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:
2290532078
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ming Gu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Decision Quality: 1 paper
- Skill Acquisition: 1 paper
- Ai Safety And Ethics: 1 paper
- Inequality: 1 paper
- Market Structure: 1 paper
- Output Quality: 1 paper
- Training Effectiveness: 1 paper
- Wages: 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 |
|---|---|---|---|
| Student access to AI is undermining traditional standardized tests, turning assessment into a policy problem that mixes education and economics. Nations should pivot to formative and performance‑based evaluations and impose data, training and audit standards to prevent widening inequality and commercial capture of student data.openalex | Ming Gu provider id |
2026-03-10 | 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.