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:
2442460804
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kwan Hong Tan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Worker Satisfaction: 1 paper
- Automation Exposure: 1 paper
- Consumer Welfare: 1 paper
- Decision Quality: 1 paper
- Market Structure: 1 paper
- Organizational Efficiency: 1 paper
- Skill Acquisition: 1 paper
- Skill Obsolescence: 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 |
|---|---|---|---|
| AI often raises short‑term wellbeing and convenience but can, through repeated use, erode skills, autonomy and social ties — producing long‑run welfare losses; the authors formalize this two‑horizon risk (DCDT) and propose an AI‑Happiness Impact Assessment to spot and prevent reversals.openalex | Kwan Hong Tan provider id |
2026-08-13 | 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.