AI for Food Security and Nutrition Governance Program

Course Objectives

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

1. Apply AI to strengthen nutrition leadership and governance, aligning with PMNP’s LNAP formulation and budget tracking.

2. Use AI for data quality checks and analysis of FHSIS, OPT Plus, and local surveys to support monitoring and advocacy.

3. Integrate AI tools into primary health care service planning and frontline delivery.

4. Collaborate with academic institutions in BARMM for sustainable training and research partnerships.

5. Produce an AI-enabled LGU Food Security and Nutrition Governance Plan grounded in PMNP results frameworks.
   
   

Target Participants

• Local Chief Executives & Local Nutrition Committees (LNCs)

• Municipal Nutrition Action Officers (MNAOs)

• Municipal/Provincial Planning and Development Officers

• Local Agriculture and Health Officers

• Barangay Nutrition Scholars & Barangay Health Workers (through extension modules)

Methodology

The course uses Project-Based Learning (PBL) centered on LGUs’ actual PMNP projects, delivered through:

• Online Synchronous: Webinars, AI labs, and peer learning clinics.

• Online Asynchronous: Microlearning videos, toolkits, AI guides, and reflection activities.

• Onsite Practical Application: Direct integration into LGU projects such as LNAP updating, data validation, and dashboard development.
  
  

Course Structure (8 Weeks)

Phase 1. Foundations & Project Setup (Week 1)

• PMNP pillars: leadership, PHC, data & M&E

• Role of academic institutions as training partners

• Project task: Select LGU project anchor (e.g., LNAP, food security plan, data quality check).
  
  

Phase 2. AI for Nutrition Situation Analysis (Weeks 2–3)

• AI-assisted analysis of FHSIS, OPT Plus, poverty data.

• Onsite: Conduct AI-enabled local nutrition profile.

• Output: Draft Nutrition Situation Report.
  
  

Phase 3. AI for Planning & PHC Convergence (Weeks 4–5)

• AI forecasting for maternal/child health, food security, emergencies.

• Support frontline providers in PHC & nutrition convergence.

• Output: Draft plan sections on PHC and food security.
  
  

Phase 4. AI for Governance & Accountability (Week 6)

• Adaptive leadership for multisectoral coordination.

• Build AI-assisted budget tracking tools and accountability dashboards.

• Output: Dashboard prototype for LGU.
  
  

Phase 5. Capstone: AI-Enabled LGU Plan (Weeks 7–8)

• Peer review with academic institutions.

• Presentation at regional summit with UNICEF, BARMM MOH, MOST, and MAFAR.

• Final Output: AI-enabled Food Security and Nutrition Governance Plan (aligned with PMNP indicators).
  
  

Integration with PMNP Packages

PMNP Capacity Area Course Application

Nutrition Leadership & Governance AI-supported policy advocacy, budget tracking, LNAP costing

Primary Health Care AI-enabled service planning, PHC dashboard integration

Data, Monitoring & Evaluation AI-assisted data quality checks (FHSIS, OPT Plus), predictive analytics

Outputs & Certification

• Intermediate Outputs: Nutrition profile, plan draft, dashboard prototype.

• Final Output: AI-enabled Food Security & Nutrition Governance Plan.

• Certification: Jointly issued by ASoG, BARMM academic institutions, and UNICEF partners.
  
  

Duration & Commitment

• 8 weeks (hybrid)

• 1-3 hrs/month synchronous online

• 1–2 hrs/week asynchronous

• Onsite applications integrated with LGU PMNP activities