UX/UI Training Program

Complete curriculum for the Figma EDU Bootcamp 2026 - modules, lessons, exercises, and no-code development training

Program Curriculum

The Fataplus UX/UI Product Design Bootcamp follows a learn-by-doing methodology where participants ship real products for AgriTech clients while mastering design and development fundamentals.

Core Modules

Module 1: Research & Ethnography

Field Research Methods

Learn ethnographic research techniques adapted for rural AgriTech contexts:

• Interview Protocol Design: Structure conversations with farmers, cooperative managers, and agronomists
• Observation Studies: Shadow irrigation rituals and cooperative scheduling workflows
• Data Collection: Capture transcripts, field notes, and quantitative survey data
• Bilingual Communication: Practice French/Malagasy interview techniques for inclusive research

Real Example: Team Alaotra conducted interviews with rice farmers who stated: “Nous attendons que le champ d’à côté commence” (We wait for the neighboring field to start) - revealing reliance on peer observation rather than scientific timing.

Asset Ingestion & Synthesis

Process raw research into actionable insights:

• Research Digest Creation: Compile interview transcripts, API specs, and survey data into structured tables
• Insight Extraction: Identify patterns, pain points, and opportunity areas
• Evidence Mapping: Link insights to direct quotes and quantitative data
• Gap Analysis: Identify missing data sources and plan follow-up research

Tools: Miro boards, Notion databases, research digest templates

Capstone Example:

Source	Key Insight	Evidence	Follow-up
Interview_RiceFarmer_001	Farmers rely on neighbors for timing	”Nous attendons que le champ d’à côté commence”	Capture irrigation diary over 4 weeks
CoopTraining_Sept2025	Need offline-first onboarding	Trainers request printable guides	Prototype SMS onboarding script
PilotSurvey_July2025	68% use basic Android phones	Survey data	Define minimum device requirements

Persona Development

Build rich persona profiles that guide design decisions:

• Persona Structure: Story, Jobs-to-be-Done, Pains & Gains, Tech Preferences
• Segment Definition: Primary users vs. secondary stakeholders
• Trust Anchors: Identify influencers and decision-makers in the user’s ecosystem
• Validation Planning: Define co-design sessions to test assumptions

Sample Persona: Voahirana Randria - Cooperative Irrigation Scheduler

Persona: Voahirana Randria

Segment: Cooperative Irrigation Scheduler
Location: Andilamena cooperative, Alaotra-Mangoro

Story: Coordinates watering schedules for 65 rice farmers, balancing limited pump availability and seasonal rains using paper logs, WhatsApp, and weekly meetings.

Primary JTBD: Ensure equitable irrigation slots so every farmer receives water at the optimal time

Top Pains: Manual scheduling causes conflicts; forecasts unreliable; lacks pump downtime visibility

Top Gains: Wants predictive guidance, automated alerts, proof of impact for donors

Tech Setup: Android phone with intermittent 3G; cooperative laptop shared weekly

Trust Anchors: FOFIFA agronomists, cooperative elders

Module 2: Concept Development & AI Strategy

Insight-to-Concept Mapping

Transform research insights into prioritized product concepts:

• Concept Brainstorming: Generate AI-enabled solutions addressing user pains
• Impact/Feasibility Scoring: Evaluate concepts on 2x2 matrix
• Portfolio Prioritization: Select top 3 concepts for MVP development
• Evidence-Based Rationale: Justify each concept with research insights

Exercise: Map top 3 insights to AI concepts with impact/feasibility matrix

Capstone Portfolio:

1. Predictive Watering Advisor (High Impact, Medium Feasibility)
   • SMS and app alerts with explainable recommendations
   • Addresses: Unreliable timing, water waste, peer dependency
2. Cooperative Scheduling Optimizer (High Impact, High Feasibility)
   • Drag/drop calendar resolving pump conflicts
   • Addresses: Manual scheduling errors, equity issues
3. Impact Analytics Coach (Medium Impact, High Feasibility)
   • Automated reporting to MAEP and donors with SDG tracking
   • Addresses: Proof of impact, donor visibility

Data Readiness Audit

Assess AI feasibility through systematic data evaluation:

• Data Inventory: Map available data sources (APIs, databases, manual logs)
• Quality Assessment: Evaluate completeness, accuracy, timeliness
• Gap Identification: Document missing data and remediation plans
• Governance Alignment: Ensure privacy compliance and data stewardship

Audit Framework:

