Digital

Creating applications to manually populate and modify knowledge graphs is a complex task. In this paper, we propose a novel approach for designing user interfaces for this purpose, based on existing SHACL constraint files. Our method consists of taking SHACL constraints and creating multi-form web applications. The novelty of the approach is to treat the editing of knowledge graphs via multi-form application interaction as a business process. This enables user interface modeling, such as modeling of application control flows by integrating ontology-based business process management components. Additionally, because our application models are themselves knowledge graphs, we demonstrate how they can leverage OWL reasoning to verify logical consistency and improve the user experience. Full article

The increasing recognition of interprofessional education in dentistry has further stimulated interest in digital dentistry-based convergence education as a means of fostering collaboration and enhancing clinical competence. Therefore, this study aimed to examine perceptions, experiences, perceived necessity, and educational needs regarding digital dentistry convergence education among undergraduate students majoring in dental hygiene and dental technology in South Korea. A total of 464 valid responses were collected through a structured questionnaire and analyzed for general characteristics, perceptions of convergence education, prior learning experience, perceived necessity, and preferred curriculum areas. Frequency analysis, chi-squared tests, and correlation analyses were applied. The participants’ direct experience with convergence education was limited, but more than 90% of the respondents recognized its necessity. Dental hygiene students most frequently preferred convergence with dental technology, while dental technology students preferred convergence with dental hygiene. Both groups prioritized clinical and basic courses as areas for convergence education and expected improvements in job-related knowledge as the primary educational outcome. Dental hygiene and dental technology students strongly acknowledged the importance of digital dentistry convergence education and interdisciplinary collaboration. These findings support the development of learner-centered convergence curricula and highlight the need to establish feasible educational models through curriculum innovation. Full article

Cultural organizations have traditionally been viewed as resistant to change, often bound by legacy structures, public dependency, and non-commercial missions. However, recent advances in digital technologies—ranging from AI and VR to IoT and big data—are reshaping the operational and strategic landscape of these institutions. Despite this shift, academic literature has yet to comprehensively map how technological innovation transforms cultural organizations into practice. This paper addresses this gap by introducing the concept of the Cultural Organizational System (COS)—a holistic framework that captures the multi-component structure of cultural entities, including space, tools, performance, management, and networks. Using a PRISMA-based scoping review methodology, we analyze over 90 sources to identify the types, functions, and strategic roles of technological innovations across COS components. The findings reveal a taxonomy of innovation use cases, a mapping to Oslo innovation categories, and a quadrant model of enablers and barriers unique to the cultural sector. By offering an integrated view of digital transformation in cultural settings, this study advances innovation theory and provides practical guidance for cultural leaders and policymakers seeking to balance mission-driven goals with sustainability and modernization imperatives. Full article

Every day, countless individuals with hearing or speech disabilities struggle to communicate effectively, as their conditions limit conventional verbal interaction. For them, sign language becomes an essential and often sole tool for expressing thoughts and engaging with others. However, the general public’s limited understanding of sign language poses a major barrier, often resulting in social, educational, and professional exclusion. To bridge this communication gap, the present study proposes a smart wearable glove system designed to translate Arabic sign language (ArSL), especially Moroccan sign language (MSL), into a written alphabet in real time. The glove integrates five MPU6050 motion sensors, one on each finger, capable of capturing detailed motion data, including angular velocity and linear acceleration. These motion signals are processed using an Artificial Neural Network (ANN), implemented directly on a Raspberry Pi Pico through embedded machine learning techniques. A custom dataset comprising labeled gestures corresponding to the MSL alphabet was developed for training the model. Following the training phase, the neural network attained a gesture recognition accuracy of 98%, reflecting strong performance in terms of reliability and classification precision. We developed an affordable and portable glove system aimed at improving daily communication for individuals with hearing impairments in Morocco, contributing to greater inclusivity and improved accessibility. Full article

The present study utilized two prominent emotion theories to investigate intention and behavior involved in propagating threatening social media messages. Participants were randomly assigned to different blocks of tweets/Xs with the same word count but different topics/sentiments. The topics in Study 1 (N = 619) were neutral and illegal border crossing, whereas the topics in Study 2 (N = 577) were the virulent risk of COVID-19 and the potential risks of newly developed vaccines. Dissemination intention was gauged by the number of tweets that participants wanted to share. Participants were also asked to summarize the messages to observe their behavioral engagement with the information, specifically through time spent on the task and the number of words written. An intention–behavior disjoint was found under all threatening topics and on both sides of the political divide. Fearful participants showed engaging intentions (wanted to share more tweets) but disengaging behaviors (wrote fewer words and submitted their summaries sooner). The necessary and sufficient conditions for the intention–behavior disjoint seemed to be the presence of threatening contents and subjective fear. Communicating risks can spark interest, but it is important not to burden the audience with too much fear, or they may stop spreading the word. Full article

