Search Results (303)

Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

Oral tradition stories hold a vital place in language education, offering rich repositories of linguistic, cultural, and historical knowledge. In the Spanish as a Foreign Language (SFL) context, their inclusion provides dynamic opportunities to explore diversity, foster critical and creative thinking, and challenge dominant epistemologies. Despite their pedagogical potential, these narratives remain largely absent from formal curricula, with most SFL textbooks still privileging canonical works, particularly those from the Latin American Boom or European literary texts. This article aims to provide practical guidance for SFL instructors on designing effective, culturally responsive materials for the teaching of vocabulary. Drawing on a methodological framework for material design and a cognitive approach to vocabulary learning, I present original pedagogical material based on a Colombian oral tradition story about the witches of La Jagua (Huila, Colombia) to inspire educators to integrate oral tradition stories into their classrooms. As argued throughout, oral narratives not only support vocabulary acquisition and intercultural competence but also offer students meaningful engagement with the values, worldviews, and linguistic diversity that shape Colombian culture. This approach redefines language teaching through a more descriptive, contextualized, and culturally grounded lens, equipping learners with pragmatic, communicative, and intercultural skills essential for the 21st century. My goal with this article is to advocate for teacher agency in material creation, emphasizing that educators are uniquely positioned to design pedagogical resources that reflect their own cultural realities and local knowledge and to adapt them meaningfully to their students’ needs. Full article

ICT tools are fundamental for promoting educational inclusion, as they allow for flexible teaching practices adapted to the diversity of students. Their appropriate integration into teaching makes it easier to respond to different paces, styles, and needs, promoting the active and meaningful participation of all learners. However, this inclusive potential is only fulfilled if equitable access to and use of ICT is guaranteed, both at school and at home. This qualitative study explores the digital divide in its three main dimensions: access to technology, digital skills, and the meaningful use of ICT in educational settings. Through focus groups and semi-structured interviews, the study gathers the perceptions of different members of the educational community. The sample consists of 89 participants, including teachers, students, families, and school administrators from the Communities of Madrid and Castilla-La Mancha. The results reveal a common concern about inequalities in ICT access and use, related to economic, geographical, and educational factors. These findings emphasise the need for targeted public policies to bridge digital gaps and highlight the importance of promoting digital competence across the entire educational community to achieve true educational inclusion. Full article

Inclusive Education aims to ensure equitable learning opportunities for all students, including those with special educational needs (SEN) and disabilities, by promoting accessible teaching practices and supportive learning environments. Although its importance for fostering the academic and social development of diverse learners has been widely recognized in international policy and research, its practical implementation is still under investigation, particularly within the Greek primary education system. This study investigates (a) Greek primary school teachers’ perspectives, focusing on their attitudes, knowledge, challenges, and perceived needs in Inclusive Education for SEN students and (b) research trends relating to these perspectives, focusing on publication trends and methodological characteristics, through a systematic literature review using the PRISMA methodology. The analysis revealed a shift in Greek primary teachers’ attitudes towards Inclusive Education for SEN students, trending towards neutral or negative responses, alongside limited knowledge, various challenges, and an absence of targeted support mechanisms. The analysis also highlighted chronological gaps between the identified studies in international journals and a variability in methodological approaches and sample characteristics. These insights point to a pressing need for further targeted and ongoing research on Greek primary teachers’ perspectives and professional development initiatives to enable effective and inclusive practices for SEN students in Greek primary education. Full article

Background/Objectives: The use of simulation has become more popular in healthcare settings, and simulation is also very popular in ultrasound training, allowing the learners to virtually practice and improve clinical skills. Obstetric pathology and gynecologic lesions can have a large range of sonographic features, and the detection rates for these can be increased by using ultrasound simulation systems to train users. In the following paper, we provide insight into the application of simulation tools in obstetric and gynecologic ultrasound training. Methods: We present different ultrasound models for GYN/OB ultrasound training. Results: Ultrasound simulation is a key component of obstetrics and gynecology (OB/Gyn) ultrasound education. Conclusions: By examining the best practices, we highlight the diverse simulation options available to help learners technical and non-technical skills in a controlled learning environment. Full article

