In today's academic, professional and public health environment, research is increasingly shaped by rapid access to digital tools, growing expectations for evidence-based decision-making, and the emergence of artificial intelligence in research workflows.
Bridging Theory and Practice: Academic faculty in Nursing, medical research & social sciences and Postgraduate/doctoral students would benefit from structured methodologies that translate academic research directly into evidence-based practice and improved healthcare outcomes.
Critical Challenges of AI Integration: While AI offers new opportunities to accelerate literature review, support instrument development, assist with data analysis, and improve scholarly communication, it also introduces important concerns related to accuracy, bias, ethics, and transparency.
Key distinguishing features of this course
This AI integrated course on Research methods has been designed in response to this challenge. It is a short, practice-oriented course that introduces participants to the full research cycle. while integrating the responsible use of AI as a research tool. The course emphasizes a human-in-the-loop approach, where the researcher remains the critical thinker and accountable author, while AI is used as an assistant to improve efficiency, reflection, and workflow.
To equip participants with practical skills to design, analyze, and communicate research, while ensuring rigor, ethics, and transparency.
The course includes the following 5 modules:
The course emphasizes:
Registered Nurses across government/private/charitable institutions, to include