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
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1932628
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Moritz Sudhof (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Error Rate: 1 paper
- Adoption Rate: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Skill Acquisition: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Fluent users drive AI to do harder, more ambitious work by iterating and critiquing outputs — they fail more often but these failures are visible and recoverable, yielding higher success on complex tasks, while novices more frequently encounter undetected, silent failures.openalex | Moritz Sudhof provider id |
2026-04-28 | 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.