BMC Medical Education is inviting submissions for a Collection entitled Nursing education. As the complexity of patient care increases, so does the need for well-trained nursing professionals. The landscape of nursing education has been shaped by various factors, including advancements in technology, changes in healthcare delivery systems, and the increasing emphasis on interprofessional collaboration. As such, it is crucial to explore new pedagogical approaches, curriculum innovations, and assessment methods that equip future nurses with the skills and knowledge necessary to thrive in diverse healthcare environments.

The significance of nursing education extends beyond individual career development; it plays a vital role in improving patient outcomes and advancing public health. Recent advancements, such as the integration of simulation-based learning, the use of virtual reality, and the implementation of competency-based education, have transformed the way nursing students are trained. Moreover, there is a growing recognition of the importance of mental health and emotional resilience in nursing practice, which underscores the need for comprehensive education that addresses these critical aspects.

Looking ahead, the ongoing integration of artificial intelligence and data analytics into nursing curricula promises to enhance the decision-making skills of future nurses. Additionally, an increased focus on global health issues and cultural competency in nursing education will prepare graduates to navigate the complexities of healthcare in an interconnected world. These developments hold the potential to not only elevate the standards of nursing education but also to significantly improve healthcare delivery on a global scale.

We welcome original research and perspectives that contribute to a deeper understanding of key topics, including but not limited to:

Simulation-based learning in nursing

Interprofessional education strategies

Incorporating technology in nursing curricula

Mental health training for nursing students

This Collection supports and amplifies research related to SDG 3: Good Health and Well-being.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

BMC Medical Education welcomes submissions to our Artificial intelligence (AI) in curriculum development and assessment Collection. The incorporation of AI technologies and methodologies into medical and allied health curricula and student assessment is not just a trend, but a crucial step towards improving program effectiveness and producing efficient and professional doctors and allied health professionals. This Collection aims to explore how AI is reshaping medical and allied health curricula, student training, and assessment, as well as the potential implications of these changes on healthcare services and patient wellbeing.

Research on AI in medical education is still in its early stages. However, there has been a marked increase in interest and activity in this area during 2023 and 2024. Still, the majority of existing publications are commentary articles, perspective pieces, letters to the editor, or editorials, rather than original research. This highlights the pressing need for rigorous research and systematic reviews on the application of AI in medical education. Additionally, most contributions come from countries such as the United States, Canada, the United Kingdom, Australia, China, Singapore, Denmark, and Oman. In contrast, there is a significant underrepresentation of research output from regions including North Africa, the Middle East, South-East Asia, and South America.

This new Collection focuses specifically on curriculum development and student assessment, and researchers are encouraged to submit their work for consideration. Continued exploration in this domain has the potential to revolutionize medical education and, ultimately, enhance patient care worldwide. This reflects the transformative power of AI in shaping the future of medical training.

Submissions of innovative research contributing to this goal are invited. The scope of this Collection includes, but is not limited to, the following topics:

• Validity and reliability of AI in curriculum development

• AI applications in student selection and the allocation of medical and allied health graduates into specialties and subspecialties

• The use of AI in developing assessment questions and marking essay questions

• The potential of AI in marking OSCE examinations

• AI applications in providing performance feedback to students

• Critical assessment of AI-performed tasks in curriculum design and in student evaluation

• Validity and accuracy of AI-generated clinical cases

• Utilization of AI in faculty development and continuing medical education

• Students’ use of AI in learning environments

• Critical analysis of AI-generated responses and outputs

• Evaluation of published research on AI’s role in assessment and question design

Contributions are welcomed from a range of disciplines, including healthcare professionals, medical educators, social scientists, and computer scientists. The Collection also seeks submissions from fields such as dentistry, pharmacy, nursing, physiotherapy, occupational therapy, speech pathology, psychology, midwifery, oral therapy, paramedicine, and optometry. The goal is to build a vibrant, interdisciplinary Collection that captures the latest developments and research on AI's impact in curriculum and assessment within medical and allied health education.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

This Collection supports and amplifies research related to SDG 4: Quality Education.