Scholar. Fifteen eligible studies were analyzed using qualitative content analysis and thematic synthesis.ResultsTwo production modes were identified: fully AI-based video generation (e.g., Sora, HeyGen, Veo) and AI-assisted human-made production (e.g., DALL·E, ChatGPT). Pedagogical applications included using AIGIVs as instructional alternatives and as tools for reflective pedagogy, particularly ethical and critical reflection. Benefits included efficiency and scalability, improved accessibility and personalization, and enhanced emotional engagement and memory. Risks involved ethical concerns, technical limitations, and inauthentic or unreliable content.DiscussionAIGIVs show strong potential for higher education, but their value depends on instructional design, human oversight, and responsible governance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1710358</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1710358</link>
        <title><![CDATA[AI-driven framework for automated competency formalization: from professional standards to adaptive learning outcomes]]></title>
        <pubdate>2025-12-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ainur Mukashova</author><author>Jamalbek Tussupov</author><author>Sandugash Serikbayeva</author><author>Ayagoz Mukhanova</author><author>Muslim Sergaziyev</author><author>Madina Sambetbayeva</author><author>Aigerim Yerimbetova</author><author>Zhanar Lamasheva</author><author>Zhanna Sadirmekova</author><author>Valiya Ramazanova</author>
        <description><![CDATA[The rapid evolution of the labor market necessitates innovative approaches to align higher education curricula with professional standards. This study presents an AI-driven framework utilizing the GPT model to automate the formalization of professional competencies and learning outcomes from unstructured textual sources, such as professional standards and job descriptions. By transforming unstructured industry standards and job descriptions into structured competency maps, the framework ensures alignment with labor market needs. These maps are integrated into learning management systems (LMS) such as Canvas and Moodle, enabling the development of adaptive curricula. The methodology was validated using a dataset of professional standards from various industries, achieving a 30% increase in semantic accuracy compared to traditional methods. In addition, a multi-class classification task using Multinomial Naive Bayes, Gaussian Naive Bayes, and Random Forest models classified learning outcomes across college, undergraduate, graduate, and doctoral levels, achieving an accuracy score of 0.98, further confirming their applicability across qualification systems. Challenges such as technological inequalities and lack of pedagogical flexibility remain. This scalable approach enables educational institutions to bridge the gap between academia and industry, helping to produce employable graduates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1581143</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1581143</link>
        <title><![CDATA[The piloting and evaluation of the graduate employability skills app]]></title>
        <pubdate>2025-11-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Athanassios Jimoyiannis</author><author>Elizabeth Boyle</author><author>Graham G. Scott</author><author>Sobah Abbas Petersen</author><author>Ewa Topolewska-Siedzik</author><author>Panagiotis Tsiotakis</author><author>Aisha Abbas</author><author>Gavin Baxter</author><author>Maria Iqbal</author>
        <description><![CDATA[The Graduate Employability Skills app (GES-APP) is an innovative application that aims to assist students in higher education in thinking about and reflecting on their employability skills. This paper describes the iterative, 3 phase evaluation framework that was used in designing and evaluating the app and presents the key findings at each stage of the process. The iterative evaluation approach ensured that the GES-APP was modified at each phase by taking account of staff and students’ suggestions for improvements. Participants in the evaluation were students and staff from the 4 partner institutions of the EU-funded GES-APP project. The initial piloting focused on self-reporting of employability skills and participants provided positive comments about this activity, the key idea, content organization and functionality. They considered that it was innovative, meaningful and important and motivated them in exploring their employability skills and attitudes. Participants also provided many useful suggestions that were used to improve the layout and the quality of graphics, enhance users’ interactivity and feedback, and clarify the role of the employability coach. This revised prototype of the GES-APP was used in the quantitative and qualitative piloting of the extended app in phase 2. Further improvements were made and tested in phase 3. This involved a more rigorous, quasi-experimental design testing the final version of the GES-APP, using pre-and post-questionnaires to record students’ understanding of their employability skills, employer needs and career preparation. Statistically significant changes were found following the GES-APP intervention along two key dimensions: (a) Understanding employer needs and the labor market and (b) preparing for a career. This evaluation suggests that students would welcome digital support of this kind in helping them make the transition from higher education into the world of work.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1734114</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1734114</link>
        <title><![CDATA[Correction: Optimizing architectural-feature tradeoffs in Arabic automatic short answer grading: comparative analysis of fine-tuned AraBERTv2 models]]></title>
