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Module 1: Foundations of Research and Thesis Development
Upon completion of this module, residents will be able to:
• Understand the thesis requirements and academic standards in basic and paraclinical sciences.
• Identify feasible research topics aligned with departmental resources and expertise.
• Formulate clear, testable research questions using established frameworks.
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Module 2: Advanced Literature Review and Research Proposal Writing
This module builds the conceptual, regulatory, and methodological foundation required to design a sound postgraduate thesis- from understanding institutional requirements to translating ideas into structured research questions, hypotheses, and objectives.
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Module 3: Study Design, Methodology, and Data Management
This module builds the conceptual, regulatory, and methodological foundation required to design a sound postgraduate thesis- from understanding institutional requirements to translating ideas into structured research questions, hypotheses, and objectives.
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Module 4: Comprehensive Biostatistics for Thesis Research
To equip learners with the statistical thinking, analytical skills, and practical tools required to analyze thesis data correctly, interpret results responsibly, and present findings in a defensible, publication-ready format.
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Research Methodology and Biostatistics for Postgraduate Residents in Basic Sciences and Paraclinical Sciences
Learning Objectives:
By the end of this lesson, students will be able to:
- Distinguish between assistive AI and generative AI in academic writing contexts
- Apply language improvement tools ethically without crossing institutional boundaries
- Articulate institutional policies on AI disclosure and transparency in research writing
- Evaluate the risks of over-reliance on AI-generated content in scientific publications
Content:
- Role of digital tools in modern scientific writing
- AI as an assistant, not an author
- Language improvement tools (e.g., QuillBot and similar platforms)
- Distinguishing Assistive AI and Generative AI
- Ethical boundaries of AI use in academia
- Disclosure, transparency, and institutional policies
- Risks of over-reliance on AI-generated text research.