Empowering Educators: Mastering AI Prompt Writing

BackgroundThere has been a resurgence of Artificial Intelligence (AI) on a global scale in recent times, resulting in the development of cutting-edge AI solutions within hospitals. However, this has also led to the creation of isolated AI solutions that are not integrated into clinical IT. To tackle this issue, a clinical Artificial Intelligence (AI) platform that handles the entire development cycle of clinical AI models and is integrated into clinical IT is required. This research investigates the integration of a clinical AI platform into the clinical IT infrastructure. This is demonstrated by outlining the stages of the AI model development cycle within the clinical IT infrastructure, illustrating the interaction between different IT system landscapes within the hospital with BPMN diagrams.MethodsInitially, a thorough analysis of the requirements is conducted to refine the necessary aspects of the clinical AI platform with consideration of the individual aspects of clinical IT. Subsequently, processes representing the entire development cycle of an AI model are identified. To facilitate the architecture of the AI platform, BPMN diagrams of all the identified processes are created. Clinical use cases are used to evaluate the processes using the FEDS framework.ResultsOur BPMN process diagrams cover the entire development cycle of a clinical AI model within the clinical IT. The processes involved are Data Selection, Data Annotation, On-site Training and Testing, and Inference, with distinctions between (Semi-Automated) Batch Inference and Real-Time Inference. Three clinical use cases were assessed to evaluate the processes and demonstrate that this approach covers a wide range of clinical AI use cases.ConclusionsThe evaluations were executed successfully, which indicate the comprehensive nature of our approach. The results have shown that different clinical AI use cases are covered by the BPMN diagrams. Our clinical AI platform is ideally suited for the local development of AI models within clinical IT. This approach provides a basis for further developments, e.g., enabling the training and deployment of an AI model across multiple sites or the integration of security- and privacy-related aspects.

OBJECTIVE: This study aims to assess the accuracy and clinical utility of artificial intelligence (AI) platforms in responding to questions related to penile fracture, a rare but urgent urological emergency. MATERIAL and METHODS: Twenty-five questions addressing key clinical aspects of penile fracture were submitted to four AI platforms: ChatGPT, Copilot, Gemini, and Perplexity. Two expert urologists evaluated each response across five domains –relevance, clarity, structure, utility, and factual accuracy– using a 5-point Likert scale. Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC), and statistical comparisons were made using one-way ANOVA and Tukey’s post-hoc tests. RESULTS: Copilot and ChatGPT scored highest overall, with mean scores of 4.90 and 4.89 respectively, while Perplexity scored significantly lower (4.68; p <0.001). Copilot also achieved the highest ratings in clarity and factual accuracy. Inter-rater reliability was high, and dimensional analysis confirmed the consistent superiority of Copilot and ChatGPT in clinical relevance and clarity. CONCLUSION: While AI platforms –especially Copilot and ChatGPT– show promise in generating medically relevant content about penile fracture, limitations in factual accuracy and clinical specificity remain. Caution is advised in using these tools in urgent care settings without professional oversight. Keywords: artificial intelligence, clinical accuracy, emergency medicine, large language models, penile fracture, urology

Artificial intelligence in pediatric otorhinolaryngology: Assessing readability, understandability, and actionability of postoperative instructions.

Background Birth control methods (BCMs) are often underutilized or misunderstood, especially among young individuals entering their reproductive years. With the growing reliance on artificial intelligence (AI) platforms for health-related information, this study evaluates the performance of ChatGPT-4o and Google Gemini in addressing commonly asked questions about BCMs. Methods Thirty questions, derived from the American College of Obstetrics and Gynecologists (ACOG) website, were posed to both AI platforms. Questions spanned four categories: general contraception, specific contraceptive types, emergency contraception, and other topics. Responses were evaluated using a five-point rubric assessing Relevance, Completeness, and Lack of False Information (RCL). Overall scores were calculated by averaging the rubric scores. Statistical analysis, including the Wilcoxon Signed-Rank test, Friedman test, and Kruskal-Wallis test, was performed to compare metrics. Results ChatGPT-4o and Google Gemini provided high-quality responses to birth control-related queries, with overall scores averaging 4.38 ± 0.58 and 4.37 ± 0.52, respectively, both categorized as "very good" to "excellent." ChatGPT-4o demonstrated higher scores in the lack of false information, based on descriptive statistics (4.70 ± 0.60 vs. 4.47 ± 0.73), while Google Gemini outperformed in relevance, with a statistically significant difference (4.53 ± 0.57 vs. 4.30 ± 0.70, p = 0.035, large effect size). Completeness scores were comparable (p = 0.655). Statistical analyses revealed no significant differences in overall performance (p = 0.548), though Google Gemini demonstrated a potential trend of stronger performance in the "Other Topics" category. Within-model variability showed ChatGPT-4o had more pronounced differences among metrics (moderate effect size, Kendall's W = 0.357), while Google Gemini exhibited smaller variability (Kendall's W = 0.165). These findings suggest that both platforms offer reliable and complementary tools for addressing knowledge gaps in contraception, with nuanced strengths that warrant further exploration. Conclusions ChatGPT-4o and Google Gemini provided reliable and accurate responses to BCM-related queries, with slight differences in strengths. These findings underscore the potential of AI tools, in addressing public health information needs, particularly for young individuals seeking guidance on contraception. Further studies with larger datasets may elucidate nuanced differences between AI platforms.

