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
22Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2290790886
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Li Liu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Training Effectiveness: 1 paper
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| A comprehensive survey finds that the promise of large foundation models for human-AI collaboration depends less on model scale and more on human-centered design, preference-driven objective shaping, and governance; the literature is expanding quickly but remains non-systematic and highlights open challenges in bias, evaluation, and socio-economic effects.openalex | Li Liu provider id |
2026-08-22 | 21 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 21 cumulative citations. This is a coverage summary, not an author score or h-index.