Background/Objectives: Vertebral degenerative features are promising but often subjectively scored indicators for adult age estimation. We evaluated an objective surface roughness metric, the “average distance to the fitted ellipse” score (DS), calculated automatically for every vertebra from C7 to S1 on routine CT images. Methods: CT scans of 176 adults (94 males, 82 females; 21–94 years) were retrospectively analyzed. For each vertebra, the mean orthogonal deviation of the anterior superior endplate from an ideal ellipse was extracted. Sex-specific multiple linear regression served as a baseline; support vector regression (SVR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian naïve-Bayes pseudo-regressor (GNB-R) were tuned with 10-fold cross-validation and evaluated on a 20% hold-out set. Performance was quantified with the standard error of the estimate (SEE). Results: DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). Linear regression explained 40% (males) and 47% (females) of age variance (SEE ≈ 11–12 years). Non-parametric learners improved precision: RF achieved an SEE of 8.49 years in males (R² = 0.47), whereas k-NN attained 10.8 years (R² = 0.45) in women. Conclusions: Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption. Full article

As generative artificial intelligence (GenAI) chatbots gain traction in educational settings, a growing number of studies explore their potential for personalized, scalable learning. However, methodological fragmentation has limited the comparability and generalizability of findings across the field. This study proposes a unified, learning analytics–driven framework for evaluating the impact of GenAI chatbots on student learning. Grounded in the collection, analysis, and interpretation of diverse learner data, the framework integrates assessment outcomes, conversational interactions, engagement metrics, and student feedback. We demonstrate its application through a multi-week, quasi-experimental study using a Socratic-style chatbot designed with pedagogical intent. Using clustering techniques and statistical analysis, we identified patterns in student–chatbot interaction and linked them to changes in learning outcomes. This framework provides researchers and educators with a replicable structure for evaluating GenAI interventions and advancing coherence in learning analytics–based educational research. Full article

The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article

Despite robust evidence linking career adaptability (CA) to positive vocational and psychosocial outcomes in general populations, research on the CA among individuals with special educational needs (SEN) remains limited. Prior reviews have largely overlooked the distinct challenges faced by SEN populations. To address this gap, we conducted a systematic review across five major databases, yielding an initial pool of 81 studies. Following rigorous screening, only eight quantitative studies met the inclusion criteria, reflecting the early stage of the research in this area. The included studies span diverse SEN groups, including individuals with visual impairments, intellectual disabilities, and mental health conditions. CA was consistently found to be associated with adaptive outcomes such as self-esteem, self-efficacy, hope, and career satisfaction. However, the literature is characterized by methodological limitations, notably the predominance of cross-sectional designs, the underrepresentation of neurodevelopmental conditions (e.g., ASD, ADHD), and a lack of cross-cultural perspectives and standardized instruments specifically adapted to SEN learners. Future studies should focus on the need for longitudinal and mixed-method designs, contextually cross-cultural research, and inclusive measurement tools. Furthermore, exploring the ecological and emotional predictors of CA; expanding to underrepresented SEN subgroups; and evaluating diverse interventions beyond mentoring are essential to informing tailored educational and vocational support for individuals with SEN. Full article

The current study examines the determinants influencing preparatory year (PY) students’ intentions to adopt AI-powered natural language processing (NLP) models, such as Copilot, ChatGPT, and Gemini, and how these intentions shape their conceptions of digital sustainability. Additionally, the extended unified theory of acceptance and use of technology (UTAUT) was integrated with a diversity of educational constructs, including content availability (CA), learning engagement (LE), learning motivation (LM), learner involvement (LI), and AI satisfaction (AS). Furthermore, responses of 274 PY students from Saudi Universities were analysed using partial least squares structural equation modelling (PLS-SEM) to evaluate both the measurement and structural models. Likewise, the findings indicated CA (β = 0.25), LE (β = 0.22), LM (β = 0.20), and LI (β = 0.18) significantly predicted user intention (UI), explaining 52.2% of its variance (R² = 0.522). In turn, UI significantly predicted students’ digital sustainability conceptions (DSC) (β = 0.35, R² = 0.451). However, AI satisfaction (AS) did not exhibit a moderating effect, suggesting uniformly high satisfaction levels among students. Hence, the study concluded that AI-powered NLP models are being adopted as learning assistant technologies and are also essential catalysts in promoting sustainable digital conceptions. Similarly, this study contributes both theoretically and practically by conceptualising digital sustainability as a learner-driven construct and linking educational technology adoption to its advancement. This aligns with global frameworks such as Sustainable Development Goals (SDGs) 4 and 9. The study highlights AI’s transformative potential in higher education by examining how user intention (UI) influences digital sustainability conceptions (DSC) among preparatory year students in Saudi Arabia. Given the demographic focus of the study, further research is recommended, particularly longitudinal studies, to track changes over time across diverse genders, academic specialisations, and cultural contexts. Full article