The performance of two artificial intelligence (AI) platforms, ChatGPT 3.5 (OpenAI, California, United States) and Gemini (Google AI, California, United States) was assessed by answering 200 questions of microbiology drawn from validated sources. The questions were selected from topics such as General Microbiology, Immunology, and Microbiology Applied to Infectious Diseases. The study was conducted from December 2023 to March 2024, and the responses of the different AI platforms were compared with an answer key. Statistical analysis was performed to assess accuracy. ChatGPT 3.5 and Gemini had comparable accuracy with correct response scores of 71% and 70.5%, respectively. Their performance varied across different sections. Gemini performed better in General Microbiology and Immunology, and ChatGPT 3.5 had a better score in the Applied Microbiology section. The study's findings highlight that AI platforms such as ChatGPT and Gemini can be utilized in microbiology and medical education.The evolution and continuous updating of AI platforms are required to improve their performance.

The primary objective of the study was to evaluate and compare the effectiveness of four artificial intelligence (AI) platforms - ChatGPT, DeepSeek AI, Copilot, and Gemini - in providing solutions to common human resource (HR) challenges in corporate hospitals across India. A cross-sectional, questionnaire-based online study was conducted among 50 h professionals with over 10 years of experience across corporate hospitals in India. Ten real-world HR problem scenarios were designed and fed into the four AI platforms using a standardized prompt. The responses were anonymized, coded, and rated by participants on five criteria: accuracy, relevance, practicability, overall response, and likelihood of use. Data were analyzed using Epi Info (version 7.0) with Chi-square tests; P < 0.05 was considered significant. Among participants, 40% were male and 60% female; 50% had over 25 years of HR experience. ChatGPT and DeepSeek AI received the highest ratings for accuracy (85% and 84% "strongly agree"), relevance, and practicability, with 80%-81% indicating a high likelihood of future use. Copilot and Gemini showed moderate acceptance (55%-60% likelihood of use). There was no significant association between responses and participant gender or years of experience. AI platforms, particularly ChatGPT and DeepSeek AI, demonstrate strong potential to support HR professionals in solving routine challenges, improving efficiency, and decision-making. However, effective adoption requires training, data accuracy, and ethical safeguards to address issues such as bias and privacy.

