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
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2372252202
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Anmol Singhal (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| AI-assisted script generation and live topic tracking reshape requirements interviews: untrained interviewers using the workflow produced substantially higher-quality scripts (92.8 vs 74.8) and elicited more low-level requirements while focusing on fewer topics but asking more follow-ups per topic.arxiv | Anmol Singhal provider id |
2026-08-03 | 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.