Digital, Volume 5, Issue 2 (June 2025) – 10 articles

Ontology development is a multidisciplinary work involving domain experts and knowledge engineers. Bringing together such a team to develop an ontology of quality is not easy. Therefore, ontologies are often created with limited expertise either in the medical domain or in ontology engineering. Unfortunately, the existing methodologies do not provide much guidance on how the different steps of ontology development should be performed, particularly in the case of reduced involvement of domain experts. This challenge is getting more difficult when there is a multitude of medical knowledge sources and ontologies covering parts of the domain, and often, each has a different representation of the same concept, for example, as a symptom, disease, or clinical sign. This research presents a methodology for creating a medical ontology of quality with limited involvement of the domain experts. The latter are only consulted in the domain definition and evaluation phases. We combine building an ontology from codified knowledge and ontology reuse to enhance reusability and interoperability. The methodology is inspired by METHONTOLOGY, for which we make several improvements, especially in the ontology reuse phase. We provide proof of concept of the proposed methodology with a case study involving the development of the pneumonia diagnosis ontology (PNADO). Full article

The increasing complexity of academic programs and student needs necessitates personalized, data-driven academic advising. Traditional heuristic-based methods often fail to optimize course selection, leading to inefficient academic planning and delayed graduations. This study introduces a hierarchical multi-model machine learning framework for personalized course recommendations, integrating five predictive models: Success Probability Model (SPM), Course Fit Score Model (CFSM), Prerequisite Fulfillment Model (PFM), Graduation Priority Model (GPM), and Recommended Load Model (RLM). These models operate independently in a local model framework, generating specialized predictions that are synthesized by a global model framework through a meta-function. The meta-function aggregates predictions to compute a final score for each course and ensures recommendations align with student success probabilities, program requirements, and workload constraints. It enforces key constraints, such as prerequisite satisfaction, workload optimization, and program-specific requirements, refining recommendations to be both academically viable and institutionally compliant. The framework demonstrated strong predictive performance, with root mean squared error values of 0.00956, 0.011713, and 0.005406 for SPM, CFSM, and RLM, respectively. Classification models for PFM and GPM also yielded high accuracy, exceeding 99%. Designed for modularity and adaptability, the framework allows for the integration of additional predictive models and fine-tuning of recommendation priorities to suit institutional needs. This scalable solution enhances academic advising efficiency by transforming granular model predictions into personalized, actionable course recommendations, supporting students in making informed academic decisions. Full article

The rapid increase in educational data from diverse sources such as learning management systems and assessment records necessitates the application of advanced analytical techniques to identify at-risk students and address persistent issues like dropout rates and academic underperformance. However, many existing models struggle with generalizability and fail to effectively manage data challenges such as class imbalance and missing data, leading to suboptimal predictive performance. This study proposes a hybrid deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks to improve the accuracy of student academic performance prediction and enable timely educational interventions. To improve the performance of the model, we incorporate feature selection techniques and optimization strategies to enhance reliability. We also address common preprocessing challenges such as missing data and data imbalance. The proposed model was evaluated on two benchmark datasets to ensure model generalization capability. The hybrid model achieved predictive accuracies of 98.93% and 98.82% on the two datasets, respectively, outperforming traditional machine learning models and standalone deep learning approaches across key performance metrics including accuracy, precision, recall, and F-score. Full article

This study explores the thematic structure and editorial focus of the digital media landscape in Bulgaria by analyzing one year of online news publications from eight major media outlets. The data were collected through a custom-built web scraping application developed in Java, which enabled the automated extraction and processing of full-text articles from publicly accessible news websites. The structured dataset, generated during the scraping process, records word-level occurrences in both article titles and bodies, along with publication dates and URLs. By applying lexical frequency analysis and temporal tracking, this study identifies the most frequently used words and platform-specific usage patterns. The findings reveal clear distinctions in editorial focus between public broadcasters, private national media, and international outlets. Additionally, the analysis highlights how title construction and word prominence vary depending on platform type and media strategy. This study demonstrates the potential of web scraping and computational text analysis as scalable tools for investigating media systems in small and transitional democracies. Full article

Digital trust is increasingly crucial for successful interactions in modern digital environments. However, the existing literature lacks a unified definition and a comprehensive understanding of its core factors. This study addresses these gaps by conducting a systematic literature review to explore and synthesise existing definitions of digital trust and identify the fundamental factors that shape it. A total of 86 relevant sources were analysed, revealing that digital trust is typically conceptualised as confidence in people, processes, and technology aimed at ensuring a secure digital environment, with data protection and privacy playing critical roles. Through thematic analysis, “openness” emerged as an additional factor complementing previously established elements of the integrative model of organisational trust, such as ability, benevolence, and integrity. Based on 42 definitions, we developed a new holistic definition of digital trust. The authors evaluated its content validity, confirming its alignment with the essential factors shaping digital trust’s essence. The findings highlight the multidimensional nature of digital trust and offer an operationalised framework for future measurement and application. Full article

