A Service Design Approach to AI-Driven Responsible Food Tourism
Shifting from Extractive Consumption to Generative Participation.
MY ROLE
Product & Service Designer · UX Researcher
Output
AI-assisted service concept · Digital service layer
Context
Master’s Thesis · Collaboration with Ecobnb
Tools
Figma, LM NOTEBOOK, ChatGPT, Stitch
Project Overview
EcoFood Experience was my Master’s thesis, developed in collaboration with Ecobnb.
The project explores how AI can help travellers discover authentic local food experiences while keeping users conscious of, and in control of, their decisions.
Food Tourism as a Design Opportunity
Food is economically important, yet digitally fragmented
This fragmentation reduces the visibility of local food practices and limits meaningful engagement between travellers and host communities!
Accommodation, Transport, and attractions are widely standardizes with sustainable criteria and seamlessly are integrated within online booking systems.
No food experience! Food experiences remain scattered across separate websites or left to informal, independent discovery.
How might we help travellers discover authentic local food experiences without missing the communities behind them?
How might we help travellers discover authentic local food experiences without missing the communities behind them?
So, what is the problem?
The service aims to create value for both travellers and local hosts.
The service aims to create value for both travellers and local hosts.
For local communities, it helps keep local food experiences visible and connected to tourism.
Intended Impact
The necessary shift from extractive consumption to generative participation
Food tourism plays an important role in enabling this transition because it creates direct connections with local culture and communities
Evidence for travellers
Case studies
Surveys (n=26)
Gap 1
Food experiences exist on tourism websites, but they are disconnected from the traveller's journey.
Evidence for local hosts
Case studies
Surveys (n=17)
Local host communities remain structurally excluded from the very systems they supply.
Gap 2
Evidence for platforms
Case studies
Surveys (n=17)
Current AIs serves commercial conversion rather than reasonable guidance.
Gap 3
what can design actually do here?
Redesigning the system requires an “Inside-Out” orienatation
Design Objectives
Concept: the service layer as a guidance system
Input
core
output
User Interface Design
six digital components structure the continuous food journey
What I Would Measure
If launched, I would explore:
whether users prefer conversational AI or comparison tools
whether AI increases decision confidence and perceived control
whether the concept could scale to other platforms
whether the service improves visibility for local hosts by their profile systems
whether a designed AI experience works better than simply adding a chatbot
What I Learned
AI is becoming part of many new services, but adding AI alone does not improve an experience.
The designer’s role is to decide where AI is useful, how transparent it should be, and how users can stay in control.
For me, sustainable AI means using technology to support better decisions and strengthen the people and communities behind a service—not replace them.
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