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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5037800601
ORCID evidence
Observed aliases (4)
- Yuan-Ling Chen (openalex, provider refresh)
- Yuan-Ling Chen (openalex, source metadata)
- Yuan‐Ling Chen (openalex, provider refresh)
- Yuan‐Ling Chen (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Skill Acquisition: 1 paper
- Training Effectiveness: 1 paper
- Firm Productivity: 1 paper
- Inequality: 1 paper
- Other: 1 paper
- Task Allocation: 1 paper
- Team Performance: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Employee‑led 'need crafting' offers a practical way to make reskilling for AI more effective by aligning learning with workers' motivations and business priorities; the idea is theory‑grounded but awaits firm‑level causal tests to confirm productivity and distributional effects.openalex | Yuan-Ling Chen provider id |
2026-08-31 | 0 |
Citation observation summary
OpenAlex 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.