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
2Unique collaborators
1/1OpenAlex citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5071294124
ORCID evidence
Observed aliases (1)
- Ming Yin (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Automation Exposure: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| An AI that challenges users’ reasoning improves judgment: when the system generates counterfactual critiques of human rationales, people rely less on the AI and make more accurate house-price predictions, though they report higher cognitive effort; the gains are strongest for participants comfortable with AI.semantic_scholar | Ming Yin orcid |
Fetched 2026-06-06 | 2 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.