Program Structure

Module 1: Why AI in Nutrition & Dietetics?

• How nutrition practice is shifting: digital health, wearables, and data-based guidance.
• Where AI helps: personalization, tracking, prediction, and decision support.
• What AI cannot do: clinical judgment, context, and individualized care boundaries.
• Responsible AI in health: safety, ethics, and trust.

Module 2: Nutrition Data Basics (The Foundation)

• Types of nutrition data: food logs, recalls, biometrics, labs, wearables, surveys.
• Food composition databases and nutrition labels: how data is structured.
• Data quality challenges: missing entries, under-reporting, portion bias, recall errors.
• Creating clean, usable datasets for analysis and planning.

Module 3: AI for Food Recognition & Dietary Tracking

• How image-based food recognition works (simple explanation).
• Portion estimation and common real-world errors.
• Barcode/label scanning and nutrient estimation workflows.
• Designing user-friendly tracking systems that reduce friction.

Module 4: Personalized Meal Planning & Recommendation Systems

• Personalization factors: goals, allergies, culture, budget, schedule, preferences.
• How recommendation systems suggest meals and swaps.
• Balancing macros/micros, calories, and dietary patterns (practical logic).
• Building safe constraints: medical conditions, contraindications, and red flags.

Module 5: AI for Clinical Dietetics Support (Decision Assistance)

• Screening and risk scoring: diabetes, obesity, metabolic syndrome (conceptual workflows).
• AI-assisted interpretation of trends in weight, glucose, lipids, and adherence.
• Nutrition care planning support: generating options, not replacing dietitians.
• Documentation and reporting: turning data into clear client notes.

Module 6: Behavior Change, Adherence & Coaching with AI

• Why adherence fails: psychology of food choices and routines.
• AI for habit tracking: triggers, nudges, reminders, and goal reinforcement.
• Conversational AI for coaching: safe boundaries and escalation rules.
• Designing realistic plans for real humans (not perfect templates).

Module 7: Public Health Nutrition & Population Insights

• Using AI to analyze large-scale nutrition surveys and community data.
• Identifying deficiencies and high-risk groups (data-driven targeting).
• Program design: interventions, monitoring, and impact measurement basics.
• Nutrition misinformation detection and community education support.

Module 8: Ethics, Privacy & Quality Control in Nutrition AI

• Bias in nutrition AI: cultural diets, socioeconomic factors, and accessibility.
• Privacy: what data is sensitive and how to handle it responsibly.
• Safety checks: verifying recommendations, medical disclaimers, and escalation.
• How to evaluate AI tools: accuracy, transparency, and clinical appropriateness.

Final Project

• Create an AI-Assisted Nutrition Workflow for a chosen use case.
• Include: user profile, goals, constraints, meal planning logic, tracking plan, and safety checks.
• Example projects: AI meal plan for PCOS, diabetic-friendly menu planner, sports nutrition tracker, campus canteen healthy swap system.