Course Outcomes
Expected learning outcomes
Course Outcomes
- Use AI to support monitoring, evaluation, research and learning processes.
- Improve research design, data collection tools, analysis and reporting quality.
- Apply AI to summarize qualitative and quantitative information for decision-making.
- Strengthen indicator tracking, impact reporting and learning documentation.
- Promote ethical and responsible AI use in research, evaluation and data management.
Curriculum
Course modules and outline
Course Outline
Module 1: AI in monitoring, evaluation, research and learning
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 2: AI for research design and literature review support
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 3: Data collection tools and quality assurance
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 4: Qualitative analysis and thematic coding support
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 5: Quantitative summaries and indicator interpretation
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 6: AI for evaluation reports and recommendations
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 7: Impact stories, learning briefs and knowledge products
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 8: Dashboards, data visualization and reporting support
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 9: Research ethics, privacy and responsible AI use
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 10: Developing AI-supported M&E templates
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Target Audience
Who should attend?
Target Audience
M&E officers, researchers, data officers, project managers, donor funded project teams, NGO professionals, statisticians, policy analysts, programme managers, grant officers, government planning units, development practitioners and learning specialists.
Why Attend
Key course benefits
Practical capacity building approach
Designed for government, NGOs and public institutions
Real-world African case studies
Certificate of completion
Applicable tools and templates
Interactive facilitation and exercises
Course Enquiry
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