With the rise of data-intensive research,data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence(AI)readiness.In the biomedical domain,data are charac...With the rise of data-intensive research,data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence(AI)readiness.In the biomedical domain,data are characterized by high complexity and privacy sensitivity,calling for robust and systematic data management skills.This paper reviews current trends in scientific data governance and the evolving policy landscape,highlighting persistent challenges such as inconsistent standards,semantic misalignment,and limited awareness of compliance.These issues are largely rooted in the lack of structured training and practical support for researchers.In response,this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive,lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness.Furthermore,it outlines a tiered training strategy tailored to different research stages—undergraduate,graduate,and professional,offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.展开更多
This study aims to investigate the current status of AI competencies among Chinese high school students using quantitative research methods.By integrating UNESCO’s AI Competency Framework for Students with China’s e...This study aims to investigate the current status of AI competencies among Chinese high school students using quantitative research methods.By integrating UNESCO’s AI Competency Framework for Students with China’s educational practices,a questionnaire was developed.Altogether,300 students from eight high schools joined the research.SPSS 26.0 was used for reliability and validity tests,exploratory factor analysis,and group difference analysis.The results show that the questionnaire has good reliability and validity(Cronbach’sα=0.974,KMO=0.912,Bartlett’s test of sphericity was significant with p<0.001,cumulative variance explained 72.0%).Besides,Chinese high school students’AI competencies exhibit a typical pattern of“strong in technology,weak in ethics,and insufficient in creativity”,with technical application ability(mean=3.7)significantly higher than ethical assessment(mean=3.0)and system design(mean=2.8).What’s more,students in the eastern region have significantly better overall competencies than those in the central and western regions(p<0.05),with the largest gap in the technical application dimension(mean difference=0.7)and the smallest gap in the ethical dimension(mean difference=0.3).The study recommends optimizing high school curricula by adding modules for ethical debates and interdisciplinary projects and promoting equitable distribution of educational resources.This research empirically supports global AI education standardization and offers a scientific basis for AI education policies and curriculum design in China’s high schools.展开更多
The design of diabetes inpatient educational preparation should be based on the needs of the nurses involved in terms of skills in this area. The objective of this qualitative study is to identify the preparatory need...The design of diabetes inpatient educational preparation should be based on the needs of the nurses involved in terms of skills in this area. The objective of this qualitative study is to identify the preparatory needs of nurses working in the medical and surgical units of a Lebanese hospital in terms of Survival Skills Education for Hospitalized Diabetic Patients (SSEHDP). Method: The focus group method is used for data collection using a semi-structured interview guide. The needs expressed by the thirty-two participating nurses were classified into categories of the competency framework for providing self-management education to diabetic patients proposed by the American Diabetes Association. Results: By focusing on the themes of an SSEHDP, a list of preparatory needs was drawn up. The needs identified and analyzed are then translated into general and specific learning objectives for educational preparation. Conclusion: The needs analysis is only the first step in a work that will ideally continue into the implementation and eventual evaluation of an educational program developed to help nurses acquire skills in the education of diabetic patients.展开更多
Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for me...Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.展开更多
文摘With the rise of data-intensive research,data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence(AI)readiness.In the biomedical domain,data are characterized by high complexity and privacy sensitivity,calling for robust and systematic data management skills.This paper reviews current trends in scientific data governance and the evolving policy landscape,highlighting persistent challenges such as inconsistent standards,semantic misalignment,and limited awareness of compliance.These issues are largely rooted in the lack of structured training and practical support for researchers.In response,this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive,lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness.Furthermore,it outlines a tiered training strategy tailored to different research stages—undergraduate,graduate,and professional,offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
基金supported by Research Project on Humanities and Social Sciences in Higher Education Institutions of Guizhou Provincial Department of Education(No.24RWZX060).
文摘This study aims to investigate the current status of AI competencies among Chinese high school students using quantitative research methods.By integrating UNESCO’s AI Competency Framework for Students with China’s educational practices,a questionnaire was developed.Altogether,300 students from eight high schools joined the research.SPSS 26.0 was used for reliability and validity tests,exploratory factor analysis,and group difference analysis.The results show that the questionnaire has good reliability and validity(Cronbach’sα=0.974,KMO=0.912,Bartlett’s test of sphericity was significant with p<0.001,cumulative variance explained 72.0%).Besides,Chinese high school students’AI competencies exhibit a typical pattern of“strong in technology,weak in ethics,and insufficient in creativity”,with technical application ability(mean=3.7)significantly higher than ethical assessment(mean=3.0)and system design(mean=2.8).What’s more,students in the eastern region have significantly better overall competencies than those in the central and western regions(p<0.05),with the largest gap in the technical application dimension(mean difference=0.7)and the smallest gap in the ethical dimension(mean difference=0.3).The study recommends optimizing high school curricula by adding modules for ethical debates and interdisciplinary projects and promoting equitable distribution of educational resources.This research empirically supports global AI education standardization and offers a scientific basis for AI education policies and curriculum design in China’s high schools.
文摘The design of diabetes inpatient educational preparation should be based on the needs of the nurses involved in terms of skills in this area. The objective of this qualitative study is to identify the preparatory needs of nurses working in the medical and surgical units of a Lebanese hospital in terms of Survival Skills Education for Hospitalized Diabetic Patients (SSEHDP). Method: The focus group method is used for data collection using a semi-structured interview guide. The needs expressed by the thirty-two participating nurses were classified into categories of the competency framework for providing self-management education to diabetic patients proposed by the American Diabetes Association. Results: By focusing on the themes of an SSEHDP, a list of preparatory needs was drawn up. The needs identified and analyzed are then translated into general and specific learning objectives for educational preparation. Conclusion: The needs analysis is only the first step in a work that will ideally continue into the implementation and eventual evaluation of an educational program developed to help nurses acquire skills in the education of diabetic patients.
基金Science and Technology Innovation 2030 Major Project,Grant/Award Number:2023ZD0508506。
文摘Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.
