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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2282301642
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Angel Mary John (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
- Governance And Regulation: 1 paper
- Ai Safety And Ethics: 1 paper
- Error Rate: 1 paper
- Job Displacement: 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 |
|---|---|---|---|
| Consumer legal AIs confidently get Indian contract law wrong: a 60-case audit finds large high-confidence error rates (Meta AI 31.7%, Perplexity 15.0%, ChatGPT 6.7%) especially on post-2018 Specific Relief issues, while 71% of law students report no formal training in ethical AI use, leaving courts and practitioners exposed to hallucinated citations.arxiv | Angel Mary John provider id |
2026-08-21 | 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.