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:
17344806
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Maram Assi (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Developer Productivity: 1 paper
- Output Quality: 1 paper
- Task Completion Time: 1 paper
- Team Performance: 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 |
|---|---|---|---|
| LLM coding assistants speed up developers and cut routine work, but their effect on code quality and teamwork remains unresolved; most studies are short-term and exploratory, leaving long-run and team-level impacts unclear.openalex | Maram Assi provider id |
2026-04-27 | 14 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 14 cumulative citations. This is a coverage summary, not an author score or h-index.