Background: Dementia is a progressive neurodegenerative condition that impairs cognitive functions such as memory, language comprehension, and problem-solving. Assistive technologies can provide vital support at various stages of dementia, significantly improving the quality of life by aiding daily activities and care. However, for these technologies to be effective and widely adopted, a human-centred design (HCD) approach is of consequence for both their development and evaluation. Objectives: This scoping review aims to explore how HCD principles have been applied in the design of assistive technologies for people with dementia and to identify the extent and nature of their involvement in the design process. Eligibility Criteria: Studies published between 2017 and 2025 were included if they applied HCD methods in the design of assistive technologies for individuals at any stage of dementia. Priority was given to studies that directly involved people with dementia in the design or evaluation process. Sources of Evidence: A systematic search was conducted across five databases: Web of Science, JSTOR, Scopus, and ProQuest. Charting Methods: Articles were screened in two stages: title/abstract screening (n = 350) and full-text review (n = 89). Data from eligible studies (n = 49) were extracted and thematically analysed to identify design approaches, types of technologies, and user involvement. Results: The 49 included studies covered a variety of assistive technologies, such as robotic systems, augmented and virtual reality tools, mobile applications, and Internet of Things (IoT) devices. A wide range of HCD approaches were employed, with varying degrees of user involvement. Conclusions: HCD plays a critical role in enhancing the development and effectiveness of assistive technologies for dementia care. The review underscores the importance of involving people with dementia and their carers in the design process to ensure that solutions are practical, meaningful, and capable of improving quality of life. However, several key gaps remain. There is no standardised HCD framework for healthcare, stakeholder involvement is often inconsistent, and evidence on real-world impact is limited. Addressing these gaps is crucial to advancing the field and delivering scalable, sustainable innovations. Full article

Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth’s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020–2024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day–night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets. Full article

Humor translation remains a significant challenge due to its reliance on wordplay, cultural context, and nuance. This study compares a Neural Machine Translation (NMT) system (hereafter referred to as MT) with a Large Language Model (GPT-based translation using three different prompts) for translating jokes from English to Thai. Results show that GPT-based models significantly outperform MT in humor retention, with the explanation-enhanced prompt (GPT-Ex) achieving the highest joke preservation rate (62.94%) compared to 50.12% in MT. Additionally, humor loss was more frequent in MT, while GPT-based models, particularly GPT-Ex, better retained jokes. A McNemar test confirmed significant differences in annotation distributions across models. Beyond evaluation, we propose using GPT-based models with optimized prompt engineering to enhance humor translation. Our refined prompts improved joke retention by guiding the model’s understanding of humor and cultural nuances. Full article

Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network. Full article

Artificial intelligence tools are quickly being applied in many areas of science, including learning sciences. Learning requires various types of thinking, sustained by distinct sets of neural networks in the brain. Labelling these systems gives us tools to manage them. This paper presents a pilot application of Large Language Models (LLMs) to physics textbook analysis, grounded in a well-developed neural network theory known as the Five Pillars of the Mind. The domain-specific networks, innate sense, and the five pillars provide a framework with which to examine how physics is learnt. For example, one can identify which pillars are active when discussing a physics concept. Identifying which pillars belong to which physics concept may be significantly influenced by the bias of the author and could be too time-consuming for longer, more complex texts involving physics concepts. Therefore, using LLMs to identify pillars could enhance the application of this framework to physics education. This article presents a case study in which we used selected Large Language Models to identify pillars within eight pages of learning material concerning forces aimed at 12- to 14-year-old pupils. We used GPT-4o and o4-mini, as well as MAXQDA AI Assist. Results from these models were compared with the authors’ manual analysis. Precision, recall, and F1-Score were used to evaluate the results quantitatively. MAXQDA AI Assist obtained the best results with 1.00 precision, 0.67 recall, and an F1-Score of 0.80. Both products by OpenAI hallucinated and falsely identified several concepts, resulting in low precision and, consequently, low F1-Score. As predicted, ChatGPT o4-mini scored twice as high as ChatGPT 4o. The method proved to be promising, and its future development has the potential to provide research teams with analysis not only of written learning material, but also of pupils’ written work and their video-recorded activities. Full article

