Hackathon prototype for an AI mentorship platform that delivers personalized guidance through mentor discovery, user profiling, and conversational interaction.
Mentora is a team-built prototype exploring how AI-powered digital mentors can provide more accessible, personalized, and judgment-oriented guidance for students and early-career users.
The project was developed during a hackathon and presented as a live demo and pitch deck.
- Live demo: Mentora Demo
- Team repository: sage-demo
- Hackathon page: Hack-to-the-Future
Students often work hard but still lack informed direction. Generic AI tools are fast and scalable, but they are often weak in domain-specific judgment, prioritization, and personalized strategic guidance.
Mentora uses a dual-context mentorship concept:
- Expert Context: public field knowledge, private insights, heuristics, and judgment style
- User Context: academic stage, goals, constraints, and current challenges
These are combined to generate more personalized and strategically useful guidance.
- Knowledge supply from mentors
- User profile mapping
- Mentor selection
- Tailored output based on mentor perspective and user context
Mentora reflects an important idea: informed direction is often more valuable than raw information. The prototype explores how AI systems can support high-stakes personal and career decisions through context-aware, mentor-guided interaction.
Team Hali4X:
- A Lin
- Andy
- Evan
I contributed as a member of the hackathon team in concept development, product framing, and project presentation. I participated in shaping the mentorship workflow, user-facing demo narrative, and overall positioning of the system as an AI-guided mentorship prototype.
- Next.js
- Tailwind CSS
- Vercel
This project is a hackathon prototype and demonstration system. It is not a production-ready platform and does not include a full backend or deployed AI inference pipeline.