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 dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2404076087
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Soumya Paul (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
- Ai Safety And Ethics: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Job Displacement: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Skill Obsolescence: 1 paper
- Task Allocation: 1 paper
- Training Effectiveness: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| Employees report that human–AI collaboration improves efficiency and decision-making, but fears over job displacement, skills and ethics persist; effective implementation, training and transparent communication appear to reduce resistance.semantic_scholar | Soumya Paul provider id |
Fetched 2026-05-18 | 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.