AI on Monitoring, Evaluation, Research and Learning Training Course

Course Overview

The AI on Monitoring, Evaluation, Research and Learning (MERL) Training Course is designed to equip Monitoring and Evaluation (M&E) professionals, Monitoring, Evaluation, Accountability and Learning (MEAL) specialists, researchers, programme managers, policy analysts, humanitarian practitioners, development professionals, government officials, donor-funded project staff, and data scientists with advanced knowledge and practical competencies in applying Artificial Intelligence (AI) to improve monitoring, evaluation, research, organizational learning, and evidence-based decision-making. As organizations increasingly adopt Artificial Intelligence (AI), Machine Learning (ML), Generative AI, Large Language Models (LLMs), Predictive Analytics, Natural Language Processing (NLP), Computer Vision, Business Intelligence, Digital Transformation, Results-Based Management (RBM), Monitoring, Evaluation, Accountability and Learning (MEAL), Sustainable Development Goals (SDGs), Geographic Information Systems (GIS), Data Science, Big Data Analytics, Knowledge Management, Adaptive Management, Impact Evaluation, Research Automation, and Digital Data Collection, AI has become a transformative tool for enhancing programme effectiveness, improving data quality, accelerating research processes, and strengthening organizational performance. This course enables participants to leverage AI responsibly to optimize the complete MERL cycle while maintaining ethical standards and data integrity.

Participants will gain practical experience in AI-assisted survey design, intelligent data collection, automated data cleaning, machine learning for predictive analysis, qualitative and quantitative research automation, statistical analysis, dashboard development, GIS analytics, donor reporting, report writing, sentiment analysis, literature review automation, data visualization, evidence synthesis, forecasting, anomaly detection, impact evaluation, organizational learning systems, and AI-powered decision support. Practical sessions integrate ChatGPT, Microsoft Copilot, Google Gemini, Claude AI, Microsoft Power BI, Excel, SPSS, STATA, R, Python, SQL Server, NVivo, KoboToolbox, SurveyCTO, Open Data Kit (ODK), DHIS2, QGIS, ArcGIS, TensorFlow, cloud computing platforms, and business intelligence tools to improve programme monitoring, evaluation, research quality, donor reporting, and knowledge management across development, humanitarian, health, agriculture, education, governance, climate change, and private sector programmes.

Participants will further explore advanced concepts including AI ethics, responsible AI, data governance, algorithm transparency, AI risk management, cybersecurity, privacy protection, explainable AI (XAI), automated monitoring systems, adaptive learning systems, predictive modelling, implementation research, organizational intelligence, innovation management, digital compliance, and continuous improvement frameworks. The course demonstrates how AI enhances evidence generation, accelerates analytical workflows, improves reporting quality, supports real-time monitoring, identifies emerging trends, strengthens programme accountability, and promotes informed strategic planning while ensuring responsible and ethical AI implementation.

Delivered through instructor-led presentations, AI laboratories, practical software demonstrations, hands-on machine learning exercises, collaborative workshops, web-based tutorials, dashboard development sessions, GIS applications, simulation projects, peer learning, and comprehensive real-world case studies, this course enables participants to develop practical competencies in integrating AI into every stage of Monitoring, Evaluation, Research and Learning. Upon successful completion, participants will possess the technical, analytical, ethical, and leadership skills required to transform MERL systems through Artificial Intelligence, improve organizational learning, strengthen donor accountability, support digital transformation, and drive sustainable development through evidence-based innovation.

Course Objectives

1. Understand the principles and applications of Artificial Intelligence in Monitoring, Evaluation, Research and Learning (MERL).
2. Apply AI tools to improve programme monitoring, evaluation, and performance measurement.
3. Utilize machine learning techniques for predictive analytics and impact evaluation.
4. Automate research processes, literature reviews, data cleaning, and report generation using AI.
5. Strengthen qualitative and quantitative data analysis using AI-powered platforms.
6. Develop AI-enabled dashboards and data visualization tools for decision support.
7. Integrate AI into Monitoring, Evaluation, Accountability and Learning (MEAL) systems.
8. Apply responsible AI principles, data governance, and ethical standards in development programmes.
9. Improve organizational learning, adaptive management, and evidence-based decision-making.
10. Build institutional capacity for AI-driven digital transformation and sustainable programme performance.

Organizational Benefits

1. Strengthens institutional Monitoring, Evaluation, Research and Learning systems.
2. Improves programme performance through AI-assisted decision-making.
3. Enhances data quality, accuracy, and analytical efficiency.
4. Accelerates research, reporting, and knowledge generation.
5. Improves donor reporting and organizational accountability.
6. Supports predictive analytics for proactive programme management.
7. Enhances digital transformation and business intelligence capabilities.
8. Strengthens organizational innovation and adaptive learning.
9. Improves resource utilization through automation and intelligent analytics.
10. Promotes sustainable evidence-based planning and continuous organizational improvement.

Target Participants

• Monitoring and Evaluation Specialists
• Monitoring, Evaluation, Accountability and Learning (MEAL) Officers
• Researchers
• Programme Managers
• Project Managers
• Data Analysts
• Business Intelligence Specialists
• Public Policy Analysts
• Humanitarian Professionals
• Development Practitioners
• Public Health Specialists
• Agricultural Programme Officers
• Education Programme Managers
• Government Officials
• NGO and INGO Staff
• United Nations Agency Personnel
• Donor-funded Project Staff
• Information Management Officers
• Digital Transformation Managers
• Professionals involved in research, monitoring, evaluation, data analysis, organizational learning, knowledge management, and evidence-based programme management.

