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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
123862788
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Connie Hsueh (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Worker Satisfaction: 1 paper
- Developer Productivity: 1 paper
- Governance And Regulation: 1 paper
- Other: 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 |
|---|---|---|---|
| Forcing workers to use generative AI together reduced both document output and average quality, while a short training that framed AI as a thought partner improved quality among the best performers; but session timing imbalances, dropout and automated grading quirks mean the results should be interpreted cautiously.arxiv | Connie Hsueh provider id |
2026-04-09 | 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.