This pilot study explores the efficacy of an AI-powered gamified chatbot integrated with an Online-Merge-Offline (OMO) strategy to enhance parental nutrition knowledge. Conducted in a Taiwanese public childcare setting, the intervention comprised eight weekly nutrition seminars delivered by registered dietitians, supplemented by a LINE-based chatbot providing interactive, gamified learning experiences. Pre-test and post-test evaluations were administered via the chatbot to assess knowledge acquisition. The results from 20 unique participants, including 9 with complete data, indicated a statistically significant improvement in nutritional knowledge (p < 0.0001, Cohen’s d = 2.50), suggesting a substantial educational impact. The integration of gamification elements—such as level completion, community rankings, and personalized feedback—with OMO modalities allowed for sustained engagement, knowledge reinforcement, and seamless transition between digital learning and physical application. This study provides empirical evidence supporting the feasibility and pedagogical value of OMO-gamified chatbots in health promotion and lays the groundwork for future large-scale, longitudinal investigations. Full article

The advent of transformer technology and large language models (LLMs) has further broadened the already extensive application field of artificial intelligence (AI). A large portion of medical records is stored in text format, such as clinical trial texts. Part of these texts is information regarding eligibility criteria. We aimed to harness the immense capabilities of an LLM by fine-tuning an open-source LLM (Llama-2) to develop a classifier from the clinical trial data. We were interested in investigating whether a fine-tuned LLM could better decide the eligibility criteria from the clinical trial text and compare the results with a more traditional method. Such an investigation can help us determine the extent to which we can rely on text-based applications developed from large language models and possibly open new avenues of application in the medical domain. Our results are comparable to the best-performing methods for this task. Since we used state-of-the-art technology, this research has the potential to open new avenues in the field of LLM application in the healthcare sector. Full article

Students with disabilities often face challenges in participating in classroom activities with normal students. Assistive technologies powered by Artificial Intelligence (AI) or Machine Learning (ML) can provide vital support to ensure inclusive and equitable learning environments. In this paper, we identify AI or ML-powered inclusive education tools and technologies, explore the factors required for developing personalized learning plans using AI, and propose a real-time personalized learning framework. We have identified inclusive education tools and technology driven by AI or ML as well as factors impacting the creation of AI-based personalized learning based on our exploration of Google Database, blog sites, company sites, tools, and techniques used in different centers. This study proposes a system model that includes engagement and adaptive learning components. The system uses Bloom’s taxonomy to continuously track the learner’s development. We identified a comprehensive list of AI- or ML-powered inclusive education tools and technologies and determined key factors for developing personalized learning plans, including emotional state, student progress, preferences, learning styles, and outcomes. Based on this research, AI-based inclusive education has the potential to improve educational experiences for students with disabilities by creating a more equitable and inclusive learning environment. Full article

Background: As digital health resources become increasingly prevalent, assessing the quality of information provided by publicly available AI tools is vital for evidence-based patient education. Objective: This study evaluates the accuracy and readability of responses from four large language models—ChatGPT 4.0, ChatGPT 3.5, Google Bard, and Microsoft Bing—in providing contraceptive counseling. Methods: A cross-sectional analysis was conducted using standardized contraception questions, established readability indices, and a panel of blinded OB/GYN physician reviewers comparing model responses to an AAFP benchmark. Results: The models varied in readability and evidence adherence; notably, ChatGPT 3.5 provided more evidence-based responses than GPT-4.0, although all outputs exceeded the recommended 6th-grade reading level. Conclusions: Our findings underscore the need for the further refinement of LLMs to balance clinical accuracy with patient-friendly language, supporting their role as a supplement to clinician counseling. Full article

Mobile Health (mHealth) technologies are transforming healthcare by making it more accessible, efficient, and patient-centric. This study investigates the factors influencing Millennial’s mobile health adoption intention (mHAI). We propose a research model based on the integrated model of the Unified Theory of Acceptance and Use of Technology—UTAUT and the health belief model—HBM. A cross-sectional study was carried out employing purposive sampling to enlist Generation Y (born between 1981 and 1996) and 220 valid questionnaires were collected. We employed structure equation modeling partial least square (SEM-PLS) along with artificial neural network (ANN) and importance–performance map analysis (IPMA) to analyze our model. The research findings revealed that performance expectancy is the most influential factor, while effort expectancy showed no significant association with mHAI. Theoretical and managerial implications are offered to expand the literature on digital healthcare studies, indicating how healthcare providers in developing countries can attract their potential users. Full article