Purpose: This study develops a chatbot for school violence prevention (C-SVP) among elementary school students.Methods: Among the analysis, design, development, implementation, and evaluation (ADDIE) models, ADD phases were applied to develop a C-SVP. Students’ learning needs were identified by constructing content with a design that attracted their attention. Subsequently, a formative evaluation was conducted on the developed C-SVP to test its applicability by ten elementary school students targeting the 5th and 6th grades.Results: The chatbot was designed using KakaoTalk and named “School Guardian Angel.” The formative evaluation revealed that the developed C-SVP was easily accessible and useful for elementary school students.Conclusion: The developed C-SVP is expected to be effective in preventing violence among elementary school students. However, further research involving children of various age groups is required.

Texts in medical fields containing sensitive information pose challenges for AI research usability. However, there is increasing interest in generating synthetic text to make medical text data bigger for text-based medical AI research. Therefore, this paper suggests a text augmentation system for cerebrovascular diseases, using a synthetic text generation model based on DistilGPT2 and a classification model based on BioBERT. The synthetic text generation model generates synthetic text using randomly extracted reports (5000, 10,000, 15,000, and 20,000) from 73,671 reports. The classification model is fine-tuned with the entire report to annotate synthetic text and build a new dataset. Subsequently, we fine-tuned a classification model by incrementally increasing the amount of augmented data added to each original dataset. Experimental results show that fine-tuning by adding augmented data improves model performance by up to 20%. Furthermore, we found that generating a large amount of synthetic text is not necessarily required to achieve better performance, and the appropriate amount of data augmentation depends on the size of the original data. Therefore, our proposed method reduces the time and resources needed for dataset construction, automating the annotation task and generating meaningful synthetic text for medical AI research.

Data scarcity is a significant barrier to developing high-performing AI models for medical text classification. To improve stroke prediction from electrocardiogram (ECG) interpretations reports where training data are scarce, we propose a novel data augmentation technique, Multi-Expert Perspective Augmentation. We use the LLM Phi-4 to rewrite original machine-generated ECG reports from the simulated perspectives of five different medical specialists. This prompt-based approach generates text with diverse clinical viewpoints while preserving the original meaning. We trained a BiomedBERT-based Gradient Boosting model to classify for stroke, comparing a baseline model trained only on the original data against a model trained with our augmented data. The model trained solely on original data showed poor performance (F1-score: 0.6698) with a severe precision–recall imbalance. In contrast, the augmented model achieved a significantly improved and balanced performance, with an F1-score of 0.8421, an accuracy of 0.8427, a precision of 0.8571, and a recall of 0.8276. Our method also outperformed other LLM-based augmentation techniques. Our findings demonstrate that rewriting text from multiple simulated expert perspectives is an effective strategy for data augmentation, enhancing the linguistic and contextual diversity of training data and leading to a more balanced and accurate classification model.