Soft skills correspond to intrapersonal and interpersonal abilities related to how individuals interact, make decisions, and manage their activities. In the context of undergraduate nursing education, their development is fundamental to the preparation of professionals capable of acting in an ethical, critical, and relational manner, making it relevant to understand how these competencies are incorporated into the teaching and learning process. In this context, the objective of this study is to understand how faculty members in undergraduate nursing programs incorporate soft skills into their pedagogical approaches and practices, identifying the competencies considered essential and the challenges to their implementation. A qualitative study was conducted with 26 nursing faculty members from four federal public universities in southern Brazil. Data were collected between June and September 2025 through semi-structured interviews, following the criteria of the Consolidated Criteria for Reporting Qualitative Research checklist. The interviews were processed using IRaMuTeQ software and analyzed in light of Discursive Textual Analysis. Three analytical categories emerged: faculty understanding of soft skills in nursing education; pedagogical approaches and strategies for the development of these competencies; and perceived difficulties in their promotion within teaching. The faculty members recognize the relevance of soft skills and report the use of active methodologies and reflective strategies for their development. However, most had not received specific training, and the teaching of these competencies occurs predominantly in an implicit manner. The findings demonstrate that, although soft skills are widely valued in nursing education, their promotion still lacks pedagogical systematization and institutional support. Challenges such as the subjectivity of these competencies, the prioritization of technical skills by students, and distractions associated with the use of technologies limit their intentional development. These results contribute to the international literature in nursing education by highlighting the need for structured institutional strategies for faculty development and for the explicit integration of soft skills into nursing curricula.
The rapid integration of generative artificial intelligence (GenAI) tools, such as ChatGPT, into educational contexts has raised important questions regarding how adolescents conceptualize and make sense of these technologies. Understanding students' perceptions is essential for developing age-appropriate, ethical, and pedagogically sound approaches to AI use in secondary education. This descriptive qualitative study employed a phenomenological approach and metaphor analysis to explore secondary school students' perceptions of generative artificial intelligence. The study sample consisted of 332 students aged 14-18 years from four secondary schools in Türkiye. Data were collected using an open-ended prompt ("Generative artificial intelligence is like … because …") and analyzed through content analysis. Metaphors were categorized based on shared semantic and conceptual features, and inter-rater reliability was established using Cohen's kappa (κ = 0.92). Analysis revealed ten metaphor categories clustered under five overarching themes: generative artificial intelligence as (1) a source of knowledge, (2) a teaching and guiding entity, (3) a supportive and assisting tool, (4) a reflection of human intelligence, and (5) a dual-purpose (beneficial-risky) technology. Students most frequently conceptualized GenAI as a comprehensive knowledge source (e.g., book, encyclopedia) and as a human-like cognitive entity (e.g., brain, wise person). At the same time, metaphors reflecting ethical awareness and potential risks, such as misuse and overreliance, were also identified. The findings indicate that secondary school students hold multifaceted and nuanced perceptions of generative artificial intelligence, encompassing both educational opportunities and ethical concerns. These results highlight the importance of integrating AI literacy into secondary education in ways that promote critical thinking, responsible use, and awareness of GenAI's limitations alongside its potential benefits. It was determined that secondary school students perceive generative artificial intelligence ambivalently as both a useful tool and a source of ethical and emotional concern, highlighting the need for developmentally appropriate artificial intelligence literacy approaches. • GenAI tools such as ChatGPT are increasingly integrated into educational contexts and have the potential to support personalized learning, information access, and student engagement. • Existing research has primarily focused on educators' perspectives or higher education settings, while studies examining adolescents' perceptions of GenAI remain limited. • This study provides empirical evidence on secondary school students' metaphorical perceptions of generative artificial intelligence within a K-12 context. • Findings reveal that adolescents conceptualize GenAI in multifaceted ways, including as a knowledge source, teaching and guiding entity, supportive tool, reflection of human intelligence, and a dual-purpose (beneficial-risky) technology.