Digital transformation (DT) is rapidly reshaping education at multiple levels, including curriculum, instructional practices, and institutional culture. Within this context, entrepreneurship education has become a key field for preparing individuals to navigate uncertainty and generate social and economic value in a digital society. Entrepreneurial competencies are increasingly conceptualized as a multidimensional construct that encompasses creativity, problem-solving, critical thinking, collaboration, and digital literacy. This study aims to identify core entrepreneurial competencies relevant to the digital era and examine how technology-integrated instructional strategies contribute to their development. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, analyzing 72 peer-reviewed journal articles published between January 2021 and June 2025. The findings indicate that DT drives structural changes in education beyond tool adoption, with technologies such as artificial intelligence (AI), data analytics, and digital collaboration platforms serving as catalysts for innovative thinking and entrepreneurial behavior. These technologies are not merely supportive tools but are embedded in competency-based learning processes. This review provides a comprehensive competency framework integrating three domains, AI-collaborative pedagogy validation, and implementation strategies, enabling educators, curriculum developers, and policymakers to redesign entrepreneurship education that aligns with the realities of digital learning environments and fosters future-ready entrepreneurial capabilities. This conceptual framework theoretically systematizes the integration of innovative thinking and ethical execution capabilities required in the digital era, contributing to defining the future direction of entrepreneurship education. Full article

In a world where artificial intelligence is constantly changing education, taking students’ feelings into account is a crucial framework for enhancing their engagement and academic performance. This article presents LearnerEmotions, an online application that employs machine vision technology to determine how learners are feeling in real time through their facial expressions. Teachers and institutions can access analytical dashboards and monitor students’ emotions with this tool, which is designed for use in both in-person and remote classes. The facial expression recognition model used in this application achieved an average accuracy of 0.91 and a loss of 0.3 in the real environment. More than 9 million emotional data points were gathered from an experiment involving 65 computer engineering students, and these insights were correlated with attendance and academic performance. While negative emotions like anger, sadness, and fear are associated with decreased performance and lower attendance, the statistical study shows a strong correlation between positive feelings like surprise and joy and successful academic performance. These results underline the necessity of technological tools that offer immediate pedagogical regulation and support the notion that emotions play an important role in the learning process. Thus, LearnerEmotions, which considers students’ emotional states, is a potential first step toward more adaptive learning. Full article

Generative artificial intelligence (GAI) is poised to transform clinical dentistry by enhancing diagnostic accuracy, personalizing treatment planning, and improving procedural precision. This study integrates logic programming and entropy within knowledge representation and reasoning to generate hypotheses, quantify uncertainty, and support clinical decisions. A six-month longitudinal questionnaire was administered to 127 dentists, of whom 119 provided valid responses across four dimensions: current use and knowledge (CUKD), potential applications (PAD), future perspectives (FPD), and challenges and barriers (CBD). Responses, analyzed with both classical statistics and entropy-based measures, revealed significant differences among dimensions (p < 0.01, η² = 0.14). CUKD, PAD, and FPD all increased steadily over time (baseline means 2.32, 3.06, and 3.27; rising to 3.75, 4.51, and 4.71, respectively), while CBD remained more variable (1.87–3.87). The overall entropic state declined from 0.43 to 0.31 (p = 0.018), reflecting reduced uncertainty. Statistical and entropy-derived trends converged, suggesting growing professional clarity and cautious acceptance of GAI. These findings indicate that, despite persistent concerns, GAI holds promise for advancing adaptive and evidence-driven dental practice. Full article

This study explores how artificial intelligence utilization in digital government, through AI-enabled service automation and AI-based decision support, contributes to digital government transformation, emphasizing the mediating roles of stakeholder trust and stakeholder participation. Grounded in stakeholder theory and public value theory, the research aims to understand the relational mechanisms that connect technological innovation to institutional change. A quantitative research design was employed using survey data collected from 412 stakeholders, including citizens, civil society members, public employees, and private actors, who had interacted with AI-driven government services in Pakistan. Structural equation modeling was used to test a conceptual model involving direct and indirect effects. Results reveal that both AI-enabled service automation and AI-based decision support significantly enhance stakeholder trust and participation, which in turn positively influence digital government transformation. Stakeholder trust emerged as a stronger mediator than participation. The findings highlight the importance of ethical, transparent, and participatory AI integration in public administration. Theoretically, the study extends digital governance literature by validating behavioral mediators in technology-driven reform. Practically, it offers strategic insights for policymakers on how to enhance stakeholder engagement and legitimacy in AI-based public systems. Overall, the study emphasizes that successful digital transformation is not solely technological, but also deeply relational and participatory. Full article