Simulation-based medical education (SBME) is a critical training tool in healthcare, shaping learners' skills, professional identities, and inclusivity. Leadership demographics in SBME, including age, gender, race/ethnicity, and medical specialties, influence program design and learner outcomes. Artificial intelligence (AI) platforms increasingly generate demographic data, but their biases may perpetuate inequities in representation. This study evaluated the demographic profiles of simulation instructors and heads of simulation labs generated by three AI platforms-ChatGPT, Gemini, and Claude-across nine global locations. A global cross-sectional study was conducted over 5days (November 2024). Standardized English prompts were used to generate demographic profiles of simulation instructors and heads of simulation labs from ChatGPT, Gemini, and Claude. Outputs included age, gender, race/ethnicity, and medical specialty data for 2014 instructors and 1880 lab heads. Statistical analyses included ANOVA for continuous variables and chi-square tests for categorical data, with Bonferroni corrections for multiple comparisons: P significant < 0.05. Significant demographic differences were observed among AI platforms. Claude profiles depicted older heads of simulation labs (mean: 57years) compared to instructors (mean: 41years), while ChatGPT and Gemini showed smaller age gaps. Gender representation varied, with ChatGPT and Gemini generating balanced profiles, while Claude showed a male predominance (63.5%) among lab heads. ChatGPT and Gemini outputs reflected greater racial diversity, with up to 24.4% Black and 20.6% Hispanic/Latin representation, while Claude predominantly featured White profiles (47.8%). Specialty preferences also differed, with Claude favoring anesthesiology and surgery, whereas ChatGPT and Gemini offered broader interdisciplinary representation. AI-generated demographic profiles of SBME leadership reveal biases that may reinforce inequities in healthcare education. ChatGPT and Gemini demonstrated broader diversity in age, gender, and race, while Claude skewed towards older, White, and male profiles, particularly for leadership roles. Addressing these biases through ethical AI development, enhanced AI literacy, and promoting diverse leadership in SBME are essential to fostering equitable and inclusive training environments. Not applicable. This study exclusively used AI-generated synthetic data.

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

This research investigates the influence of technological innovation—namely Artificial Intelligence (AI), data analysis, and virtual guidance platforms—on Chinese higher education student satisfaction with educational counselling services. Utilizing a quantitative design, the data were gathered via a structured survey of 232 students who had received counselling services. Statistical analyses involving regression and mediation modeling through Statistical Package for the Social Sciences (SPSS) were carried out in order to evaluate direct and indirect relationships between the variables. The results show that AI integration, data analytics utilization, and virtual guidance platform availability individually have a strong positive impact on student satisfaction. In addition, the research establishes personalized student support as a mediating factor in these relationships, highlighting the need to customize services based on individual student needs to best capitalize on technological innovations. These findings imply that technology optimizes access and efficiency but that its real impact on satisfaction is only achieved when blended with actual personalization. The study provides useful guidance for higher education institutions seeking to reform their counselling systems and enhance student outcomes. By underscoring the complementarity of technology and personalization, this research offers a theoretical and empirical framework for the design and implementation of student-centered, technology-enhanced support services in higher education.

Researchers at INFN (National Institute for Nuclear Physics) face challenges from basic to hard science use cases (e.g., big-data latest generation experiments) in many areas: High Energy Physics (HEP), Astrophysics, Quantum Computing, Genomics, etc. Machine Learning (ML) adoption is ubiquitous in these areas, requiring researchers to solve problems related to the specificity of applications (e.g., tailored models and intricate domain knowledge), but also requiring solving general infrastructure-level and ML-workflow related problems. As the demand for ML solutions continues to rise across the diverse research domains, there exists a critical need for an innovative approach to accelerate ML adoption. In this regard we introduce an Artificial Intelligence (AI) platform designed as an application-agnostic Machine Learning as a Service (MLaaS) solution. The platform provides a paradigm shift by offering a flexible and generalized infrastructure that decouples the ML development process from the specific use cases. The AI platform is implemented as a software layer on top of our cloud platform: the INFN Cloud, a dedicated, geographically distributed infrastructure which offers composable, scalable, and open-source solutions. The AI platform leverages INFN Cloud resources and principles, gathering and orchestrating technologies to support end-to-end scalable ML solutions: Kubernetes, Kubeflow, KServe, KNative, Kueue, Horovod, etc., ensuring support for several ML frameworks: TensorFlow, PyTorch, Apache MXNet, XGBoost, etc. This paper describes the platform’s design and principles, as well as some selected use cases from Natural Language Processing and HEP domains that can take advantage of the “aaS” approach.

