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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2254874197
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Liang He (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| Explainable AI helps teachers perform better in the moment under concurrent assistance but erodes longer-term trust; an RCT of 120 pre-service teachers finds explanations boost immediate execution only in concurrent setups and do not transfer to independent tasks, while paradoxically suppressing accumulated trust.semantic_scholar | Liang He provider id |
Fetched 2026-04-11 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.