AI Product Design · 2024–2025 · Honda Motor Co.

Safety Driving Education System

Personalizing driver education with AI to halve traffic fatalities by 2030.

🔒

This is an internal Honda Motor Co. product. Final UI screens are confidential — this case study covers my design process, research, and decisions rather than the finished interface. Happy to walk through it in detail during a conversation.

Duration

3 months

Role

GenAI Engineer & UI/UX Design

Tools
FigmaFastAPILangChainNext.jsAzure OpenAI
19-Screen User Journey
Honda Motor Co. · 2024–2025
01

Registration

Onboarding & profile setup

02

DSQ Assessment

18Q psychometric — 9 driving traits profiled

03

AI Curriculum

Adaptive content generated from your trait profile

04

AI Lessons

Conversational AI instructor + comprehension checks

05

E-Certification

Completion celebration + digital certificate

9 DSQ traits:AggressionRisk-takingAnxietyFatigueDistractionSpeedComplianceCautiousnessConfidence

Overview

A comprehensive AI-powered driver education platform that personalizes safety training through psychometric profiling and adaptive AI instruction. Designed and built an end-to-end experience spanning 19 screens — from registration and DSQ personality assessment through AI-generated curriculum, interactive lessons with a conversational AI instructor, to e-certification.

The Problem

Traditional driving education uses a one-size-fits-all curriculum that fails to address individual risk profiles. Aggressive drivers receive the same training as cautious ones. Meanwhile, instructor shortages — especially in low- and middle-income countries — limit access to quality safety education.

15%Safety awareness improvement
19Screens designed
18QDSQ assessment
9Personality traits profiled

Research & Discovery

Driving Style Questionnaire (DSQ) — psychometric framework for risk profiling across 9 traits

Existing driving school curriculum analysis — gap identification

UX audit of legacy system — 8+ critical usability issues documented

Stakeholder alignment — Honda Safety Department, management, and learner needs

Key Insight

The biggest UX challenge wasn't the AI — it was trust. Learners needed to trust an AI instructor enough to engage honestly with their driving weaknesses.

Design Process

1

UX audit — documented critical usability failures in the legacy system

2

19-screen user journey: registration → DSQ → curriculum → AI lessons → certification

3

DSQ results page with radar chart visualizing 9 personality traits

4

AI instructor chat interface with source citations grounding every answer

5

Comprehension checks with supportive, non-punitive feedback

6

Celebration micro-animations for lesson and course completion

Critical Pivot

The original lesson page auto-played audio with no pause control, had invisible progress bars, and showed white text on white backgrounds. The UX audit revealed 8+ critical issues. The entire experience was redesigned from scratch rather than patching the existing system.

Results

15% improvement in safety awareness (Likert-scale surveys)

Production-ready system deployed for driving school use

Co-authored conference paper at NLPIR

Largest improvements for aggressive and risk-prone driver profiles

Reflection

Designing AI-powered experiences is fundamentally about designing trust. Celebration, encouragement, and clarity aren't nice-to-haves — they're essential for behavior change.

AI Product DesignUX AuditPsychometric UXSafety-Critical