Course Outline

Module 1: Introduction to Artificial Intelligence in MERL

• Fundamentals of Artificial Intelligence and Machine Learning
• AI applications in Monitoring, Evaluation, Research and Learning
• Results-Based Management (RBM) and MEAL integration
• Digital transformation strategies
• AI opportunities and limitations
• Responsible AI principles

Case Study: Developing an AI roadmap for integrating artificial intelligence into a national development programme monitoring system.

Module 2: AI for Data Collection and Monitoring

• AI-assisted survey design
• Intelligent digital data collection
• Mobile data collection using KoboToolbox, SurveyCTO and ODK
• Automated data validation
• Real-time monitoring systems
• Data quality improvement

Case Study: Using AI-enabled mobile data collection to improve humanitarian programme monitoring.

Module 3: AI for Data Management and Advanced Analytics

• Automated data cleaning
• Machine learning algorithms
• Predictive analytics
• Statistical analysis using AI
• Anomaly detection
• Forecasting programme outcomes

Case Study: Applying predictive analytics to forecast health programme performance using historical monitoring data.

Module 4: AI for Research and Knowledge Generation

• AI-assisted literature reviews
• Natural Language Processing (NLP)
• Qualitative analysis with AI
• Quantitative research automation
• Evidence synthesis
• Research report generation

Case Study: Automating systematic literature reviews for education policy research using AI.

Module 5: AI for Monitoring and Evaluation

• AI-assisted indicator tracking
• Automated evaluation systems
• Outcome harvesting
• Impact evaluation using machine learning
• Adaptive management
• Learning systems

Case Study: Integrating AI into Monitoring and Evaluation systems for agricultural development programmes.

Module 6: AI for Dashboard Development and Business Intelligence

• Microsoft Power BI with AI features
• Interactive dashboard development
• AI-powered visualization
• GIS integration
• Business intelligence
• Decision-support analytics

Case Study: Developing an AI-powered executive dashboard for donor-funded programme performance monitoring.

Module 7: AI for Donor Reporting and Communication

• AI-assisted report writing
• Automated donor reporting
• Executive summaries
• Data storytelling
• Visualization techniques
• Presentation automation

Case Study: Producing donor-compliant reports using Generative AI and business intelligence platforms.

Module 8: AI Ethics, Governance and Data Security

• AI ethics
• Responsible AI implementation
• Data governance
• Privacy protection
• Cybersecurity
• Regulatory compliance

Case Study: Establishing governance frameworks for responsible AI deployment in humanitarian organizations.

Module 9: AI for Organizational Learning and Knowledge Management

• Knowledge management systems
• Organizational intelligence
• Adaptive learning
• Lessons learned automation
• Collaboration platforms
• Continuous improvement

Case Study: Building an AI-driven organizational learning platform for a multinational NGO.

Module 10: Emerging AI Technologies for Development Programmes

• Generative AI
• Large Language Models (LLMs)
• Computer Vision
• Intelligent automation
• Cloud AI platforms
• Innovation management

Case Study: Applying Generative AI to automate programme documentation and learning products.

Module 11: Practical AI Applications Using Industry Tools

• ChatGPT
• Microsoft Copilot
• Google Gemini
• Claude AI
• Python AI libraries
• TensorFlow fundamentals

Case Study: Comparing AI platforms to automate monitoring, evaluation, and research workflows.

Module 12: Future Trends in AI for MERL

• Enterprise AI integration
• Advanced predictive modelling
• Digital ecosystems
• Smart decision-support systems
• AI-enabled organizational transformation
• Future innovations

Case Study: Designing an integrated AI-enabled Monitoring, Evaluation, Research and Learning ecosystem combining ChatGPT, Microsoft Copilot, Google Gemini, Claude AI, Microsoft Power BI, Excel, SPSS, STATA, R, Python, SQL Server, NVivo, KoboToolbox, SurveyCTO, Open Data Kit (ODK), DHIS2, QGIS, ArcGIS, TensorFlow, Artificial Intelligence, Machine Learning, Natural Language Processing, Predictive Analytics, Results-Based Management (RBM), Monitoring, Evaluation, Accountability and Learning (MEAL), Geographic Information Systems (GIS), Business Intelligence, Knowledge Management, cloud computing, adaptive management, donor reporting, implementation research, digital transformation, and evidence-based decision-making to improve programme performance, institutional accountability, organizational learning, and sustainable development outcomes.

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, AI laboratories, practical exercises, web-based tutorials, machine learning demonstrations, dashboard development workshops, statistical analysis practicals using SPSS, STATA, R, and Python, Power BI visualization, GIS mapping, AI-assisted report writing, collaborative group work, cloud computing demonstrations, digital transformation simulations, peer learning, software demonstrations, and comprehensive real-world case studies. Our facilitators are seasoned experts with over a decade of experience in Artificial Intelligence, Monitoring and Evaluation, research, data science, machine learning, business intelligence, digital transformation, and international development programmes.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two participants and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit www.fdc-k.org for more information.