Clinical empathy refers to a healthcare professional's ability to understand a patient's experiences and emotions through cognitive and affective perspective taking, and to communicate that understanding through compassionate and appropriate professional behaviors. Aging simulation suits are experiential educational tools designed to replicate the sensory and physical limitations associated with aging. However, evidence regarding their effectiveness in enhancing clinical empathy among active healthcare professionals remains limited. This study aimed to evaluate the effects of an aging simulation suit on clinical empathy among healthcare professionals working in long-term care settings. A randomized controlled trial was conducted with 82 healthcare professionals from four nursing homes in Madrid and Asturias (Spain). Participants were randomly assigned to an experimental group (EG) (n=41) or a control group (CG) (n=41). Both groups received the same structured educational session on empathy and aging. The experimental group additionally participated in an immersive experience using the GERT aging simulation suit, whereas the control group did not receive the simulation component. Self-reported empathy were measured pre- and post-intervention using the Interpersonal Reactivity Index (IRI) and the Jefferson Scale of Empathy-Health Professions version (JSPE-HPS). No significant differences were found between groups in IRI scores. However, the experimental group showed significant improvements in total JSPE-HPS scores and in the subscales Perspective Taking and Compassionate Care (p < 0.05), compared with the control group. These findings suggest that the immersive intervention enhanced both cognitive and affective components of clinical empathy. The use of an aging simulation suit was associated with improvements in specific dimensions of clinical empathy among healthcare professionals working in long-term care. This educational tool offers a valuable experiential approach that enhances understanding and compassion toward older adults. However, these findings are limited to short-term, self-reported measures, and no behavioral or patient outcome data were collected. Further longitudinal studies are needed to determine the long-term sustainability of these effects and their translation into clinical practice. ClinicalTrials.gov, Unique Protocol ID: 2711201916919; ClinicalTrials.gov ID: NCT07280689. Date of registration: 10/10/2025. Retrospectively registered.
Effective patient education is critical for informed consent. Augmented Reality (AR) offers a novel approach to improving patient understanding and satisfaction, although current evidence is limited and of low quality. This study evaluated the added value of AR compared to traditional monitor-based 3D models for patient education and decision making for orthognathic surgery. A multicentre randomised controlled trial was conducted between August 2023 and June 2024 at three university medical centres in north-west Europe. Sixty referred patients were randomised to either the intervention group or the control group. Patient satisfaction and knowledge were assessed using two questionnaires. The study was registered at ClinicalTrials.gov(NCT06140043). Patient satisfaction was significantly higher in the control group (p = 0.04). No significant differences were found in knowledge acquisition (p = 0.74). Women showed a significant preference for the monitor-based consultation (p = 0.01), while men did not show a significant difference in satisfaction. Despite some centre heterogeneity, no clear added benefit of AR in satisfaction or knowledge was visible. Observed effect sizes (d = 0.76) and post-hoc power estimates (84%) are provided for context but should be interpreted cautiously. In the absence of clear evidence a monitor remain the most practical option for patient education.
To develop a medical gratitude scale for medical students, and to investigate the relationship between medical gratitude and general attitude. A cross-sectional study was conducted in Guangdong, China. We used a stratified cluster sampling strategy to select 500 eligible clinical medical students in Guangdong, China. A self-administered medical gratitude scale was developed to measure the medical-specific gratitude. Fornell-Larcker criterion was used to estimate the discrimination of these two scales. A hierarchical multiple regression was used to estimate the relationships. 482 valid questionnaires were finally collected. Factor analysis showed the acceptable reliability and validity of the medical gratitude scale (Cronbach's α = 0.930). The mean scores for medical and general gratitude scales were 3.33 ± 0.42 and 3.11 ± 0.66, respectively. Fornell-Larcker criterion confirmed the discrimination between general and medical gratitude. Hierarchical multiple regression estimated the positive relationship between medical and gratitude (β = 0.46, 95%CI 0.38, 0.54, p < 0.001). Medical gratitude was also positively associated with the frequency of gratitude education received from teachers (β = 0.10, 95% CI 0.02, 0.18, p = 0.018) and from schools (β = 0.16, 95% CI 0.08, 0.25, p < 0.001). While correlated with general gratitude, medical gratitude appears to be conceptually distinct in its focus on the professional context. Integrating gratitude education into the medical school curriculum is a viable strategy to foster professionalized future physicians.
In the digital age, university students' sustained academic engagement and strong learning resilience in the face of increasing academic pressure and complex campus challenges are essential to the attainment of substantial academic achievement. At present, how to enhance students' academic engagement and foster learning resilience has become a pressing issue for educational administrators. Although previous studies have examined multiple factors influencing academic engagement and resilience, they have largely emphasized the isolated effects of psychological traits on individual learning performance while overlooking the complex possibility that perceived external contexts, such as the learning environment, learning climate, and social relationships, may jointly shape learning resilience through psychological and emotional regulatory mechanisms. Therefore, this study focuses on the interaction among external contexts, internal affective drivers (academic self-efficacy and perceived campus belonging), and learning resilience. Using questionnaire survey data and structural equation modeling, this study examines the extent to which external contexts are associated with academic self-efficacy and perceived campus belonging, explores whether these internal affective drivers are statistically associated with learning resilience through mediating pathways, and constructs an "external context-affective drivers-learning resilience" model to identify potential explanatory pathways and provide evidence-based implications for educational management.