        <pubdate>2025-11-12T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Frontiers Production Office </author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1672081</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1672081</link>
        <title><![CDATA[AI-powered adaptive learning interfaces: a user experience study in education platforms]]></title>
        <pubdate>2025-11-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hossein Jamali</author><author>Sergiu M. Dascalu</author><author>Frederick C. Harris</author><author>Rui Wu</author>
        <description><![CDATA[Adaptive learning platforms are increasingly used to enhance online education, yet a gap exists in understanding how the design of their AI-powered features impacts user experience. This study addresses this gap by evaluating three prominent platforms–Khan Academy, Coursera, and Codecademy–in teaching HTML. Using a mixed-methods approach with 23 participants, we assessed task completion time, user satisfaction, engagement, and task accuracy. Results revealed significant performance differences: Codecademy offered the fastest task completion, while Khan Academy achieved the highest user satisfaction. A crucial finding emerged from qualitative and quantitative data: participants found the specific AI-driven adaptive features on all platforms to be subtle and minimally impactful, with core platform interactivity being a more dominant factor. This study's main contribution is the identification of a critical trade-off between learning efficiency and user engagement, which is mediated by the discoverability and perceived value of adaptive features. We conclude that for AI-powered educational tools to realize their full potential, their adaptive features must be more discoverable, intuitive, and integral to the core learning loop. The study provides actionable insights for designers and educators seeking to balance platform efficiency with a more personalized and motivating user experience.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1683272</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1683272</link>
        <title><![CDATA[Optimizing architectural-feature tradeoffs in Arabic automatic short answer grading: comparative analysis of fine-tuned AraBERTv2 models]]></title>
        <pubdate>2025-10-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Salma Abdulbaki Mahmood</author>
        <description><![CDATA[Automated essay evaluation systems represent a contemporary solution to the challenges presented by technological advancements in education, offering high accuracy in assessment while reducing reliance on human resources. This makes them essential in light of the growing demand for fast and reliable evaluation systems. However, a critical concern remains regarding the precision of these systems in their assessments and their ability to generalize in environments where large datasets are not readily available. This research aims to examine the generalizability of Automated Short Answer Grading (ASAG) systems under different training conditions, including unannotated data and annotated data. Through a comprehensive comparative methodology, the study evaluates the performance of precisely fine-tuned AraBERTv2 models integrated with three neural network architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), while testing them with varying numbers of features (2, 3, 4) using the AS-ARSG dataset. The primary goal is to explore the models' generalizability when incomplete data is available (unannotated or partially annotated) and to develop a flexible framework that reduces dependence on human assessment while maintaining grading quality. The results confirm that the two-feature MLP model outperformed all others by achieving the best performance with less error and high correlation values (MAE = 1.31, Spearman's coefficient = 0.808). In contrast, performance degradation was noted with the increasing number of features, especially in LSTM models. Through this approach, the research contributes to developing Arabic ASAG systems capable of adapting to limited data scenarios, thereby enhancing their efficiency and practical applicability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1615791</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1615791</link>
        <title><![CDATA[Understanding the role of instructor gestures during virtual lectures]]></title>
        <pubdate>2025-10-02T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Stephanie Kelly</author><author>Jihyun Kim</author><author>Pankaj Chaudhary</author>
        <description><![CDATA[The purpose of this study was to establish the importance of instructor gestures in online lectures. Social information processing theory explains that virtual communication can be just as effective as face-to-face communication if communicators understand how to adapt their communication to the virtual channel. This study seeks to better understand the roles of camera distance and gestures in adapting lectures to virtual classrooms. An experiment examined the impact of student gender, camera distance from the instructor, and gesture use on instructor credibility, as measured by caring, competence, and trustworthiness. The results indicate that while camera distance did not impact students’ perceptions of instructor competence, the absence of gestures did impact how trustworthy and caring male students perceived the instructor to be. In the absence of gestures, male students perceived the instructor to be less caring and trustworthy. This indicates that instructors should make efforts to speak with gestures in virtual lectures, especially for male students, just as they do in the traditional classroom.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1506046</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1506046</link>