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts. Full article

This study demonstrates how mathematical ideas can be developed through genuine applications to problems that are attractive to the learners of mathematics due to consistency with their life experiences. To this end, the paper provides several examples of digital instruments both commonly available and designed by the authors with the goal to prepare schoolchildren of different ages to mathematize basic models of computer science and engineering. The mathematization includes construction and optimization of the models by using big ideas of mathematics at the level of common sense alone as a grade-appropriate prerequisite to their formal description. Also, the paper examines computer systems that can be depicted as the prototypes of artificial intelligence since, in the context of education, they can be used as tools enabling both motivation and support of one’s conceptual development rather than simply a means to carry out thinking for the learners of mathematics. Finally, by referring to a few notable contributors to mathematical, educational, and psychological knowledgebase, this study argues for the merit of intuition in the digital age as a support system in the advancement of computational problem-solving techniques. Full article

This study presents a systematic review of the literature on teaching techniques that enhance student motivation and academic performance across basic, secondary, and higher education levels. The review is grounded in the distinction between intrinsic and extrinsic motivation, highlighting their decisive roles in engagement and achievement. The analysis focuses on active learning methodologies such as project-based learning, collaborative learning, gamification, and flipped classrooms. It identifies the mechanisms by which each approach fosters students’ interest, sense of competence, and persistence. Four international databases were consulted, and studies published between 2000 and 2024 reporting quantitative measures of motivation and/or performance were selected. Five investigations met all eligibility criteria and were assessed for methodological quality. The results indicate moderate motivational effects, especially when interventions last at least eight weeks, provide frequent feedback, and place students at the center of authentic problem-solving. Greater gains were also observed in STEM disciplines and in contexts that encourage peer collaboration. Based on these findings, practical recommendations are proposed for educators: structure interdisciplinary projects, incorporate playful elements in the initial stages of formal education, combine autonomous work with small-group discussions, and use data analysis tools to deliver personalized feedback. The study concludes that adopting diverse, student-centered pedagogical practices enhances motivation and academic achievement, leading to deeper and more lasting learning outcomes. Full article

E-government represents a global initiative that leverages information and communication technologies (ICTs) to enhance public service delivery and strengthen interactions between governments and citizens. While adoption is critical to realizing the potential benefits of e-government, research from the demand-side perspective remains limited, particularly regarding how individuals engage with these systems, the factors shaping their trust, and the role of media in promoting awareness and uptake. This study examines the influence of media exposure on e-government adoption by assessing its impact on trust, perceived ease of use, satisfaction, relative advantage, complexity, and observability. A quantitative survey was conducted among residents of the Rabat-Salé-Kénitra region, and the proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model demonstrated robust reliability (Cronbach’s alpha = 0.710), and ANOVA results (p < 0.001) confirmed the substantial explanatory power of the independent variables in predicting adoption. The model accounted for 65.7% of the variance in adoption and 67.2% in trust. Media exposure and digitalization exerted strong positive effects on trust, which emerged as the most influential predictor of adoption. Additionally, observability and relative advantage positively influenced adoption, whereas complexity had a negative effect. Notably, 72.86% of respondents expressed an intention to adopt e-government services in the future. These findings underscore the pivotal role of media as a catalyst for digital transformation and offer actionable insights for policymakers aiming to enhance citizen trust and engagement with e-government services. Full article

This study evaluated the psychometric properties of the Digital Citizenship Scale in Chilean university students, specifically the factorial structure and its reliability, construct validity, and factorial invariance by sex were analyzed. The sample consisted of 905 students whose average age was 22 years, of which 59.7% were women. The methods used were Exploratory Factor Analysis and Confirmatory Factor Analysis. The result of the exploratory analysis suggested retaining the 26 items of the original scale grouped into five factors. The results of the confirmatory analysis corroborated the original structure of the scale and specified a model of five correlated factors. The reliability analysis indicated a total ordinal alpha of 0.87. The measurement invariance analysis showed that the degree of equivalence of the instrument by sex was plausible at a strict level. The scale provides guidance for institutional decision-making regarding initiatives focused on digital inclusion and participation. It was concluded that the Digital Citizenship Scale presents adequate psychometric properties for its use in Chilean university students. Full article