Motivational factors are widely recognized as central to students' engagement in cognitively demanding learning; however, the role of STEM career interest in the development of computational thinking during adolescence remains insufficiently understood. It is also unclear whether this association differs by gender. Grounded in Social Cognitive Career Theory, this study examined the association between STEM career interest and computational thinking among high school students and tested the moderating role of gender. Data were collected from 467 students (Mage = 16.05, SD = 1.20; 57.2% female) enrolled in public science high schools in Diyarbakır, Türkiye, using a descriptive correlational design. Participants completed the STEM Career Interest Scale and the Computational Thinking Skills Scale. Moderation analysis was conducted using PROCESS (Model 1) with 5,000 bootstrap resamples. STEM career interest was positively associated with computational thinking. Gender showed no significant main effect, and the interaction between STEM career interest and gender was not significant, indicating that the strength of this association was similar for female and male students. These findings suggest that, within academically selective STEM-focused environments, motivational orientations toward STEM are linked to computational thinking in comparable ways across genders. The results highlight the importance of supporting students' motivational engagement, alongside instructional practices, in fostering computational thinking during secondary education.
Oral lichen planus is a chronic disease of the oral mucosa, with pain as one of its main symptoms. This study aimed to assess the correlation between the results of four pain intensity measures-including the Visual Analog Scale (VAS), Numeric Rating Scale (NRS), Short Form McGill Pain Questionnaire (SF-MPQ), and Verbal Rating Scale (VRS)- in a population of patients diagnosed with oral lichen planus. In this prospective observational study, 66 patients with oral lichen planus participated. Four pain assessment scales were used, including VAS, NRS, VRS, and SF-MPQ. Participants completed these assessments at baseline and again after two weeks of treatment. A paired t-test, Spearman correlation, linear regression analysis, and adjusted multiple regression analysis (regarding age and level of education) were used to analyze the data. All four scales were sensitive to changes in pain after treatment and a significant reduction in pain scores was observed (p < 0.001). There was a strong positive correlation between all scales (p < 0.001). Regression analysis showed that scores on each scale could significantly predict scores on the other scales (p < 0.001). Multiple regression analysis adjusted for age and level of education, showed the correlations between the pain scales remained strong and significant (p < 0.001). These commonly used pain assessment scales showed strong correlation with each other, and it seems that the results obtained from each might be comparable with the others. However, further researches in larger studies and different populations are needed.
Breast cancer patients often experience significant psychological distress. This study examined distress trajectories from diagnosis to 6 months post-treatment and explored differences across demographic, medical, and psychosocial subgroups. In this prospective cohort study, 528 patients with breast cancer were recruited between 1 December 2023 and 31 December 2024. Assessments were conducted at baseline (at diagnosis, T0), after the first treatment (T1), mid-treatment (T2), at treatment completion (T3), and at three (T4) and six months (T5) post-treatment. Growth mixture modeling (GMM) was used to identify distinct trajectories of psychological distress. Multinomial logistic regression analysis was performed to examine associations between patient-related factors and trajectory membership. Three psychological distress trajectories were identified: a high-distress remission group (17.05%), a moderate-stable distress group (11.93%), and a low-fluctuating distress group (71.02%). Multivariable analyses showed that higher educational attainment, breast-conserving surgery, early disease stage, partial self-management ability, and strong social support were associated with membership in the moderate-stable or low-fluctuating groups (p < 0.05). Employment, health insurance coverage, avoidant medical coping style, and higher baseline anxiety and depression scores were concurrently associated with membership in the high-distress remission group (p < 0.05). Although psychological distress generally decreased over time, 71.02% of patients followed a low-fluctuating trajectory, 11.93% maintained moderate distress with potential risk of persistence, and 17.05% showed high initial distress that remitted substantially within 6 months. Continuous monitoring and early psychosocial support are recommended, particularly for patients with moderate- or high-risk trajectories.
