Search Results (98)

The green macroalgae Ulva Linnaeus, 1753, also known as sea lettuce, is one of the most ecologically and economically significant algal genera. Its representatives occur in marine, brackish, and freshwater environments worldwide and show high adaptability, rapid growth, and marked biochemical diversity. These traits support their ecological roles in nutrient cycling, primary productivity, and habitat provision, and they also explain their growing relevance to the blue bioeconomy. This review summarizes current knowledge of Ulva biodiversity, taxonomy, and physiology, and evaluates applications in food, feed, bioremediation, biofuel, pharmaceuticals, and biomaterials. Particular attention is given to molecular approaches that resolve taxonomic difficulties and to biochemical profiles that determine nutritional value and industrial potential. This review also considers risks and limitations. Ulva species can act as hyperaccumulators of heavy metals, microplastics, and organic pollutants, which creates safety concerns for food and feed uses and highlights the necessity of strict monitoring and quality control. Technical and economic barriers restrict large-scale use in energy and material production. By presenting both opportunities and constraints, this review stresses the dual role of Ulva as a promising bioresource and a potential ecological risk. Future research must integrate molecular genetics, physiology, and applied studies to support sustainable utilization and ensure safe contributions of Ulva to biodiversity assessment, environmental management, and bioeconomic development. Full article

The global demographic transition toward an ageing population has necessitated substantive reforms in dental education, particularly within the field of geriatric prosthodontics. Conventional curricula have frequently prioritized technical competencies while insufficiently addressing the integration of biological, psychosocial, and ethical complexities inherent in the care of older adults. This scoping review critically examined these curricular deficiencies by synthesizing evidence from 34 peer-reviewed studies, employing Bloom’s Taxonomy as a conceptual framework to inform a systematic and pedagogically grounded curriculum redesign. The primary aim was to identify existing gaps in undergraduate and postgraduate education, evaluate the efficacy of active and simulation-based learning modalities, assess the utility of reflective practices and standardised assessment tools, and formulate strategic, taxonomy-aligned pedagogical guidelines. Following the PRISMA-ScR methodology, the included studies were thematically analysed and categorized across the six cognitive levels of Bloom’s Taxonomy. Findings highlighted the effectiveness of integrated educational strategies, including Case-Based Learning, interprofessional education, virtual simulations, and structured assessments such as Objective Structured Clinical Examinations (OSCE). Furthermore, reflective models such as “What? So What? Now What?” fostered higher-order cognitive processes, ethical reasoning, and self-directed learning. By aligning cognitive levels—from foundational knowledge recall to innovative creation—ten evidence-based educational guidelines were developed. These guidelines are pedagogically sound, empirically supported, and adaptable to diverse curricular contexts. The proposed framework ensures a deliberate, progressive trajectory from theoretical comprehension to clinical expertise and ethical leadership. Future research should explore longitudinal outcomes and develop scalable, culturally responsive models to support the broader implementation of curricular reform in geriatric dental education. Full article

A novel Gram-negative bacterium, designated strain SN16^T, was isolated from a harmful algal bloom (HAB). Strain SN16^T exhibited potent, broad-spectrum algicidal activity against the colony-forming alga Phaeocystis globosa and eight other HAB-causing species, highlighting its potential as a promising candidate for the biological control of HABs. A phylogenetic analysis of 16S rRNA gene sequences placed strain SN16^T within the genus Flagellimonas. The average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) values between strain SN16^T and its relatives were 75.4–91.4% and 19.3–44.0%, respectively. These values fall below the established thresholds for species delineation, confirming that SN16^T represents a novel species. A chemotaxonomic analysis revealed its dominant cellular fatty acids to be iso-C_15:0 and iso-C_15:1 G. The major polar lipid was phosphatidylethanolamine, and the primary respiratory quinone was menaquinone-6. Genome mining identified 11 biosynthetic gene clusters (BGCs), including those encoding for terpenes, ribosomal peptide synthetases, and non-ribosomal peptide synthetases. By integrating BGC analysis with the observed algicidal activities, we predicted that pentalenolactone and xiamycin analogues are the likely causative compounds. Based on this polyphasic evidence, strain SN16^T is proposed as a novel species of the genus Flagellimonas, named Flagellimonas algicida sp. nov. This is the first report of Flagellimonas species exhibiting broad-spectrum algicidal activity, including activity against the colonial form of P. globosa—a key ecological challenge in HAB mitigation. Full article