Aim: In the digital age, artificial intelligence (AI) platforms have gradually replaced traditional manual techniques for information retrieval. However, their effectiveness in conducting academic literature searches remains unclear, necessitating a comparative assessment. This study examined the efficacy of AI search engines (Elicit, Consensus, ChatGPT) vs. manual search for literature retrieval, focusing on the surgical management of trapeziometacarpal osteoarthritis. Methods: The study was executed per the Cochrane Handbook for Systematic Reviews and PRISMA guidelines. AI platforms were given relevant keywords and prompts, while manual searches used PubMed, Cochrane CENTRAL, Web of Science, and Scopus databases from January 1901 to April 2024. The study focused on English-language randomized controlled trials (RCTs) comparing surgical management of trapeziometacarpal osteoarthritis (TMCJ OA). Two independent evaluators screened and extracted data from the studies. Primary outcomes involved the quality and relevancy of studies chosen by both search methods, evaluated by false positive rates and number of studies, including outcomes of interest. Results: The manual search yielded the most results (6,018), followed by Elicit (4,980), Consensus (3,436), and ChatGPT (6). Elicit identified the highest number of RCTs (205) but also had the greatest false positive rate (94%). Ultimately, the manual search identified 23 suitable studies, Elicit found 10, Consensus found 9, and ChatGPT identified only 1. No additional studies were found by AI search engines that were not discovered in the manual search. Conclusion: The findings highlight the potential advantages and drawbacks of AI search engines for literature searches. While Elicit was prone to error, Consensus and ChatGPT were less comprehensive. Significant enhancements in the precision and thoroughness of AI search engines are required before they can be effectively utilized in academia.

Background Balochistan is the largest province of Pakistan by area, and the least developed. It suffers from several political, tribal and border conflicts. The distances to health facilities for the catchment population are long with limited accessibility. Immunization is one of the most cost-effective interventions to prevent deaths from vaccine preventable diseases (VPDs), especially in children. While Pakistan has an overall routine immunization coverage of 66 per cent for fully immunized children (FIC), coverage in the province of Balochistan is much lower at 29 per cent. This study aimed to assess the feasibility of introducing mHealth intervention using an artificial intelligence (AI) platform based on SMS (short-message service) and Interactive Voice Response (IVRs) to remind and persuade parents to get their children vaccinated. Methods We employed a mixed study design using both quantitative and qualitative approaches. Baseline data were collected from 1,600 eligible mothers/parents within the catchment areas of 75 basic health units (BHUs) in Quetta (provincial capital of Balochistan province), and the automated platform was instituted with SMS and IVRs from EPI (Expanded Programme on Immunization) Quetta. Daily reminders and IVRs were sent to the cell numbers in the database/records. Responses were noted on the AI platform. After a period of about two months, an end line survey of 1,203 participants was undertaken with a loss of 397 (25%) participants due to seasonal migration to warmer cities in Sindh province. For the qualitative part, three key informant interviews (KII) were conducted; two KIIs with Lady Health Supervisors and one KII with the WHO staff responsible for routine immunization. There were also three focus group discussions (FGDs). Results We found that the intervention was feasible as well as acceptable both at the community and programme management levels. The baseline indicators of immunization coverage improved significantly on end line survey (95% confidence interval, CI = 0.208-0.269; P&lt;0.001). Conclusions Our study demonstrates the potential for mHealth and AI to improve childhood immunization and addresses equity in the least developed areas of this country. The replication of the strategy in subnational immunization programmes could decrease morbidity and mortality due to VPDs.

Background: Artificial intelligence (AI) has emerged as a transformative tool in education, offering personalized feedback, adaptive learning, and data-driven instructional strategies. While individual studies have explored its role in mathematics, findings remain fragmented. This study employs a meta-analysis to systematically evaluate the overall effect of AI applications on students’ mathematics performance. Methods: A comprehensive search was conducted across five databases (Google Scholar, Scopus, ERIC, Web of Science, and SCISPACE) for peer-reviewed quantitative studies published between 2017 and 2025. A total of 1,042 articles were identified, and after applying strict inclusion criteria guided by the PRISMA framework, 7 studies were retained. Data extraction followed a standardized protocol, and methodological quality was appraised using the Joanna Briggs Institute Critical Appraisal Checklist. Statistical analyses were performed with JASP, including effect size estimation, heterogeneity testing, and publication bias diagnostics. Results: The pooled effect size was g = 0.603 (p &lt; 0.001, 95% CI = 0.393–0.813), indicating a moderate-to-large positive impact of AI on mathematics performance. Heterogeneity was substantial (Q(6) = 52.41, I² = 90.08%), suggesting variability across contexts and intervention types. However, fail-safe N analysis (N = 520.124) confirmed robustness against publication bias. Conclusion: Findings demonstrate that AI significantly enhances mathematics learning, supporting constructivist and socio-cultural theories of education where AI scaffolds learner self-efficacy and engagement. Policymakers and educators are encouraged to strategically integrate AI into curricula and teacher training to maximize its transformative potential in mathematics education.

