Search Results (32)

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

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 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 transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning exam questions with course objectives, improving clarity and difficulty, and generating new items guided by learning goals. The research spans four university courses—two theory-focused and two application-focused—covering diverse cognitive levels according to Bloom’s taxonomy. A balanced dataset ensures representation of question categories and structures. Three LLM-based agents—VectorRAG, VectorGraphRAG, and a fine-tuned LLM—are developed and evaluated against a meta-evaluator, supervised by human experts, to assess alignment accuracy and explanation quality. Robust analytical methods, including mixed-effects modeling, yield actionable insights for integrating generative AI into university assessment processes. Beyond exam-specific applications, this methodology provides a foundational approach for the broader adoption of AI in post-secondary education, emphasizing fairness, contextual relevance, and collaboration. The findings offer a comprehensive framework for aligning AI-generated content with learning objectives, detailing effective integration strategies, and addressing challenges such as bias and contextual limitations. Overall, this work underscores the potential of generative AI to enhance educational assessment while identifying pathways for responsible implementation. Full article

The growing attention to virtual reality (VR) suggests that immersive technologies will be widely applied across various contexts, including education. However, conceptual divergences and a lack of understanding regarding the learning process in immersive virtual environments reveal the nascent understanding of the use of immersive virtual reality (iVR) and its benefits in educational contexts. Against this backdrop, this article presents a proposed pedagogical framework aimed at aligning the affordances of iVR environments—such as immersion, interactivity, and embodiment—with educational objectives, thereby enhancing learning experiences. Developed through a design-based research (DBR) methodology, the framework integrates theoretical contributions from the CAMIL model and the XR ABC framework in conjunction with the revised Bloom’s taxonomy. The structure organizes immersive experiences into three levels—Absorb, Experience, and Explore—which are distinguished by the interactivity and complexity of the proposed tasks. Each level is mapped to the cognitive domains and dimensions of knowledge, promoting the progressive development of cognitive and psychomotor skills. It is posited that aligning the technological features of the environment with educational objectives and the level of active student engagement can facilitate learning in immersive environments. Full article

An important goal in the reform of architectural design education is to instruct students in ways of acquiring relevant site information quickly and efficiently during a design project, and then integrating that information into their architectural designs. This study focuses on a teaching experiment conducted within the “Urban Village Renovation Design” course for third-year undergraduates at Zhejiang University. This study aims to improve teaching efficiency by combining active and passive information acquisition methods during the site information acquisition stage. A teaching experiment on “Urban Village Renovation Design” was conducted with third-year undergraduates at Zhejiang University, comparing two experimental groups based on whether the teacher provides site information reports (i.e., passive information acquisition). The study explores efficient methods for acquiring different types of site information in architectural design teaching and develops a matching framework. It evaluates the impact of active vs. passive methods on students’ cognitive levels, using Bloom’s taxonomy, and quantitatively tests cognitive efficiency differences through the ROI model. Results show that combining both methods yields the highest teaching efficiency, with specific types of information corresponding to effective active or passive acquisition methods. This study explores which research methods can yield beneficial site information more efficiently and clarifies the role of previously overlooked passive information acquisition methods in site cognition, providing theoretical support for the design of teaching plans during the research phase. From a practical standpoint, it is suggested that instructors provide certain site information directly rather than have students acquire it independently, to shorten the research phase of teaching and simultaneously enhance site cognition efficiency. Full article

In an e-learning environment, question levels are based on Bloom’s Taxonomy (BT), which normally classifies a course’s learning objectives into diverse levels. As per the previous literature, the assessment procedure lacks accuracy and results in redundant keywords when automatically assigning Bloom’s taxonomic categories using a keyword-based approach. These assessments are considered challenging as far as e-learning-based students are concerned, as the text feed is the only instrumental testing part. Student assessments are limited to multiple-choice questions and lack an evaluation of students’ text-based input. This paper proposes a natural-language processing-based intelligent deep-learning model that relies on parametric cognitive assessments. By applying class labels to students’ descriptive responses, the proposed approach helps classify a variety of questions mapped to BT levels. The first contribution of this work is a compiled dataset of the assessment items from 300 students, who were tested on 20 questions at each level. Each level is calculated by combining the responses from all students, resulting in 6000 questions per cognitive level for a total of 36,000 records. The second contribution is the development of an intelligent model based on a recurrent neural network (RNN), which not only predicts Bloom’s question level but also learns it over further iterations. The students’ text-based answers are accessed to gauge performance using a refined question pool gathered through the RNN model. The student dataset is mapped and tested using the NLP model for further classification of the students’ cognitive levels. This assessment is related to the formulation of questions and the compilation of Episode 2 for assessment. The third contribution is the comparison and demonstration of the improvements in learning using a parametric cognitive-based assessment in an episodic manner. Improved classification accuracy was attained by adding more processing layers based on the iterative, RNN-based learning model to achieve the vital threshold difference. The cognitive based questions pool classification achieved by RNN results in 98% accuracy. The resulting student assessments, based on performance, increased to an accuracy ratio of 92.16% and a precision ratio of 92.36% at an aggregate level based on the Random Forest classifier. We claim that our work serves as an initiative for effective student evaluations in interactive and e-learning-based environments when handling other types of inputs, like mathematical, graphical, and multimodal inputs. Full article