Congenital anomalies and genetic disorders contribute substantially to perinatal morbidity and mortality, particularly in low- and middle-income countries. Prenatal healthcare providers play a key role in identifying affected pregnancies and referring to patients for genetic counselling; however, referral practices remain suboptimal. To assess the utilisation of genetic counselling services and perceptions of genetic counselling among prenatal healthcare providers in Gauteng Province, South Africa. An electronic survey was distributed to prenatal healthcare providers working in public and private healthcare sectors in Gauteng. The survey assessed access to genetic counselling services, referral practices, knowledge of referral indications, understanding of the genetic counsellor's role, and perceived barriers to referral. Fifty-four respondents were included. Seventy-four percent of participants reported being able to refer to patients for genetic counselling, but only 57% had utilised the service. No participant correctly identified all appropriate referral indications, and only 24% understood the scope of practice of genetic counsellors. Only 6% felt confident in their knowledge of genetic counselling. Although genetic counselling services are available and utilised in Gauteng, they are not accessed to their full potential. Improved education and clearer referral guidance are required to optimise prenatal genetic care in this setting.
Zoonotic diseases are common threats to global health. A large number of infectious diseases are transmitted from animals to humans. The current study aimed to assess the community's knowledge, attitudes, and practices (KAP) regarding common zoonotic diseases in the Arbaminch district. A cross-sectional survey was carried out between November 2024 and June 2025. A total of 384 participants were interviewed in the study. Participants residing in these areas were randomly chosen. Data were collected using a structured questionnaire. The collected data were analyzed using Stata 17, and the results were reported using descriptive statistics and the chi-square test. The findings of this study revealed that a majority (55%) of participants had good knowledge about zoonotic diseases. Respondents know several modes of transmission for zoonotic diseases, with animal bites (32.5%) being the most recognized, followed by direct contact (15.5%), ingestion of raw products (10%), and inhalation (10%). Regarding attitudes, 63.2% of respondents exhibited a positive attitude towards the importance of zoonotic disease prevention and control, and 67.4% of respondents followed relatively good hygiene and preventive behaviors. However, risky practices were still common. Knowledge score showed a significant association with age. Attitudes of participants were significantly associated with education, age, occupation, and income. Similarly, practices were significantly associated with gender, education level, occupation, and income, with all associations being statistically significant (p < 0.05). The overall community knowledge, attitudes, and practices regarding zoonotic diseases were relatively good.
With rising demand for remote work and education, smartphones and other portable photographic devices are increasingly used to capture physical documents, which are then shared as electronic files. However, shadows in such images hinder reading. Currently available shadow removal datasets exhibit certain limitations. This paper creates a semi-synthetic dataset (SSD-DIS) with 12,224 image sets. Using Blender for shadow masks, multi-source shadow-free images, and adjusted shadow intensity/color, it simulates real-world shadow scenarios. Experiments show SSD-DIS enhances neural networks' learning of document shadow features; models trained on it outperform those using traditional datasets, supporting document shadow removal algorithm research.
This study investigates the effectiveness of various text representation methods in distinguishing between AI-generated and human-written content, using a corpus of 1000 Italian essays. Four techniques were employed for text representation: Text Features, Most Frequent Words (MFWs), Correspondence Analysis (CA), and a fine-tuned Large Language Model (LLM). Machine learning models, including Random Forests, Elastic-net, and Support Vector Machine, were applied to these representations. The study achieved high classification accuracy, with Text Features performing exceptionally well. However, adversarial tactics revealed vulnerabilities in models based on Text Features and MFWs, while approaches based on CA and LLM demonstrated greater resilience. CA in particular proved to be the best compromise between parsimony in the number of predictors, accuracy and robustness to targeted text-modification attacks. The research also focused on the explainability of the results, highlighting significant differences between AI- and human-written texts, including sentence structure and lexical choices. Ethical considerations, particularly in educational settings, were emphasized, along with the need for further cross-linguistic studies and diverse domain datasets. This research provides valuable insights into the challenges and potential solutions in AI text detection, emphasising the importance of balancing accuracy with interpretability and robustness against adversarial attacks.