The increasing integration of artificial intelligence (AI) tools in education has reshaped instructional planning practices, particularly in lesson design and resource development. This descriptive phenomenological study explored the experiences of basic education teachers at Divine Word College of Laoag in integrating ChatGPT into lesson planning. Data were collected through open-ended questionnaires followed by a focused group discussion conducted via video conferencing with ten purposively selected teachers. Using thematic analysis following Braun and Clarke’s framework, findings reveal that ChatGPT supports lesson planning by enhancing logical content organization, generating aligned instructional examples and activities, improving planning efficiency, and suggesting diverse teaching strategies. However, participants also reported challenges, including the generic nature of AI-generated content, limited contextual relevance to local curricula and learner needs, insufficient AI-related training, and concerns regarding over-dependence and loss of pedagogical autonomy. This study highlights teachers’ experiences using generative AI for lesson planning and emphasizes the need for teacher mediation, clear institutional guidelines, targeted training, and ethical use to ensure AI supports—not replaces—professional judgment. Keywords: ChatGPT, lesson planning, artificial intelligence in education, basic education, teacher experiences, descriptive phenomenology, generative AI

Artificial Intelligence (AI) is a system that is developed and continues to innovate deeply fields of study that are created either on machines or computers that have the same intelligence or even more like humans. Artificial Intelligence (AI) technology is often used, not only one or two Artificial Intelligence (AI) platforms have been created by humans for makes human work easier. However, Artificial Intelligence (AI) has various types where each platform has different uses and functions, the author this time wants describes the platforms that students often use for survival universities according to a survey that the author has carried out. Research methods used to find out this phenomenon is a qualitative descriptive research method with techniques collect questionnaire data by asking questions and can be answered directly by respondents according to their respective answers. Artificial Intelligence (AI) has potential play an important role in supporting students with learning disabilities and helping them reach their maximum potential. For the emotional plane students, the use of Artificial Intelligence (AI) technology can help build feelings confident in learning outcomes, because human artificial intelligence technology can help learning in a pleasant and comfortable environment, thereby increasing the sense of self-confidence and reduce anxiety in learning. UINSA student use this intelligence tool to make work easier and save money their time. This human artificial intelligence technology also does not completely have it benefits for students, if we as students are aware of technological advances with the suitability of his actions regarding Max Weber's rational actions, for achieveing the actor’s dreams. AbstrakArtificial Intelligence (AI) merupakan sistem yang dikembangkan dan terus berinovasi dalam bidang studi yang yang dibuat baik pada mesin ataupun komputer yang memiliki kecerdasan sama atau bahkan lebih seperti manusia. Teknologi Artificial Intelligence (AI) sering digunakan, tidak hanya satu atau dua saja platform Artificial Intelligence (AI) yang diciptakan manusia untuk memudahkan pekerjaan manusia. Namun, Artificial Intelligence (AI) memiliki berbagai jenis yang setiap platform memiliki tata guna dan fungsi yang berbeda, penulis kali ini ingin menjabarkan platform-platform yang sering digunakan mahasiswa dalam keberlangsungan hidup universitas menurut survey yang sudah penulis laksanakan. Metode penelitian yang digunakan untuk mengetahui fenomena ini adalah metode penelitian deskriptif kualitatif dengan teknik pengumpulan data kuesioner dengan memberikan pertanyaan dan dapat dijawab langsung oleh responden sesuai dengan jawaban mereka masing-masing. Artificial Intelligence (AI) berpotensi memainkan peran penting dalam mendukung mahasiswa dengan ketidakmampuan belajar dan membantu mereka mencapai potensi maksimal mereka. Untuk bidang emosional mahasiswa, penggunaan teknologi Artificial Intelligence (AI) dapat membantu membangun rasa percaya diri dalam hasil belajar, karena teknologi kecerdasan buatan manusia dapat membantu pembelajaran dalam lingkungan yang menyenangkan dan nyaman, sehingga meningkatkan rasa percaya diri dan mengurangi kecemasan dalam belajar. Mahasiswa UINSA mempergunakan alat bantu kecerdasan ini untuk mempermudah pengerjaan serta menghemat waktu mereka.Upaya yang mereka lakukan telah dijelaskan oleh Max Weber sebagai bentuk dari tindakan rasional yang berorientasi, kepada cita-cita para pelaku. Kata kunci: Artificial Intellegence, Pembelajaran, tindakan rasional