Artificial intelligence is transforming higher education, particularly in flipped classroom settings, in which students learn independently prior to class and collaborate during in-person sessions. This study examines the role of ChatGPT as a virtual peer in a veterinary anatomy course centered on cardiovascular and respiratory systems. Over two academic years (2023–2025), 297 first-year veterinary students worked in small groups to explore anatomy through structured prompts in English and Spanish using ChatGPT versions 3.5 and 4. Activities involved analyzing AI output, evaluating anatomical accuracy, and suggesting alternative names for vascular variations. Learning outcomes were assessed using Bloom’s Taxonomy-based questions, and student perceptions were captured via online surveys. Progressive performance improvement was noted across three instructional phases, particularly in higher-level cognitive tasks (Bloom level 4). Responses to English prompts were more accurate than those to Spanish prompts. While students appreciated ChatGPT’s role in reinforcing knowledge and sparking discussion, they also flagged inaccuracies and emphasized the need for critical evaluation. Peer collaboration was found to be more influential than chatbot input. Conclusions: ChatGPT can enrich flipped anatomy instruction when paired with structured guidance. It supports content review, fosters group learning and promotes reflective thinking. However, developing digital literacy and ensuring expert oversight are essential to maximizing the educational value of AI. Full article

In the realm of educational assessment, the integration of artificial intelligence (AI) offers a promising pathway for the development of robust evaluations. This study explores the application of AI chatbots in crafting and validating examinations tailored to road geometric design, while adhering to the principles of Bloom’s Taxonomy. Utilizing Gemini AI Studio, three distinct exam versions were generated, covering eight crucial topics within road geometric design. A panel of expert chatbots, including Chat GPT 3.5, Claude 3, Sonet, Copilot, Perplexity, and You, assessed the validity of the exam content. These chatbots achieved scores of 9.17 or higher, establishing their proficiency as experts. Subsequent evaluations focused on relevance and wording, revealing high scores for both metrics, indicating the adequacy of the assessment tools. The two remaining versions were administered to student groups enrolled in the Road Construction II course at the Universidad Técnica Particular de Loja. Only 1.2% of students reached Bloom’s Taxonomy level 3, with many questions deemed easy, leading to varying trends in cognitive levels. Comparative analysis of student scores revealed significant discrepancies between a previous “classic” exam. While AI shows potential in crafting valid assessments aligned with Bloom’s Taxonomy, greater human involvement is necessary to ensure high-quality instrument generation. Full article

The increasing complexity of aircraft systems and the growing global demand for certified maintenance personnel necessitate a fundamental shift in aviation training methodologies. This paper proposes a comprehensive digital-twin-based training ecosystem tailored for aviation maintenance education. The system integrates three core digital twin models: the learner digital twin, which continuously reflects individual trainee competence; the ideal competence twin, which encodes regulatory skill benchmarks; and the learning ecosystem twin, a stratified repository of instructional resources. These components are orchestrated through a real-time adaptive engine that performs multi-dimensional competence gap analysis and dynamically matches learners with appropriate training content based on gap severity, Bloom’s taxonomy level, and content fidelity. The system architecture uses a cloud–edge hybrid model to ensure scalable, secure, and latency-sensitive delivery of training assets, ranging from computer-based training modules to high-fidelity operational simulations. Simulation results confirm the system’s ability to personalize instruction, accelerate competence development, and support continuous regulatory readiness by enabling closed-loop, adaptive, and evidence-based training pathways in digitally enriched environments. Full article

Disaster risk reduction (DRR) education is considered increasingly necessary, particularly for children. DRR educational interventions aim to enhance knowledge and attitudes related to self-protective capacity. However, comparative studies on students in areas prone to different disasters and comprehensive criteria covering both knowledge and attitudes toward behavior remain limited. A short DRR course was developed for primary schools across three regions (mountainous, low-lying, and coastal) in Hue City, one of Vietnam’s most vulnerable areas to extreme weather events. This study aimed to comprehensively evaluate student performance by applying Bloom’s taxonomy and treatment-control pre-post-follow-up design with panel analysis methods. From December 2022 to September 2023, three surveys, involving 517 students each, were conducted in six schools (three schools received the course and surveys, while the other three only participated in surveys). The intervention revealed similarities and differences between the groups. The course positively impacted on some elements of knowledge and preparedness intentions in students from low-lying and mountainous regions (including ethnic minorities). Higher-grade students in the mountainous region showed improvement in intentions, but not in attitudes toward self-protection. No gender differences in intentions were found. Although limited overall improvements, the study’s various methods, approaches and continuous assessment can be applied globally to design, implement, and assess DRR education courses effectively. Full article

