Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2213597440
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Liyao Cui (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Organizational Efficiency: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| AI activity lifts demand for China’s education and training market primarily in bullish conditions, while policy uncertainty tends to depress it during calmer periods. Risk spillovers from AI to training are larger and more volatile than those from policy uncertainty, implying policymakers should balance AI-driven opportunity with market stability.openalex | Liyao Cui provider id |
2026-05-25 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.