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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1854421836
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Keziah Naggita (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Skill Acquisition: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| Designing AI interactions around reachable targets and actionable counterfactuals can steer people toward genuine improvement while limiting gaming, and algorithmic constraints enable trade-offs between incentives and classifier accuracy; these claims are supported by formal guarantees, dataset experiments, and a small online experiment of parental responses to device mistreatment.arxiv | Keziah Naggita provider id |
2026-08-06 | 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.