        <title><![CDATA[Cyberbullying: a comparative analysis between the results of a scoping study and a questionnaire applied to students]]></title>
        <pubdate>2025-09-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Luís Coutinho</author><author>José Alberto Lencastre</author><author>Ana Maria Tomás</author>
        <description><![CDATA[This article presents a scoping study using the Scopus Database to analyze literature on cyberbullying and students’ perceptions. Using the keywords ‘cyberbullying’, ‘students’, and ‘perceptions’, we narrowed down 6,271 initial articles to 14 that met our inclusion criteria. Additionally, we conducted a questionnaire survey with 193 Portuguese students aged between 10 and 19 to understand their perceptions of cyberbullying. Our analysis revealed cyberbullying as a growing concern with significant negative impacts on students’ mental and emotional wellbeing. The correlation between our questionnaire results and the scoping study findings emphasizes the urgent need for comprehensive intervention strategies. Our research indicates that effective cyberbullying prevention requires a multi-faceted approach including: development of social and emotional skills among students; promotion of appropriate technology use beyond technical literacy; targeted teacher training programs; establishment of clear intervention protocols within schools; empowerment of cyber-observers as active prevention agents; and recognition that cyberbullying often functions as an extension of face-to-face aggression rather than anonymous attacks. This study brings into focus the critical importance of fostering digital citizenship within educational settings, with teachers and school administrators playing pivotal roles in creating safe digital environments. The findings underscore how properly structured educational interventions can significantly increase reporting rates and decrease cyberbullying incidents, thereby promoting students’ overall wellbeing in the digital age.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1587040</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1587040</link>
        <title><![CDATA[Research AI: integrating AI and gamification in higher education for e-learning optimization and soft skills assessment through a cross-study synthesis]]></title>
        <pubdate>2025-09-01T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Agostino Marengo</author><author>Alessandro Pagano</author><author>Brady Lund</author><author>Vito Santamato</author>
        <description><![CDATA[IntroductionThe integration of Artificial Intelligence (AI) and gamification into higher education is reshaping educational practices by personalizing learning and fostering essential workforce skills. This study critically examines the effectiveness of these technologies, their impact on student engagement, and the factors influencing students’ acceptance.MethodsA systematic literature review complemented by Topic Modeling using Latent Dirichlet Allocation (LDA) identified key research themes. Subsequently, predictive modeling with machine learning algorithms, hyperparameter optimization, and Local Interpretable Model-Agnostic Explanations (LIME) were applied to classify academic documents and interpret influential factors.ResultsFindings indicate that AI effectively customizes educational pathways, enhancing engagement and academic performance. Gamification notably supports soft skill development, providing more interactive assessments than traditional approaches. However, challenges related to data privacy and technological accessibility remain significant, particularly affecting international students and institutions with limited resources.DiscussionAI and gamification demonstrate considerable potential for transforming higher education through personalized learning and interactive skill assessments. Nevertheless, widespread adoption depends on addressing data privacy concerns and ensuring technological equity. Future research should investigate the long-term implications of these technologies in developing students’ adaptability within a dynamic global workforce.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1611952</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1611952</link>
        <title><![CDATA[Impact of digital transformation: assessing the knowledge and adoption of disruptive technologies in a higher education institution]]></title>
        <pubdate>2025-07-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Janet Aquino</author><author>Roger Alarcón</author><author>Laurita Guevara</author><author>Jessie Bravo-Jaico</author><author>Nilton Germán</author><author>Carlos Valdivia-Salazar</author><author>Oscar Serquén</author><author>Gisella Luisa Elena Maquen-Niño</author><author>Alfonso Tesén-Arroyo</author>
        <description><![CDATA[Digital transformation has become a key factor for educational development since it not only involves the integration of disruptive technologies but also the restructuring of the process and cultural adaptation that allows Higher Education Institutions (HEI) to respond to changes in society and the demands of the labor market. This article aimed to determine the level of knowledge about the different aspects of digital transformation in a HEI, encourage the adoption of disruptive technologies and facilitate an effective transition to digital educational environments through an analysis based on 6 dimensions such as: important principles and definitions, technological support for DT, Digital tools, disruptive technologies in DT, Security in Information Technologies and Digital Culture, applying two surveys as instruments to teachers, students and graduates of the university community. A quantitative approach with quasi-experimental design was used, evaluating a pre-test and post-test with a total of 34 questions distributed among the six dimensions analyzed using a Likert scale and applying the