Entry Requirements for AI For Policy Research, M&E & Evidence-Based Decision-Making
Learners must have basic digital literacy, including the ability to use a computer, internet browsing, and common productivity tools.
Duration: 1 Months
Delivery Method: Both Online & Physical
Fee Structure for AI For Policy Research, M&E & Evidence-Based Decision-Making
| Full Course Fees | |
|---|---|
| Registration Fee | KES 0.00 ($ 0.00) |
| Certification Fee | KES 0.00 ($ 0.00) |
| Administration Fee | KES 0.00 ($ 0.00) |
| Internal Exam Fee | KES 0.00 ($ 0.00) |
| External Exam Fee | KES 0.00 ($ 0.00) |
| Examining Body Membership Fee * | KES 0.00 ($ 0.00) |
| Tuition Fee | KES 0.00 ($ 0.00) |
| Fees Totals | KES 0.00 ($ 0.00) |
| * Examining Body Membership Fee may be payable through us or directly to the Examining Body | |
Course Requirements for AI For Policy Research, M&E & Evidence-Based Decision-Making
Practical Requirements for AI For Policy Research, M&E & Evidence-Based Decision-Making (where applicable)
Course Units/Overview for AI For Policy Research, M&E & Evidence-Based Decision-Making
AI for Policy Research, M&E & Evidence-Based Decision-Making
Target: Policymakers, think tanks, NGOs
Lesson 1: AI in Policy Ecosystems
- African governance contexts
Lesson 2: Advanced Research Design
- Mixed-methods + AI
Lesson 3: AI-Assisted Data Analysis
- Thematic, statistical, trend analysis
Lesson 4: Monitoring, Evaluation & Learning (MEL)
- AI for indicators & dashboards
Lesson 5: Policy Drafting & Scenario Modeling
- Evidence-driven recommendations
Lesson 6: Data Visualization & Storytelling
- Communicating insights
Lesson 7: Ethics, Bias & Accountability
- Responsible policy AI
Lesson 8: Capstone Project
- Policy paper or M&E framework
Course Description for AI For Policy Research, M&E & Evidence-Based Decision-Making
Course Description
Course Code: APRMED-301
Course Title: AI for Policy Research, Monitoring & Evaluation (M&E) and Evidence-Based Decision-Making
This course provides learners with a comprehensive understanding of how Artificial Intelligence (AI) and data-driven tools can be applied to policy research, monitoring and evaluation, and evidence-based decision-making in governmental, non-governmental, and institutional contexts. The course equips participants with the knowledge and skills to design, implement, and evaluate policies and programs using AI-supported analytics, ensuring decisions are grounded in robust evidence and contextual insights.
Learners explore the application of AI across the full policy cycle, including problem identification, data collection, trend analysis, predictive modeling, impact assessment, and evidence-based policy formulation. The course examines how machine learning, natural language processing, and data visualization tools can enhance research efficiency, improve accuracy in reporting, and strengthen decision-making at institutional and national levels (OECD, 2021; UNECA, 2022; World Bank, 2023; UNESCO, 2023).
A critical focus is placed on ethical, transparent, and accountable use of AI in policy research, including safeguarding data privacy, mitigating algorithmic bias, ensuring reproducibility, and promoting inclusive decision-making. Learners are trained to interpret AI-generated outputs critically and integrate them with traditional research methods, ensuring that technology complements human judgement rather than replacing it (Floridi et al., 2018; Banerjee & Duflo, 2019; Acemoglu & Robinson, 2019; Collier, 2018).
By the end of the course, participants will be able to design AI-enhanced research and M&E frameworks, apply data-driven insights to policy and programmatic decisions, and develop evidence-based strategies that are scalable, sustainable, and contextually relevant. This course is suitable for policy analysts, program managers, development professionals, researchers, and decision-makers seeking to leverage AI for high-impact governance and organisational learning.
Course Instructor(s) for AI For Policy Research, M&E & Evidence-Based Decision-Making
TBA
Examining Body for AI For Policy Research, M&E & Evidence-Based Decision-Making
FINSTOCK EVARSITY COLLEGE
FAQs for AI For Policy Research, M&E & Evidence-Based Decision-Making
Course FAQs – APRMED-301
1. Who should take this course?
This course is designed for policy analysts, program managers, researchers, decision-makers, and development professionals who wish to leverage AI for evidence-based policy research, monitoring, and evaluation (M&E).
2. Do I need prior experience in AI or data science?
No. The course introduces AI concepts progressively and focuses on practical application in policy research and M&E, so beginners and intermediate learners can participate effectively.
3. What skills will I gain?
Participants will be able to:
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Apply AI and data analytics to policy research and M&E frameworks
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Conduct evidence-based decision-making using predictive and prescriptive models
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Interpret AI-generated insights critically and ethically
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Design AI-supported data collection, monitoring, and evaluation systems
4. How is the course delivered?
Delivery is blended:
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Face-to-face sessions (computer lab-based where applicable)
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Live online sessions (Google Meet)
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Self-paced learning through the LMS
5. How long is the course?
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Duration: 1 month
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Lessons: 8 sessions, 1 hour each
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Schedule: Monday to Friday, with combined sessions on select days
6. How will I be assessed?
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Assignments + Continuous Assessment Tests (CATs): 40%
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Final Exam: 60%
Assessments are practical and focus on real-world policy research and M&E applications.
7. Will I receive a certificate?
Yes. Learners who successfully complete the course and meet assessment requirements will receive a Certificate of Completion from Finstock Evarsity College.
8. Is the course aligned with international standards?
Yes. It draws on frameworks from UNESCO, OECD, World Bank, and UNECA, ensuring international relevance while addressing African institutional realities.
9. Can I use AI tools freely during the course?
Yes. The course teaches responsible and ethical AI use, emphasising academic integrity, data privacy, and transparency in decision-making.
10. How does this course support career advancement?
It prepares learners for policy research, M&E roles, program evaluation, and data-driven decision-making, strengthening both technical and strategic skills relevant to governance, NGOs, development agencies, and research institutions.
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