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 →
2Distinct papers
6Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2256674786
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Dan Jurafsky (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 2 papers
- Human Ai Collab: 2 papers
- Productivity: 2 papers
Claim outcomes
- Task Completion Time: 2 papers
- Other: 2 papers
- Adoption Rate: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Large experiment finds a 'speedup illusion': users expect LLMs to be faster but actual completion times on simple tasks are unchanged, even as subjective effort falls; the bias is specific to AI and not seen when imagining human help.arxiv | Dan Jurafsky provider id |
2026-05-22 | 0 |
| Users routinely lean on AI for trivial tasks that it does not meaningfully speed up, while underreporting how often they use it and overestimating time savings; prior exposure further entrenches reliance, risking an inefficient overreliance feedback loop.arxiv | Dan Jurafsky provider id |
2026-05-21 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 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.