The COVID-19 pandemic has disrupted the progress toward Sustainable Development Goal 3, particularly in developing countries, exacerbating existing health disparities and creating new challenges for health systems worldwide. This study explores the role of university research in advancing SDG 3 targets in a post-pandemic context using the University of the Western Cape as a case study. Through qualitative data analysis of research titles and abstracts registered between 2020 and 2022, we applied the WHERETO model of McTighe and Bloom’s Taxonomy to categorize research according to the SDG 3 targets and indicators. This approach provides insight into which health priorities were addressed through scholarly research at UWC in alignment with the UN 2030 Agenda, particularly during pandemic recovery. Our findings indicate that research priorities largely corresponded with South Africa’s health challenges, with the highest concentration of studies addressing non-communicable diseases and mental health (Target 3.4), infectious diseases (Target 3.3), and medicine development (Target 3.b). These priorities align with the National Health Research Committee’s identified health priorities for disadvantaged communities in the Western Cape. Notably, research on mental health and emergency preparedness (Target 3.d) increased significantly during the pandemic period, reflecting shifting priorities in response to COVID-19. This study offers critical insights into how university research shifted priorities adapted during the pandemic and identifies areas requiring focused attention to support post-pandemic recovery. By highlighting research gaps and opportunities, our findings provide a foundation for developing more comprehensive approaches to health research that address the disparities exacerbated by COVID-19 while advancing the 2030 agenda. This model could inform research prioritization at other institutions facing similar challenges in both local and global contexts. Full article

The rise in generative artificial intelligence (GenAI) demands new forms of literacy among higher education students. This paper introduces a novel task-centered generative artificial intelligence literacy framework, which was developed collaboratively with academic and administrative staff at a large research university in Israel. The framework identifies eight skills which are informed by the six cognitive domains of Bloom’s Taxonomy. Based on this framework, we developed a measuring tool for students’ GenAI literacy and surveyed 1667 students. Findings from the empirical phase show moderate GenAI use and medium–high literacy levels, with significant variations by gender, discipline, and age. Notably, 82% of students support formal GenAI instruction, favoring integration within curricula to prepare for broader digital society participation. The study offers actionable insights for educators and policymakers aiming to integrate GenAI into higher education responsibly and effectively. Full article

Bloom’s taxonomy provides a well-established framework for categorizing the cognitive complexity of assessment questions, ensuring alignment with course learning outcomes (CLOs). Achieving this alignment is essential for constructing meaningful and valid assessments that accurately measure student learning. However, in higher education, the large volume of questions that instructors must develop each semester makes manual classification of cognitive levels a time-consuming and error-prone process. Despite various attempts to automate this classification, the highest accuracy reported in existing research has not exceeded 93.5%, highlighting the need for further advancements in this area. Furthermore, the best-performing deep learning models only reached an accuracy of 86%. These results emphasize the need for improvement, particularly in the application of deep learning models, which have not been fully exploited for this task. In response to these challenges, our study explores a novel approach to enhance the accuracy of cognitive level classification. We leverage a combination of augmentation through synonym substitution, advanced feature extraction techniques utilizing DistilBERT and TF-IDF, and a robust ensemble model incorporating soft voting. These methods were selected to capture both semantic meaning and term frequency, allowing the model to benefit from contextual depth and statistical relevance. Additionally, Bayesian optimization is employed for hyperparameter tuning to refine the model’s performance further. The novelty of our approach lies in the fusion of sparse TF-IDF features with dense DistilBERT embeddings, optimized through Bayesian search across multiple classifiers. This hybrid design captures both term-level salience and deep contextual semantics, something not fully exploited in prior models focused solely on transformer architectures. Our soft-voting ensemble capitalizes on classifier diversity, yielding more stable and accurate results. Through this integrated approach outperformed previous configurations with an accuracy of 96%, surpassing the current state-of-the-art results and setting a new benchmark for automated cognitive level classification. These findings have significant implications for the development of high-quality, scalable assessments in educational settings. Full article