This study investigated the effectiveness of remote microphone technology (RMT) for children with inattention and associated listening difficulties. A two-phase trial was conducted. Phase one was a 4-week randomized controlled crossover trial (n = 35), assessing listening and attention in noise with and without RMT. Phase two was a 30-week extension trial following participants continuing with RMT (n = 20) and controls (n = 17). Auditory processing, attention, memory and reading fluency were assessed before and after the extension. Questionnaires were administered throughout to monitor progress. In phase one, RMT use significantly improved speech intelligibility, listening comprehension, and auditory attention (P < 0.05). Participants reported improved classroom listening and ADHD symptoms (P < 0.05). In phase two, the experimental group showed greater improvement in reading fluency and self-reported quality of life (P < 0.01) compared to controls, with no significant differences in auditory processing or cognitive skills. These findings support RMT as an effective intervention for inattentive children with listening difficulties in educational settings. Clinical Trial Registration: Australia New Zealand Clinical Trials Registry. Registration date: 18/05/2023. Clinical Trial Numbers: ACTRN12623000512628. ACTRN12623000511639. Web links to study: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=385642&isReview=true. https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=385560&isReview=true.
Health-related quality of life (HRQoL) is a vital indicator of evaluating care outcomes and prognosis, yet little is understood about its developmental trajectories in older patients with chronic pain. This study aimed to identify latent HRQoL trajectories and their predictors, and to develop explainable machine learning models for predicting HRQoL deterioration. This prospective cohort study assessed 608 older patients with chronic pain at admission and at 1, 3, and 6 months post-admission, collecting data on HRQoL, general characteristics, pain level, activities of daily living (ADL), depression, and perceived social support. Growth mixture modeling was applied to identify trajectories of physical and mental HRQoL. Predictors were selected using LASSO regression and SVM-RFE. Nine explainable machine learning models were developed for both components, and SHAP interpreted the outputs. An HRQoL decision-support dashboard was developed to facilitate potential clinical application. Three physical HRQoL trajectories were identified: Stable High, Decline and Low Stability, alongside two mental HRQoL trajectories: Improvement and Decline. Key predictors included education level, pain duration, pain level, ADL, depression, and perceived social support, with ADL and pain level being the most influential for physical and mental HRQoL, respectively. This dual-trajectory study identified five distinct HRQoL patterns in older patients with chronic pain, elucidating key predictors via explainable machine learning. The proposed HRQoL decision-support dashboard may provide an interpretable tool to support understanding of predictive relationships and assist healthcare professionals in HRQoL assessment. Not applicable.
This study aimed to examine 8th-grade students' views on the concepts of nanotechnology and nanoscience through the use of the Metaverse in science courses. The study group sample consists of five students from both the before- and after-experience groups, all of whom are in 8th grade. This study employed a qualitative research method with a case study design. Observation, interview, and document analysis were used as data collection tools. Necessary measures have been taken to ensure the validity and reliability of the research within its scope. The data were analyzed using a content analysis approach. As a result of the interviews, data were collected and analyzed. As a result of the textual examinations, code, category, and theme were determined. The findings were presented in categories through tables, and the participants' answers were included in direct quotations. Upon reviewing the literature, it becomes apparent that most studies in nanotechnology and nanoscience are conducted for informational purposes, typically presented as presentations or reports. Given the limited availability of nanotechnology and metaverse education, the study was divided into two groups: a before-experience group and an after-experience group. As a result of the survey, 8th-grade students experience the metaverse and have future expectations for nanotechnology and nanoscience. Their cognitive and affective interests have increased, as evidenced by their questioning why these applications cannot be applied to all courses and by their correct expression of the concepts. At the same time, it has been concluded that using rich materials to concretize abstract concepts, such as nanotechnology, facilitates their teaching. The study provides qualitative evidence that Metaverse-based instruction can enhance both cognitive and affective dimensions of science learning, offering design implications for integrating immersive technologies into middle school curricula to teach abstract concepts.