• Readiness Rating: Low / Medium-Low / Medium / Medium-High / High
• Gap Analysis: Evapotranspiration historical data missing for 2023
• Remediation Plan: Digitize pump logs, secure MeteoMada API contract, implement Supabase storage
• Timeline: Data stewards assigned by Week 2, API caching layer by late November

AI Service Blueprint

Design end-to-end service experiences with AI touchpoints:

• Stage Mapping: Awareness → Prepare → Receive Alert → Execute → Log Feedback → Report Impact
• Frontstage/Backstage: Separate user-facing UI from operational workflows
• AI Assist Points: Define where AI adds value vs. human intervention
• Safeguards: Human-in-the-loop checkpoints, fail-safes, training needs

Example Stage: Receive Alert

Element	Description
User Action	Farmer gets watering recommendation at dawn
Frontstage	Mobile push & SMS alert with rationale
Backstage	Scheduling engine queues alert
AI Assist	Combines weather forecast + pump availability + soil data
Systems	MeteoMada API, scheduling microservice
Safeguard	Agronomist approval for AI schedule overrides

Module 3: UX/UI Design & Prototyping

UX Flow Design

Create user flows that handle edge cases and offline scenarios:

• Journey Mapping: Map user paths from entry point to goal completion
• Offline-First Design: Plan SMS fallbacks, queue notifications, cached data
• Micro-Interactions: Design confirmation states, loading indicators, error messages
• Accessibility: High contrast for outdoor use, voice prompts for low literacy

Exercise: Design alert-to-action micro-journey with offline fallback screens

Smart Irrigation Flow:

1. Receive Alert

   Farmer receives SMS/push notification at dawn with watering recommendation and weather summary

2. Review Context

   Opens app to see forecast card, pump availability status, and alternative time slots

3. Confirm or Defer

   Taps “Confirm” or requests alternative slot if pump unavailable

4. Execute Irrigation

   Receives pump access code, irrigates plot, logs start/end times

5. Log Outcome

   Submits voice/text feedback on irrigation success and crop status

Component & Design System

Build reusable design systems in Figma:

• Component Library: Buttons, cards, status badges, toast notifications
• Design Tokens: Bilingual typography (FR/MG), color palette with outdoor contrast
• Offline States: Empty states, sync indicators, cached data badges
• Responsive Patterns: Mobile-first grids, tablet dashboards

Key Screens:

• Alert feed (mobile)
• Irrigation schedule timeline (mobile + web)
• Weather insights card (widget)
• Cooperative dashboard (web)
• Onboarding wizard (mobile)

Customer Journey Mapping

Design holistic experiences across the user lifecycle:

Journey Structure: Awareness → Consideration → Onboarding → Activation → Adoption → Impact Reporting → Advocacy

Sample Stage: Activation

Element	Details
User Goal	Receive and act on first alerts
Touchpoints	Push notification, SMS, phone check-in
Emotions	Confident if pump ready, anxious if conflicts
Metrics	Alert acknowledgment rate
Opportunity	Provide automated alternative slots + support hotline

Improvement Experiments:

• Test voice-note instructions for low-literacy farmers
• Pilot community leaderboard to gamify water savings
• Align alerts with local radio bulletins for redundancy

Module 4: No-Code Development

Platform Selection & Setup

Choose and configure no-code platforms for MVP delivery:

Stack:

• Bubble: Web admin dashboard for cooperatives and agronomists
• FlutterFlow: Mobile companion app for farmers
• Supabase: Backend database and real-time sync
• Twilio: SMS integration for offline fallback
• Zapier: Automation workflows for escalations

Setup Checklist:

• Bubble workspace provisioned
• FlutterFlow project created with offline plugin
• Supabase schema initialized
• Twilio pilot numbers acquired
• API keys configured in environment variables

Component & Data Mapping

Map Figma designs to no-code components with data bindings:

Flow	Screen/Component	Data Sources	Integrations	Notes
Receive Alert	Alert list, detail modal	MeteoMada forecast, Alert entity	Twilio SMS	Cache last 3 alerts offline
Schedule Slot	Drag/drop calendar	ScheduleSlot, Pump	Supabase RPC	Conflict detection rules
Feedback Log	Voice/text submission	FeedbackEntry	OpenAI transcription	Flag for agronomist review
KPI Dashboard	Charts, summary cards	Analytics table	Google Sheets export	Multi-language labels

Sprint Delivery

Execute iterative no-code sprints:

Sprint Schedule:

