AI for M&E Training Course

AI for M&E Training Course

Introduction

AI for M&E Training Course equips development professionals with cutting-edge artificial intelligence, machine learning, and data automation tools to enhance evidence-based decision-making, real-time performance tracking, and predictive analytics. As programs grow more complex and data-intensive, AI-driven M&E enables organizations to move beyond traditional reporting toward adaptive management, intelligent insights, and outcome-driven accountability. This course bridges the gap between conventional M&E frameworks and next-generation digital intelligence.

Participants will gain practical skills in applying AI-powered data collection, automated analysis, natural language processing, geospatial intelligence, and predictive modeling across development, humanitarian, health, education, climate, and governance programs. Through hands-on case studies and applied simulations, learners will understand how AI improves efficiency, accuracy, learning, risk detection, and impact measurement, while ensuring ethical, inclusive, and responsible AI use in M&E systems.

Course Duration

10 days

Course Objectives

By the end of the course, participants will be able to:

1. Apply AI and machine learning concepts within M&E systems
2. Design AI-enabled M&E frameworks for complex programs
3. Automate data collection, cleaning, and validation processes
4. Use predictive analytics for early warning and risk management
5. Integrate big data and real-time monitoring into M&E plans
6. Apply natural language processing (NLP) for qualitative analysis
7. Leverage geospatial AI for location-based impact analysis
8. Develop AI-powered dashboards and visualizations
9. Enhance adaptive management using AI-generated insights
10. Ensure ethical, transparent, and responsible AI use in M&E
11. Improve learning and decision-making using AI evidence
12. Assess AI readiness and digital maturity of M&E systems
13. Evaluate the impact and value of AI investments in programs

Target Audience

1. Monitoring & Evaluation Officers and Specialists
2. Development and Humanitarian Program Managers
3. Data Analysts and Research Officers
4. Donor Agency and Development Partner Staff
5. Government Planning and Policy Officers
6. NGO and INGO Technical Advisors
7. Impact Evaluation Consultants
8. Digital Transformation and Innovation Leads

Course Modules

Module 1: Introduction to AI in Monitoring & Evaluation

• Overview of AI, ML, and automation
• Evolution of M&E in the digital age
• AI vs traditional M&E approaches
• Opportunities and limitations of AI
• Case Study: AI adoption in donor-funded programs

Module 2: AI-Enabled M&E Frameworks

• Integrating AI into Results Frameworks
• Theory of Change and AI alignment
• AI-supported indicator design
• Data-driven learning loops
• Case Study: AI-enhanced Results-Based Management

Module 3: Data Foundations for AI in M&E

• Structured vs unstructured data
• Data quality and governance
• Data interoperability and integration
• Preparing datasets for AI models
• Case Study: Cleaning multi-source program data

Module 4: Automated Data Collection Tools

• Mobile data collection with AI
• Sensors, IoT, and remote data capture
• AI-powered surveys and chatbots
• Reducing data collection bias
• Case Study: AI surveys in humanitarian response

Module 5: Machine Learning for M&E Analysis

• Supervised and unsupervised learning
• Pattern recognition in program data
• Trend and anomaly detection
• Model selection basics
• Case Study: ML for performance trend analysis

Module 6: Predictive Analytics & Early Warning Systems

• Forecasting outcomes and risks
• Predicting program underperformance
• Scenario modeling
• Decision-support systems
• Case Study: Predicting project delays

Module 7: Natural Language Processing (NLP)

• Analyzing qualitative data with AI
• Text mining and sentiment analysis
• Processing reports and interviews
• AI-assisted thematic analysis
• Case Study: NLP in beneficiary feedback

Module 8: AI for Geospatial & Remote Sensing M&E

• Satellite imagery and GIS integration
• AI for spatial impact analysis
• Climate and environmental monitoring
• Mapping service delivery gaps
• Case Study: AI satellite data for climate programs

Module 9: Real-Time Monitoring & Dashboards

• AI-driven dashboards
• Automated reporting systems
• Data visualization best practices
• Real-time alerts and notifications
• Case Study: Live dashboards for donor reporting

Module 10: Adaptive Management Using AI Insights

• Learning-oriented M&E systems
• Using AI for course correction
• Decision-making under uncertainty
• Continuous improvement models
• Case Study: Adaptive programming with AI

Module 11: Ethics, Bias & Responsible AI in M&E

• Algorithmic bias and fairness
• Data privacy and protection
• Transparency and explainability
• Safeguarding vulnerable populations
• Case Study: Ethical risks in AI evaluations

Module 12: AI Tools & Platforms for M&E

• Overview of AI M&E software
• Open-source vs proprietary tools
• Integration with existing systems
• Cost-benefit considerations
• Case Study: Selecting AI tools for NGOs

Module 13: Evaluating AI-Supported Programs

• Measuring AI effectiveness
• AI contribution to outcomes
• Cost-efficiency analysis
• Learning and accountability
• Case Study: Evaluating AI pilot projects

Module 14: AI Readiness & Capacity Assessment

• Organizational AI maturity models
• Skills and infrastructure needs
• Change management strategies
• Capacity-building approaches
• Case Study: AI readiness assessment

Module 15: Future of AI in M&E

• Emerging AI trends
• Generative AI in evaluation
• AI and adaptive governance
• Preparing future-ready M&E systems
• Case Study: AI-driven future M&E models

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.