The pressing need to address climate change and environmentally related challenges highlights the importance of reimagining educational approaches to equip students with the skills required for innovation and sustainability. This study proposes a novel holistic pedagogic framework for architectural education that integrates biomimicry, systems thinking, and Bloom’s Revised Taxonomy to advance innovation, sustainability, and transformative learning. Developed through a triangulated methodological approach—combining reflective practitioner inquiry, design-based research, and conceptual model development—the framework draws from multiple theoretical perspectives to create a cognitively structured, interdisciplinary, and ecologically grounded educational model. Bloom’s Taxonomy provides a scaffold for learning progression, while the Function–Structure–Behavior (FSB) schema enhances the establishment of cross-disciplinary bridges to enable students to address complex design challenges. The framework is informed by insights from the literature and patterns observed in bio-inspired studios, student projects, and interdisciplinary workshops. These examples highlight how the approach supports systems thinking, ecological literacy, and ethical decision-making through iterative, experiential, and metacognitive learning. Rather than offering a fixed intervention, the framework is presented as a flexible, adaptable model that aligns learning outcomes with real-world complexity. It enables learners to navigate interdisciplinary knowledge, reflect critically on design processes and co-create regenerative solutions. By positioning nature as mentor, model, and measure, this pedagogic framework reimagines architectural education as a catalyst for sustainability and systemic change in the built environment. Full article

Large Language Models (LLMs) have demonstrated strong performance on English-language medical exams, but their effectiveness in non-English, high-stakes environments is less understood. This study benchmarks nine LLMs against human examinees on the Chilean Anesthesiology Certification Exam (CONACEM), a Spanish-language board examination. A curated set of 63 multiple-choice questions was used, categorized by Bloom’s taxonomy into four cognitive levels. Model responses were assessed using Item Response Theory and Classical Test Theory, complemented by additional error analysis, categorizing errors as reasoning-based, knowledge-based, or comprehension-related. Closed-source models surpassed open-source models, with GPT-o1 achieving the highest accuracy (88.7%). Deepseek-R1 is a strong performer among open-source options. Item difficulty significantly predicted the model accuracy, while discrimination did not. Most errors occurred in application and understanding tasks and were linked to flawed reasoning or knowledge misapplication. These results underscore LLMs’ potential for factual recall in Spanish medical exams but also their limitations in complex reasoning. Incorporating cognitive classification and error taxonomy provides deeper insights into model behavior and supports their cautious use as educational aids in clinical settings. Full article

The question generation system (QGS) for information technology (IT) education, designed to create, evaluate, and improve Multiple-Choice Questions (MCQs) using knowledge graphs (KGs) and large language models (LLMs), encounters three major needs: ensuring the generation of contextually relevant and accurate distractors, enhancing the diversity of generated questions, and balancing the higher-order thinking of questions to match various learning levels. To address these needs, we proposed a multi-agent system named Multi-Examiner, which integrates KGs, domain-specific search tools, and local knowledge bases, categorized according to Bloom’s taxonomy, to enhance the contextual relevance, diversity, and higher-order thinking of automatically generated information technology MCQs. Our methodology employed a mixed-methods approach combining system development with experimental evaluation. We first constructed a specialized architecture combining knowledge graphs with LLMs, then implemented a comparative study generating questions across six knowledge points from K-12 Computer Science Standard. We designed a multidimensional evaluation rubric to assess the semantic coherence, answer correctness, question validity, distractor relevance, question diversity, and higher-order thinking, and conducted a statistical analysis of ratings provided by 30 high school IT teachers. Results showed statistically significant improvements (p < 0.01) with Multi-Examiner outperforming GPT-4 by an average of 0.87 points (on a 5-point scale) for evaluation-level questions and 1.12 points for creation-level questions. The results demonstrated that: (i) overall, questions generated by the Multi-Examiner system outperformed those generated by GPT-4 across all dimensions and closely matched the quality of human-crafted questions in several dimensions; (ii) domain-specific search tools significantly enhanced the diversity of questions generated by Multi-Examiner; and (iii) GPT-4 generated better questions for knowledge points at the “remembering” and “understanding” levels, while Multi-Examiner significantly improved the higher-order thinking of questions for the “evaluating” and “creating” levels. This study contributes to the growing body of research on AI-supported educational assessment by demonstrating how specialized knowledge structures can enhance automated generation of higher-order thinking questions beyond what general-purpose language models can achieve. Full article

This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity. 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