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
4Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
6111882
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Nattavudh Powdthavee (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 2 papers
- Human Ai Collab: 2 papers
- Adoption: 1 paper
Claim outcomes
- Decision Quality: 2 papers
- Governance And Regulation: 1 paper
- Consumer Welfare: 1 paper
- Other: 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 language models reliably flag fraudulent investment opportunities and resist motivated persuasion more than lay humans; in controlled tests humans endorsed fraud ~13–14% of the time while tested LLMs never did, and motivated framing did not suppress — and slightly increased — AI warnings.arxiv | Nattavudh Powdthavee provider id |
2026-04-22 | 0 |
| AI-written 'letters from the future' raise empathy but don't open wallets or change policy views: a large online experiment finds no impact on climate-policy support or donations despite higher concern for future generations.arxiv | Nattavudh Powdthavee provider id |
2026-02-10 | 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.