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
11Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2402842826
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Haiyang Shen (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| A student-built benchmark exposes large gaps in deep research AIs: across 256 humanities and social-science questions the average pass rate is under 17%, with the leading system (GPT-5.5) clearing 57.6%; building such tests in class helps students learn to judge machine-produced knowledge.arxiv | Haiyang Shen provider id |
2026-05-20 | 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.