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
3Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5124747827
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Carluys Suescum Coelho (openalex, provider refresh)
- Carluys Suescum Coelho (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Research Productivity: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| When paired with clear rubrics and teacher oversight, generative-AI feedback measurably improves students' coherence and argumentation and can narrow gaps between native and non-native speakers; however, the gains depend on model version, classroom context and require traceable governance to avoid bias and dependency.openalex | Carluys Suescum Coelho provider id |
2026-01-01 | 0 |
Citation observation summary
OpenAlex 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.