Training Skill Data Asset Solution
The training data foundation solution aligned with the "data-bonded" positioning of the national "Future Training Center" — an institution- and medical-oriented overall training skill data-asset solution: with JingXun (peer-assisted learning) at the core, together with the YiShiJiao smart assistant, smart lab & training space and 5G private network, it realizes ambient collection, structuring and assetization of training skill data, supporting process-based assessment, skill examination and teaching-research improvement, so that data belongs to the school and consolidates into the school's long-term asset.
Key points
- As Layer 0 data foundation of the "Future Training Center Build-out Solution", it turns every operation in training into collectable, structurable and assetizable skill data, giving the school its own training data foundation.
Scenario pain points
- No record of training operations; assessment relies on impression and review lacks basis
- Students' skill levels are hard to quantify; individual weaknesses are unclear
- Training data is scattered across systems and hard to consolidate into school assets
- Teaching-research improvement lacks data support; experience is hard to replicate
System architecture
Ambient collection layer
JingXun hardware terminals + YiShiJiao carts, workstation-level collection of operation video, peer-review records and time spent
Structuring layer
Prism engine decomposes scoring, recording and time into six-dimension capability tags, forming a structured profile
Assetization layer
Individual reports, class dashboards and growth curves consolidate into reusable training skill data assets
Application layer
Process-based assessment, skill examination, teaching-research improvement, resource co-build
Value outcomes
Complete capture
Ambient collection covers every operation; no practice is missed
Clear structuring
Six-dimension capability tags turn vague "feel" into comparable data
Usable
Reports, dashboards and curves directly serve assessment, examination and research
Retainable
Data belongs to the school, locally deployable, consolidating into long-term assets
FAQs
What is the relationship between this solution and the "Future Training Center"?
The "Future Training Center" is the new infrastructure framework of national vocational-education reform, officially defined as "task-driven, data-bonded, intelligence-powered". The JingXun training skill data asset solution aligns with its "data-bonded" positioning — consolidating training process data into a reusable school data foundation serving process assessment, skill examination and teaching-research improvement; JingXun is the solution's core collection and profiling engine.
What is the relationship between the training skill data asset solution and JingXun?
JingXun is the solution's core product (collection and profiling engine). The solution also integrates the YiShiJiao smart assistant demonstration cart, the smart lab & training space and the 5G private network, expanding single-point assessment into the full chain of collection–structuring–assetization–value-add, with data clearly belonging to the school.
Does the solution support integration with the school's existing platforms?
Yes. JingXun and the solution's products provide standard interfaces that can integrate with the school's existing resource platforms and academic systems, or be deployed independently.
Are the AI value-add capabilities available now?
The first three stages of the four-stage chain (ambient collection, structuring, assetization) are deliverable in Phase 1; the fourth "value-add" stage's AI-assisted scoring (MingJing) is a Phase-2 planned capability, scheduled for early 2027 release. Data-asset rights confirmation and capitalization support are planned (Phase 2); currently the focus is on process data collection and asset consolidation.
Get in touch
Tell us about your training rooms and teaching goals — our team answers in English and Chinese.