It is important that engineers are educated to consider sustainability in their work. Thermodynamics is a fundamental course required in several engineering majors that has a natural connection to sustainability topics (e.g., energy and limits on efficiency). This work examined how sustainability was included in university-level engineering thermodynamics courses, based on 18 peer-reviewed papers that described Scholarship of Teaching and Learning studies. This review found that environmental issues were included in 15 courses, social issues in 9 courses, and economic issues in 5. There were 11 papers that included topics related to one or more of the United Nations’ Sustainable Development Goals (SDGs), with 8 of the 17 SDGs represented by one or more papers. The learning outcomes from the courses provided many examples of cognitive outcomes at all six levels of Bloom’s taxonomy. In contrast, affective domain outcomes were generally not explicit. Methods of integrating sustainability topics included mathematical examples, labs, projects, service-learning, application-based learning, simulation tools, and book reviews. These examples should inspire instructors to foster sociotechnical mindsets toward engineering, which are a key to educating engineers who value sustainability and who will advocate for its importance in engineering. Full article

Environmental unsustainability in coupled human–nature systems is accumulating. Yet, there is no accreditation requirement for unsustainability resolution competency in higher education. Thus, a new and complete representation of the pedagogy for unsustainability resolution competence has been induced, using what is already available and working. The nature of unsustainability problems points to collaboration and holism attitudes. Resolution requires social skills, namely participation, perspective taking, and the generation of social capital, and cognitive skills, namely project management, knowledge building, and modeling. Resolution is scaffolded in three successive steps during the collaborative process within a systems approach: (i) collapse complexity; (ii) select a path/trajectory; and (iii) operationalize a plan. The hierarchically cumulative abilities toward unsustainability resolution competence are to source data and information about the coupled human–nature system (SEARCH); simplify the dynamics of the human–nature system (SIMULATE); generate and test alternative paths and end points for the coupled human–nature system (STRATEGIZE); chose a favorable path among the available alternatives (SELECT); operationalize the favorable path into a plan (strategy–program–project) with measurable management and policy objectives (IMPLEMENT); and develop criteria/indicators to monitor and adjust when necessary the implementation of the plan toward system goals (STEER). For each one of these learning objectives, the Bloom’s taxonomy and a progression from behaviorist through cognitivist to constructivist tools apply. The development of mastery requires the comparison and contrast of many similar cases with the same unsustainability problem and project-based learning with specific cases for deep learning. In this way, it is the resolutions of unsustainability in human–nature systems that will be accumulating. Full article

When General Chemistry at West Point switched from interactive lectures to guided inquiry, it provided an opportunity to examine what was expected of students in classrooms and on assessments. Learning objectives and questions on mid-term exams for four semesters of General Chemistry I (two traditional semesters and two guided inquiry semesters) were analyzed by the Cognitive Process and Knowledge dimensions of Bloom’s revised taxonomy. The results of this comparison showed the learning objectives for the guided inquiry semesters had a higher proportion of Conceptual and Understand with a corresponding decrease of Factual, Procedural, Remember and Apply learning objectives. On mid-term exams, the proportion of Remember, Understand, Analyze/Evaluate, Factual, and Conceptual questions increased. We found that guided inquiry learning objectives and mid-term exam questions are more conceptual than traditional courses and may help explain how active learning improves equity in introductory chemistry. Full article

The paper aims to identify and analyze the correlation between student personality types and the use of metaverse and flipped classroom blended learning methods (BLMs) and tools by formulating a series of research hypotheses. Using Bloom’s Taxonomy, the most influential and standard theory of learning in the education cognitive field and toward this objective, the authors extracted the personality types of students and employed a mixed-methods research methodology JASP software (v.0.17.1) involving both qualitative and quantitative tools. The qualitative component involved direct observation of synchronous classroom teaching to students, while the quantitative aspect utilized structured questionnaires administered to 634 students of the International Hellenic University enrolled to attend the “Human Resource Management” course. The acquired qualitative data were processed using (a) network analysis JASP software (v.0.17.1) software in order to address the student personas through nodes, connections, and centralities and (b) structural equation software in order to identify the correlations between types of students and the variables of the metaverse and flipped classroom methods. The findings reveal that the four types of students identified have a direct and strong correlation with the use of flipped classroom and metaverse teaching methods. Full article