Future Training Center — Build-out Solution
The Future Training Center is a new type of practical-training infrastructure framework proposed in the national "AI + Education" Action Plan and elaborated at the World Digital Education Conference, officially defined as "task-driven, data-bonded, intelligence-powered". This solution takes that framework as its master plan, integrating a data foundation & assetization layer, whole-facility space build-out, training demonstration, student training & assessment and teacher research & training as five pillars, and offers a replicable going-global blueprint for the Belt and Road — a systematic blueprint for institutions moving from "building rooms" to "operating assets" (one of several approaches).
Five Pillars & Outbound Dimension
Layer 0 · Data Foundation
Training Skill Data Asset Solution
Whole-facility Space
Smart Lab & Training Space Solution
Visible
Vocational Training Demonstration Solution
Accurately Assessed
Student Training & Assessment Solution
Remotely Connected
Teacher Research & Training Solution
Cross-industry Independent Line
Clinical Teaching Solution
Key points
- An integrated training-skill data-asset operating system that is task-driven, data-bonded and intelligence-powered — built on the national "Future Training Center" framework, it turns every training session, every skill profile and every device operation into school-owned, governable, valuatable and audit-ready skill data assets, and goes global together with Chinese vocational education.
Scenario pain points
- Training bases are built piecemeal "one room, one batch of equipment", with mixed brands and disconnected data
- Process data from training is scattered across systems and hard to consolidate into reusable, valuatable school assets
- Construction lacks a top-level framework; phased investment easily leads to rebuilding, and managers struggle to see the overall outcome
- Vocational equipment "going out" lacks a standard model — outbound sales are mostly one-off instead of systematic output
System architecture
Layer 0 · Data foundation & assetization
Compliant collection → skill rights confirmation → governance value-add → asset readiness (auditable / valuatable / audit-ready for capitalization)
Scenario 1 · Whole-facility space
Smart lab & training space: spatial computing + IoT control + unified data dashboard
Scenario 2 · Training demonstration
Visible: 4K close-up + multi-view scheduling, eliminating围观-style teaching
Scenario 3 · Student Training & Assessment
Accurately assessed: workstation-level full traceability, peer review and AI-assisted scoring, aligned with vocational college-entrance and provincial skill examinations
Scenario 4 · Teacher research & training
Remotely connected: 5G+AI cross-campus teaching research and classroom diagnosis
Strategic dimension · Vocational education going global
Going global with Chinese vocational education, providing a replicable training-center model
Value outcomes
Framed
Aligned with the national Future Training Center definition; construction stays on track
Founded
Data belongs to the school and is assetizable, building long-term assets rather than sunk costs
Phased
Phased by professional cluster, each phase keeps a standard interface for smooth expansion
Exportable
Replicable model serving Belt and Road vocational-education cooperation
Solution details
I. Why now
The national "AI + Education" Action Plan first named "build Future Training Centers"; the World Digital Education Conference further elaborated it as a new type of practical-training infrastructure that is "task-driven, data-bonded, intelligence-powered". A dozen-plus ministries are laying out Future Training Centers across twenty fields — smart manufacturing, new-energy vehicles and others — during the 15th Five-Year Plan, and Guangdong has proposed building around two hundred high-level training bases. The policy window is open: institutions are shifting from "buying equipment" to "building frameworks and consolidating assets".
II. Three real problems facing training centers
First, disconnected data: training rooms are built separately; equipment, courses, students and assessment data are isolated. Second, assets do not consolidate: process skill data is lost as students graduate, unable to form reusable school assets. Third, no outbound model: vocational equipment going out is mostly one-off sales, lacking a replicable, systematic training-center model.
III. The four-layer build-out framework
Layer 0 · Data foundation & assetization: training operation traces, assessment, device operation and teacher research are consolidated through compliant collection, skill rights confirmation and governance value-add into school-auditable, valuatable, audit-ready skill data assets (readiness capability, not capitalization on the institution's behalf).
Four scenario pillars: whole-facility space build-out (smart lab & training space), training demonstration (visible), student training & assessment (accurately assessed), teacher research & training (remotely connected) — all running on the data foundation.
Strategic dimension · Vocational education going global: using mature products such as the YiShiJiao smart assistant and smart touch all-in-one/TV as samples, replicate the Future Training Center model to Belt and Road partner institutions.
IV. Three-phase phased delivery
Phase 1 (pilot): 1–2 core professional training rooms, running the minimum closed loop of collection–scheduling–resource platform and data foundation; Phase 2 (replication): expand to the professional cluster, connect IoT control and a unified data dashboard, and bring the four scenario pillars online; Phase 3 (outbound / value-add): cross-campus linkage, AI value-add capabilities, and outbound sample output.
V. One-to-one mapping with policy
"Task-driven" maps to real training tasks and skill assessment; "data-bonded" maps to the Layer-0 data foundation and skill profiles; "intelligence-powered" maps to AI classroom diagnosis, AI-assisted scoring and intelligent scheduling. The data foundation echoes the "national data resource repository for key fields such as education" proposed in the Overall Layout Plan for Building a Digital China and the data-element strategy, and connects to the Ministry of Education's vocational-education digitalization (digital credential archive / vocational big data) direction.
VI. Vocational education going global · Serving the Belt and Road
The Future Training Center is more than one school — it is the soft infrastructure for Chinese vocational education to "go out". Starting from the deployment of smart assistants and smart touch all-in-one/TV in markets such as Vietnam, we export the "framework + foundation + scenarios" as a whole, helping partner institutions build localized Future Training Centers and serving Belt and Road talent co-cultivation.
FAQs
What is the "Future Training Center", and how is it different from a single training-room solution?
The Future Training Center is a new type of practical-training infrastructure framework proposed in the national "AI + Education" Action Plan and elaborated at the World Digital Education Conference, officially defined as "task-driven, data-bonded, intelligence-powered". Unlike a single training-room solution that solves local problems of "visibility and accurate assessment", the Future Training Center is a whole-facility framework — using a data foundation to connect space, demonstration, examination and research, and oriented to outbound delivery. This solution is the master plan of that framework; its five pillars are sub-solutions that can be initiated independently or delivered as a whole.
How is your data foundation different from "classroom / teacher evaluation data"?
Our data object is training skill process data — operation traces, assessment, device operation and teacher research — ultimately consolidated into skill profiles and digital credential archives, serving skill assessment, vocational big data and talent co-cultivation; rather than classroom-evaluation data whose buying point is supervisor听课 and teacher assessment. The two differ in object and purpose; we focus on the vocational core of "skill" and "credential".
Can the data foundation help institutions with "data-asset capitalization"?
We do not replace the institution's financial capitalization act. We provide a data-asset readiness capability: compliant collection, skill rights confirmation and governance value-add, making the institution's data "ready" — auditable, valuatable and audit-ready for capitalization. Whether and how to capitalize is decided by the institution itself under instruments such as the Ministry of Finance's Interim Provisions on Accounting Treatment of Enterprise Data Resources; we provide support rather than overstepping.
Get in touch
Tell us about your training rooms and teaching goals — our team answers in English and Chinese.