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
3Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5144445531
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Ayoub KHODAR (openalex, provider refresh)
- Ayoub KHODAR (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Other: 1 paper
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Regulatory Compliance: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Academic interest in AI for Islamic finance has surged since 2020, yet no published study to 2025 applies large language models, RAG, or the AAOIFI standards as AI knowledge-bases — exposing a clear research and practical-compliance gap.openalex | Ayoub KHODAR provider id |
2026-08-05 | 0 |
Citation observation summary
OpenAlex 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.