Sprint	Focus	Deliverables	Owner	Validation
0	Setup	Design tokens, platform setup, data schema draft	Lanto	Internal review
1	Alerts MVP	Alert feed, SMS workflow, dashboard skeleton	Lanto + Hery	Farmer pilot with 5 users
2	Scheduling	Drag/drop schedule, conflict resolution rules	Lanto	Cooperative simulation workshop
3	Analytics	Impact dashboard, training materials	Rado	Field test + KPI baseline

Optional Extension: Prototype MVP component in FlutterFlow using provided data schema

Automation & AI Prompts

Implement workflows and AI integrations:

Bubble Workflows:

• On schedule update → Send SMS to affected farmers
• On conflict detected → Notify cooperative coordinator
• Daily at 06:00 → Queue weather-based alerts

Supabase Functions:

• Calculate weekly water usage metrics
• Aggregate feedback for agronomist dashboard
• Archive alerts older than 30 days

AI Prompt Kits:

Prompt: "Explain irrigation recommendation"
Context: Weather forecast + crop stage + soil moisture
Output: Bilingual SMS (FR/MG) with actionable timing
Model: GPT-4 with temperature 0.3 for consistency

Prompt: "Summarize weekly water usage"
Context: Farmer feedback logs + pump usage data
Output: Dashboard card with savings percentage and SDG alignment
Model: Claude 3.5 Sonnet for structured analysis

Module 5: Adoption & Impact

Metrics & KPI Framework

Define success metrics and instrumentation:

Primary KPIs:

• Yield Lift: +15% target
• Water Savings: +20% target
• Weekly Active Users: ≥70% during pilot
• NPS: ≥35

Supporting Indicators:

• Onboarding completion time (under 15 min target)
• Alert acknowledgment rate (≥75% target)
• Schedule conflicts resolved (under 12h target)
• Bootcamp lesson satisfaction (≥4.2/5 target)

Instrumentation:

• Capture alert acknowledgments in Supabase
• Log offline mode usage frequency
• Track feedback submission rates
• Export weekly reports to Google Sheets

Training & Content Creation

Develop adoption materials for diverse literacy levels:

Delivery Enablement:

• Video tutorials (FR/MG with subtitles)
• Printable quick-start guides
• WhatsApp micro-lessons (daily tips)
• Radio bulletin scripts aligned with alerts
• Bootcamp lab integration materials

Training Workshops:

• Co-design sessions with Voahirana and cooperative peers
• Onboarding wizard walkthroughs
• Agronomist training on AI recommendation review
• MAEP reporting dashboard tutorials

Field Validation & Iteration

Test prototypes with real users and incorporate feedback:

Validation Checkpoints:

• Week 2: Concept review with FOFIFA leadership
• Week 6: Prototype field test with 5 farmers
• Week 12: MVP launch with 65 farmers
• Week 14: Impact readout with cooperative champions

Feedback Sessions:

• Daily Slack check-ins with mentor rotation
• Office hours with Product Experience Engineer
• Loom video feedback for each team
• Follow-up peer critique sessions
• Joint reviews with cooperative champions

Weekly Lesson Format

Each bootcamp session follows this 6-hour agenda:

Time	Activity	Owner	Tools
09:00-09:15	Kickoff & context briefing	Soa (Mentor)	Slides, Playbook
09:15-10:00	Insight synthesis breakout	Cohort teams	Miro board, research digest
10:00-11:00	Concept prioritization workshop	Soa & Hery	Votenote, Notion
11:00-12:00	UX flow sketching	Lanto support	Figma, FigJam
13:00-14:00	No-code component mapping	Lanto	FlutterFlow/Bubble sandboxes
14:00-15:00	CX journey reflection	Rado	Journey map template
15:00-16:00	Team presentations + feedback	All mentors	Zoom + Loom recording

Resources & Templates

All participants receive access to:

• Research digest template: Structured table for interview synthesis
• Persona canvas: Jobs-to-be-Done framework with tech preferences
• Concept-to-prototype plan: 7-section product blueprint
• No-code sprint template: Component mapping + automation checklist
• Journey map template: 7-stage lifecycle with metrics and experiments
• Data readiness audit: Gap analysis + remediation roadmap
• Bilingual copy checklist: FR/MG translation validation

Tools & Technology

Design

Figma, FigJam, Miro

Collaboration

Slack, Notion, Zoom, Loom

No-Code

Bubble, FlutterFlow, Zapier

Backend

Supabase, MeteoMada API

Communication

Twilio SMS, WhatsApp

AI

OpenAI GPT-4, Claude 3.5 Sonnet

Next Steps

AgriTech Design Lab

Explore the Smart Irrigation case study with real design artifacts

Bootcamp Overview

Return to program overview and certification details