This study aims to improve the accuracy and interpretability of deep groundwater level forecasting in Cangzhou, a typical overexploitation area in the North China Plain. To address the limitations of traditional models and existing machine learning approaches, we develop a Stacking ensemble learning framework that integrates meteorological, spatial, and anthropogenic variables, including lagged groundwater levels to reflect aquifer memory. The model combines six heterogeneous base learners with a meta-model to enhance prediction robustness. Performance evaluation shows that the ensemble model consistently outperforms individual models in accuracy, generalization, and spatial adaptability. Scenario-based simulations are further conducted to assess the effects of the South-to-North Water Diversion Project. Results indicate that the diversion project significantly mitigates groundwater depletion, with the most overexploited zones showing water level recovery of up to 17 m compared to the no-diversion scenario. Feature importance analysis confirms that lagged water levels and pumping volumes are dominant predictors, aligning with groundwater system dynamics. These findings demonstrate the effectiveness of ensemble learning in modeling complex groundwater behavior and provide a practical tool for water resource regulation. The proposed framework is adaptable to other groundwater-stressed regions and supports dynamic policy design for sustainable groundwater management. Full article

Attaining psychosocial health for learners self-identifying as burned out is challenging. Yet, positive psychosocial outcomes are possible. Learner burnout is reducible if learners accept their and others’ rights to self-direct their learning. This acceptance requires a community that demonstrates team mindfulness. Successful self-directed learning with team mindfulness is possible at diverse academic levels and in various public education settings. The author co-founded three such educational initiatives aiming to reduce burnout in learners. To reveal the results, the author assesses the total works published since 2020 regarding these initiatives, using narrative methodology. Some form of consensus decision-making is imperative for team mindfulness—it may take different forms. For these initiatives to succeed online, a participant-trusted facilitator who takes on the role of an authentic leader is necessary. If one is lacking, the participants may achieve positive psychological outcomes but not the positive social consequences of a decision-making method upholding team mindfulness. In working with burned-out learners, positive sociological outcomes are possible when a group focuses on self-directed learning and has a learning-related team mindfulness goal in common. By summarizing the positive psychosocial effects regarding burnout and outlining the difficulties of these publicly supported programs for self-directed learning, future research directions are suggested on this topic. Full article

This study presents a hybrid ensemble learning framework for the joint detection and motor severity prediction of Parkinson’s disease (PD) using biomedical voice features. The proposed architecture integrates a deep multimodal fusion model with dense expert pathways, multi-head self-attention, and multitask output branches to simultaneously perform binary classification and regression. To ensure data quality and improve model generalization, preprocessing steps included outlier removal via Isolation Forest, two-stage feature scaling (RobustScaler followed by MinMaxScaler), and augmentation through polynomial and interaction terms. Borderline-SMOTE was employed to address class imbalance in the classification task. To enhance prediction performance, ensemble learning strategies were applied by stacking outputs from the fusion model with tree-based regressors (Random Forest, Gradient Boosting, and XGBoost), using diverse meta-learners including XGBoost, Ridge Regression, and a deep neural network. Among these, the Stacking Ensemble with XGBoost (SE-XGB) achieved the best results, with an R² of 99.78% and RMSE of 0.3802 for UPDRS regression and 99.37% accuracy for PD classification. Comparative analysis with recent literature highlights the superior performance of our framework, particularly in regression settings. These findings demonstrate the effectiveness of combining advanced feature engineering, deep learning, and ensemble meta-modeling for building accurate and generalizable models in voice-based PD monitoring. This work provides a scalable foundation for future clinical decision support systems. Full article

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning. Full article