PBL Project-based Training Model
Using Xinwei's distinctive PBL project-pull teaching model and AI agent project-management platform, we make the project process visible: real industry problems × iterative design thinking × process transparency × human–AI collaboration.
From week one, students engage a concrete engineering brief and build knowledge by solving real problems. Theory courses (math/algorithms) run in parallel or follow need—when students encounter gaps while working on autonomous driving perception, the agent platform pushes micro-courses instantly.
Project Operations
Project Intake Process
Enterprise need → technical review → task breakdown → difficulty & timeline assessment → publish to project pool
Project Selection
Students select projects each term by interest and capability fit; core (L3) projects require mentor interviews; cross-college teams are encouraged for system-level challenges.
Triple Mentorship System
Every student is paired with an academic mentor, an industry mentor, and an AI mentor—forming a dedicated practice mentor group (one group per 3–5 students) for full-journey support.
Academic Mentor
Full-time faculty from leading universities—theory frameworks, methodology, and academic standards. Guide research, thesis writing, and patent filings.
Industry Mentor
Senior engineers from Geely Research Institute—engineering constraints, industry standards, and production feasibility. Share hands-on skills and mass-production experience.
AI Mentor
An AI agent independently developed by Xinwei Technology, providing 24/7 knowledge Q&A, simulation guidance, personalized training recommendations, and competency assessment
Mentor Role Progression
Mentor roles evolve with student growth—from hands-on coaching to "you lead, we assure":
| Stage | Role | Core Focus |
|---|---|---|
| Years 1–2 | Coach | Academic initiation—build foundations and habits |
| Years 2–3 | Integrator | Cross-domain integrator—bridge academia and practice |
| Years 3–4 | Facilitator | Growth enabler—guide independent problem-solving |
| Year 4 | Master | Steward and gatekeeper—students fully lead projects |
Dual-Track Competency–Credit System & Badges
A dual-track "competency–credit" evaluation—one diploma, two credentials. The national track ensures recognition of the Bachelor of Engineering degree; the competency track builds a digital competency portfolio.
Five Competency Badges (Required for Graduation)
Problem Definition
Identify core problems from ambiguous requirements
Technical Delivery
Code/models adopted by real systems
Team Collaboration
Serve as team lead for at least one term
Iterative Resilience
Document failure adjustments and reflective evidence
Ethical Reasoning
Recognize social impacts of technical solutions
Composite GPA
Composite GPA = Course GPA × 40% + Competency Development GPA × 60%
Competency Development GPA is weighted by unit levels: Foundational = 2.0, Proficient = 3.0, Mastery = 4.0
Graduation transcripts show both traditional course grades and a competency-dimension radar chart.
Digital Competency Portfolio
Upon graduation, each student receives a shareable encrypted link including: competency radar chart, badges and levels, project portfolio (code, design docs, demo videos), industry mentor comments, and expert review feedback.
Practice Teaching System
A stepped practice chain—basic labs, industry awareness internships, specialization modules, joint university–enterprise projects, and graduation design. Practice courses total 29 credits; combined with project-based practice, overall practice accounts for no less than 70%.
Project Implementation Flow
1. Project Initiation
Industry mentors publish project lists; students form teams and select topics; three mentors jointly approve initiation—goals, technical metrics, timeline, and roles.
2. Solution Design
Academic mentors guide theoretical design; industry mentors guide engineering design; AI mentors assist simulation validation—producing a complete project design document.
3. Development & Execution
Student teams develop, debug, and test with continuous triple-mentor support: academics on theory, industry on engineering, AI for real-time assistance.
4. Midterm Review
Three mentors jointly review progress, technical challenges, and outcome quality, and propose improvements.
5. Project Acceptance
Deliver outcomes (algorithm models, software systems, test reports, functional prototypes, etc.); three mentors and industry experts jointly accept.