maya, multilingual onboarding
maya reduces everyday language friction in care environments through two linked systems: an onboarding assistant plus a gradual, adaptive language shift from native language to German, supported by structured shared living matching.
My role
Product design research synthesis interaction and UI prototyping
Scope
Research -> prototype System concept
Domain
Enterprise Onboarding Workflow Systems Language Accessibility
Tools
Figma. Claude Code
Team
Luke Caporelli Jan Lonardoni Lukas Predan Danlei Fu Peter Schneider
Year
2025
context
Settings and stakes
Care organizations depend on fast, precise communication across shifts and teams. The partner context required privacy, cultural sensitivity, and adoption from day one.
Core problem
International trainees often have formal language certificates but still struggle with everyday routines, small talk, and real workplace phrasing. This creates repeated explanations, time loss, and avoidable misunderstandings.
Why the usual fixes fail
Courses and one off trainings rarely translate into reliable performance under daily work pressure. Without continuous, in context support, the gap stays.
Partner: stiftung liebenau
8,900+
Employees
Research signals
6,826 €
Direct net cost per apprenticeship dropout
68,000
Apprentices with foreign citizenship in German health and care occupations
350,000
Nursing staff potentially missing in Germany by 2034
Discover
Research approach
We combined desk research, focus group interviews, on site visits, and stakeholder input to capture both lived reality and organizational constraints.
What we heard repeatedly
Trainees avoid asking questions, feel uncertain in routines, and default to their native language in shared living. Staff carry recurring re explanation work and coordination overhead.
Key insight
The language problem starts before the job, inside everyday life. If daily routines stay mono language, the workplace inherits the deficit.
define
Target direction
Enable everyday language capability through a human, low pressure path, not exam driven enforcement. Independence in daily life is the foundation for sustainable progress.
Problem framing
Language is the upstream lever that amplifies satellite issues such as onboarding overload, missing local knowledge, housing friction, and staff burden. We focused on language because it unlocks multiple downstream wins.
Success criteria
Trust and adoption are the success gates in this domain. The product must feel safe, respectful, and operationally realistic for high workload teams.
Problem framing and direction
Problem map
Language is the upstream lever that drives multiple downstream failures.
Why existing solutions fail
Courses do not transfer into daily care communication. Training needs in context support.
Design principles
Mutual learning, help for self help, everyday independence first.
System rationale
Why shared housing is a leverage point
Daily language exposure happens at home, not in class. Matching shapes habits before the first shift.
Peer learning built into the system
Pair advanced German speakers with beginners to turn everyday routines into low pressure practice.
One assistant, staged maturity
Same assistant evolves from onboarding clarity to everyday support as confidence grows.
ideate
System concept
maya is a living and integration platform combining shared living group matching (shared housing) with digital guidance for onboarding and everyday life, designed to reduce dependency and lower coordination load.
Two moments that matter
Pre arrival and early onboarding, when uncertainty is highest. Then the first months in daily life, when routines form and language habits lock in.
Language learning mechanism
maya starts in the native language and shifts gradually toward German, enabling passive exposure and active practice without forcing artificial learning moments.
Language Integration Levels -> Co-Living Matching Structures
prototype
AI Assistant, Phase 1 to 2
One AI assistant supports trainees across the full journey. In Phase 1 it handles onboarding questions for applicants and new arrivals to enable early self service and reduce repetitive explanations. In Phase 2 the same assistant shifts into everyday support after training start, helping with routines and practical questions while keeping a safe first line scope.
Shared living groups matching system
A structured matching flow captures language level and experience and forms learning balanced shared living groups, pairing advanced German speakers with beginners to enable everyday exposure and peer support.
Internal Dashboard Concept
A provider facing dashboard concept offers overview and decision support, so recurring questions and support needs can be identified and handled systematically rather than ad hoc.
matching flow
matching flow
matching flow
deliver
What we shipped as a semester outcome
A coherent product narrative and three linked prototypes: onboarding assistant, everyday assistant with adaptive language shift, and shared living groups matching logic.
Domain constraints in the first phases
Early phases intentionally exclude medical data and clinical workflows. We focused on onboarding and daily life support to build trust and adoption first.
Operational readiness signals
Privacy, cultural tone, and acceptance shaped the interaction model, escalation logic, and scope boundaries as primary design constraints.
prototype
impact
Expected user impact
Less uncertainty in daily routines, fewer avoidance moments, and faster confidence. The assistant lowers the social cost of asking questions and supports steady learning.
Expected organizational impact
Reduced staff overhead from repeated explanations and fewer coordination loops. Better shared living groups matching can also reduce friction in shared living and improve housing utilization.
Economic framing
We used a simplified model to communicate the order of magnitude: support time, fully loaded staff cost, and dropout cost reference values, framed as expected impact rather than measured outcomes.
Research signals
€131 / month
Expected savings per trainee
3.75 h / month
Expected STAFF TIME SAVED PER TRAINEE
10%
Expected decrease in dropouts (assumption)
What changed my thinking
Trust constraints
In care, trust is the feature. Privacy and tone decide adoption.
Next build targets
Separate private and work assistants. Add source clarity and escalation paths.
Principle statement
Prevent language breakdown early, or pay for rework later across the whole system.
reflection
What I learned
Personalization only works when routing decisions are transparent and explainable.
What I would improve next
Make escalation paths and source transparency more explicit in the UI, and separate private life and work context assistants to reduce ambiguity.
What I would build if expanded
After trust is established, explore deeper workflow integration as a later phase with strict governance and access control, aligned with a phased roadmap.
The project shows how chats feel more alive with gestures and playful interactions. Actions like pressing, shaking, or holding add richer expression than emojis.
Expressive
Messaging
In collaboration with
Jannes Daur and Leon Burg
We designed and prototyped seven distinct features, each tailored to a specific emotion. By combining simple interactions with expressive behaviors, the features make emotional states more tangible and engaging in chat. The result is a richer, more playful communication experience beyond emojis.
Solution
Digital chats often lack nuance and feel flat. The challenge was to design interactions that add depth without overloading the interface.
Problem
Process
1. First Touchpoints:
Reflected on personal communication challenges and identified memories as key to deeper conversations.
2. Benchmarking:
Analyzed existing messaging apps and found limited options for nuanced emotional expression.
3. Framing the Challenge:
Defined our goal to make emotions in chats clearer, more natural, and easier to share.
4. Deep dive research:
Explored scientific models like Ekman, Plutchik, and “How We Feel” to map emotions.
5. Emotion Selection:
Filtered and structured a broad emotion set using context-based evaluation matrices.
6. Prototyping:
Developed and iterated concepts to create features that bring more emotional depth to messaging.
Anger
Affection and intimacy, tender or passionate.
Romance
Affection and intimacy, tender or passionate.
Joy
Moments of happiness and success, from gentle to ecstatic.
Sarcasm
Irony and hidden critique, playful or biting.