Generative Artificial Intelligence

Generative artificial intelligence (GenAI) tools are increasingly used by medical students for literature searching, scientific writing, and research. However, AI-generated citations may contain errors. Some references may not exist or may contain incorrect bibliographic details, while other references may be genuine but may not actually support the claim for which they are cited. This randomized educational study will evaluate whether a structured citation-verification framework called SARA can improve medical students' ability to identify and correct these problems. SARA teaches students to assess four elements of a citation: Source authenticity, Accuracy, Relevance, and Alignment between the cited evidence and the claim being made. Year 3-5 medical students will be randomly assigned to either a 75-minute SARA training session or an attention-matched session on responsible use of GenAI in academic work. Participants will complete citation-verification assessments before training, immediately after training, and 12 weeks later. The main outcome will assess whether students can independently apply the verification approach to an unstructured literature-review task 12 weeks after training, without being prompted to use SARA. The study will also examine which types of citation errors remain difficult, whether students initiate appropriate verification, the quality of their verification, handling of valid citations, and confidence in their judgments. A subgroup of participants will take part in think-aloud and follow-up interviews to explore how they approach citation verification and transfer the strategy to new tasks.

This study is a randomized, controlled trial with a pre-test/post-test design, conducted to determine the effects of Generative AI (GenAI)-supported learning on academic achievement, learning efficiency, and critical thinking within a university setting. The study population consists of students from the Dialysis Program at Bingöl University's Vocational School of Health Services; the sample comprises 100 volunteer students who met the inclusion criteria and were randomly assigned to either an experimental group (n=50) or a control group (n=50). The experimental group will participate in a "Generative AI-Supported Learning Program" over the course of one month-meeting two days a week for a total of eight 60-minute sessions-covering topics ranging from effective prompting techniques to case analysis and academic ethics; meanwhile, the control group will continue with their routine educational activities. Data will be collected using a Demographic Information Form, the Academic Achievement Inventory, the Critical Thinking Disposition Scale, and the Artificial Intelligence Dependency Scale, and will be analyzed using biostatistical methods (t-tests, ANCOVA, and effect size calculations).

The goal of this clinical trial is to learn whether an artificial intelligence (AI)-supported communication tool can help improve communication between people receiving palliative care and their healthcare providers. The study will also evaluate a new questionnaire designed to measure the quality of healthcare communication. The main questions this study aims to answer are: 1. Can the new questionnaire accurately measure the quality of communication between participants and healthcare providers? 2. Does using the AI-supported communication tool, called NurseChat+, help participants feel more confident and prepared before meeting with their healthcare team? 3. Does NurseChat+ improve the quality of communication during palliative care visits? Researchers will compare participants who use NurseChat+ together with their usual care to participants who receive usual care alone to see whether the tool improves communication outcomes. The study will be conducted in three stages. First, researchers will develop and test a questionnaire that measures healthcare communication. Next, they will develop and refine the NurseChat+ tool. Finally, they will test the tool in adults receiving palliative care at National Taiwan University Hospital. Participants in the final stage of the study will be randomly assigned to one of two groups. One group will use NurseChat+ before their scheduled medical visits in addition to receiving usual care, while the other group will receive usual care only. Participants will complete questionnaires about their communication experiences during the study.

This study aims to design, implement, and evaluate a blended online and offline teaching model for Internal Medicine Nursing, integrating generative artificial intelligence (GAI), a virtual simulation platform, card-based exercises, and scenario simulation. The objective is to address key limitations of traditional teaching, including low student engagement, insufficient cultivation of clinical thinking, limited personalized learning, and a disconnect between theory and practice. A mixed-methods approach will be used. All undergraduate nursing students from the 2024 cohort at Changsha Medical University will be enrolled via convenience sampling as the experimental group to receive the new blended model. The 2023 cohort will serve as the control group, receiving traditional teaching. Quantitative data (course grades, satisfaction questionnaires) and qualitative data (semi-structured interviews) will be collected to comprehensively evaluate the model's effectiveness. Expected outcomes include improved student mastery of theoretical knowledge, enhanced practical skills and clinical thinking, increased learning interest, and higher teaching satisfaction. The study intends to provide a replicable, scalable innovative solution for nursing education reform, ultimately contributing to the training of high-quality applied nursing talents. Key problems addressed: Overcoming single-method teaching and poor interaction through GAI and gamification. Enhancing clinical thinking and decision-making via dynamic GAI cases and card-based exercises. Providing personalized learning paths and instant feedback using GAI technology. Bridging the theory-practice gap with high-fidelity virtual and scenario simulations. Implementing a multi-dimensional evaluation system beyond final exams to assess comprehensive student abilities.