Early and accurate detection of plant leaf diseases is an essential requirement for precision agriculture, given their severe impact on global food security. While much has been done recently, many deep learning-based approaches will still fail in real-world tests because of challenges such as background clutter, differences in illumination, occlusion, or the fact that visual symptoms for these diseases can be very subtle early on. Traditional CNN- and Transformer-based architectures generally lack accurate lesion localisation and interpretability, hindering their practical deployment in agricultural decision-support tools. To address these issues, we present LDDHybridNet, a region-based, explanation-friendly deep learning framework that can identify leaf disease at an early, accurate stage. It then applies preprocessing steps guided by ROI, based on leaf segmentation from the U-Net, followed by a compact CNN-based spatial feature-extraction framework. We arrange spatial feature embeddings extracted from lesion regions into an ordered sequence and employ a Bi-LSTM with attention to model structured contextual dependencies, allowing progression-aware feature learning without requiring actual temporal image sequences. Lastly, Grad-CAM-based post-hoc explainability is employed to interpret model decisions, enabling transparent visualisation of disease-relevant regions. We conduct extensive experiments on the PlantVillage benchmark and the FieldPlant dataset and show that LDDHybridNet consistently outperforms representative CNN, transformer, and hybrid baselines across multiple evaluation metrics. Although the near-ceiling performance on PlantVillage reveals the dataset's artificial nature, the proposed framework achieves 95.37% accuracy under real-world field conditions and 92.84% on weak-lesion early-stage samples, demonstrating the method's robustness and early-stage detection potential. The performance boosts are statistically significant (P < 0.01). In general, LDDHybridNet is an interpretable and robust deep learning framework for leaf disease detection, which can support data-driven crop protection and precision agriculture applications.
TkMYC2 mediates jasmonate-induced drought resistance and rubber biosynthesis simultaneously in Taraxacum kok-saghyz. Taraxacum kok-saghyz (T. kok-saghyz) is an important natural rubber-producing plant, yet its cultivation is often limited by drought stress, and the regulatory mechanisms underlying rubber biosynthesis and laticifer development remain incompletely understood. This study focused on TkMYC2, a core transcription factor in the jasmonate (JA) signaling pathway. Through homologous and heterologous genetic transformation, we systematically elucidated its dual functions in conferring drought tolerance and driving rubber biosynthesis. TkMYC2 expression was induced by both drought and methyl jasmonate (MeJA). Overexpression of TkMYC2 significantly enhanced the tolerance of transgenic plants to osmotic and drought stress by activating the antioxidant system (SOD, POD, CAT), maintaining ROS homeostasis, and reducing membrane lipid peroxidation. Using yeast two-hybrid and bimolecular fluorescence complementation assays, we demonstrated a direct physical interaction between TkMYC2 and TkJAZ11, a key repressor in the JA pathway. Phenotypic analyses showed that TkMYC2 overexpression promoted root thickening, laticifer development, and natural rubber accumulation, functionally supporting the hypothesis that rubber biosynthesis drives laticifer development. In summary, TkMYC2 acts as a critical molecular hub concurrently regulating drought stress response and rubber biosynthesis, providing new insights into jasmonate-mediated coordination of stress resilience and secondary metabolism, and offering a genetic resource for molecular breeding of T. kok-saghyz with enhanced yield and stress tolerance.
The increasing frequency of freshwater cyanobacterial blooms has emerged as a critical ecological and environmental concern, yet long-term time series data documenting such blooms remain scarce. This study presents a 13-year dataset (2010-2022) from two adjacent subtropical reservoirs (Shidou and Bantou) in Xiamen, Fujian Province, Southeast China. It provides a monthly and quarterly overview of 20 physicochemical parameters (348 samples), microscope-based phytoplankton (348 samples), and DNA sequence-based data for bacteria (342 samples) and microeukaryotes (348 samples). The dataset highlights recurrent cyanobacterial blooms dominated by Raphidiopsis raciborskii (basionym Cylindrospermopsis raciborskii). This long-term dataset serves as a valuable resource for investigating, predicting, and controlling cyanobacterial blooms, and will support efforts in biodiversity forecasting, ecological restoration, and targeted management of freshwater ecosystems.
The Clinical Genome Resource (ClinGen) is creating a central resource of clinically relevant genetic knowledge to improve genomic medicine. Dissemination and use of the ClinGen Resource is essential to ensure broad community uptake. We report on experiences and sustained use of ClinGen tools through engaging international genetics groups based in India, Africa and Singapore in variant classification training workshops using the ClinGen Variant Curation Interface (VCI). We developed pre and post workshop questionnaires and analyzed ClinGen tool use following the workshops. We evaluated organizational aspects and costs of creating a dedicated ClinGen VCI instance for each workshop. The workshops yielded >200 participants, with local scientists as essential participants. While ∼55% of participants were unfamiliar with variant classification, we found ∼79% were likely to use the VCI after the workshop. Further, we identified about ∼10% of workshop participants created permanent accounts. We estimate costs at ∼$3 per VCI instance. Our efforts highlight the yield of international workshops to sustained use of ClinGen's curation tools and identify areas for future consideration such as creating user-groups by experience level, and the importance of local scientist engagement in workshop deployment and organizational aspects.