Wilcoxon statistical test for related samples. The initial findings indicate that in the first dimension “important principles and definitions” with 6 questions, two of them do not present adequate significance with values of 0.06505 and 0.051, respectively. On the other hand, the dimensions technological support (6 questions), Disruptive technologies (9 questions), Digital tools (6 questions) and Information Technology Security (ICTs; 4 questions), obtained significant results in each of their questions, finally the Digital Culture dimension (4 questions) presents a question with a p-value of 0.24 presenting no significant difference, that is, the answers of the participants did not vary. In addition, at the dimension level, the one that shows the best results is Disruptive Technologies with a p-value of 9.08×10−17, which indicates that after the training, significant improvements were obtained in this dimension as opposed to the rest. Finally, performing the global analysis of the dimensions in terms of gender the scores in women are higher with respect to men, in addition, considering the analysis based on roles it was determined that teachers show better results and students have lower scores.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1628104</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1628104</link>
        <title><![CDATA[Development of adaptive and emotionally intelligent educational assistants based on conversational AI]]></title>
        <pubdate>2025-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rommel Gutierrez</author><author>William Villegas-Ch</author><author>Jaime Govea</author>
        <description><![CDATA[Although increasingly sophisticated in cognitive adaptability, current educational virtual assistants lack effective integration of real-time emotional analysis mechanisms. Most existing systems focus exclusively on static cognitive adaptation or incorporate superficial emotional responses, without dynamically modifying pedagogical strategies in response to detected emotional states. This structural limitation reduces the potential for generating personalized, empathetic, and sustainable learning experiences, particularly in complex domains such as critical reading comprehension. To address this gap, this study proposes and evaluates an educational assistant based on conversational artificial intelligence, which integrates natural language processing, real-time emotional analysis, and dynamic cognitive adaptation. The system was implemented in a controlled experimental setting with university students over a period of two weeks, utilizing a Moodle-based virtual learning platform. The evaluation methodology combines quantitative and qualitative techniques, including pre- and post-tests to assess academic performance, sentiment analysis of chat conversations to track emotional evolution, structured surveys to measure user perception, and semi-structured interviews to collect in-depth, experiential feedback. All interactions were logged for semantic and affective analysis. The architecture, organized using microservices, enables real-time semantic analysis of student messages, emotional inference, and adaptive adjustment of feedback strategies at the cognitive, emotional, and metacognitive levels. The results demonstrate a significant improvement in academic performance, with an average increase of 32.5% in correct answers from the pre-test to the post-test, particularly in inference and critical analysis skills. In parallel, the error correction rate during the sessions increased from 60 to 84%, while engagement levels and emotional perceptions showed progressive improvement. Integrating cognitive and emotional adaptation mechanisms with a rigorous multimodal evaluation process positions this assistant as an innovative advance in emotionally intelligent educational technologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1565809</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1565809</link>
        <title><![CDATA[Engineering young faculty's acceptance of real-time behavior measurement software]]></title>
        <pubdate>2025-06-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Luis Marquez-Carpintero</author><author>Francisco Gomez-Donoso</author><author>Miguel Cazorla</author>
        <description><![CDATA[The integration of technology in education faces challenges like smartphone distractions. Educators' acceptance depends on perceived usefulness, ease of use, and technical support. Innovative methodologies, such as dynamic activity detection and wearable tech, improve classroom interaction and learning outcomes. However, no studies have focused on the acceptance of this software. This research examines young engineering professors' perceptions of advanced monitoring technologies for improving student attention and engagement based on their gestures, hypothesizing significant enhancements in teachers' performance according to Extended Technology Acceptance Model (TAM2). Data were collected from 10% of the young engineering faculty members (under 40 years old) at a Spanish university (107 individuals) through a structured questionnaire examining perceptions of usefulness, ease of use, and anxiety. Short training sessions proved critical for successful implementation, addressing financial, privacy, and technical challenges. Findings indicate positive acceptance, with ease of use influencing the intention to use and the image construct. Anxiety negatively impacts usage, underscoring the need to address it. Adequate technical support and continuous training are vital. The study reveals positive acceptance of real-time behavior measurement software among young engineering professors, highlighting the importance of training and addressing anxiety and institutional acceptance.]]></description>
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