Experimental
AI-Assisted Research Validation
Testing where software assistance improves review and where it introduces hidden risk.
- Status
- Experimental
- Version
- Experimental
- Date
- 2026
- Authors
- Aryan Patel
Abstract
An experimental program testing whether AI-assisted software roles can support citation checks, code review, sensitivity work, and critique inside a reproducible workflow. The software is not treated as an independent researcher.
Research question
Where is AI assistance reliable inside a research workflow, and where must human review intervene?
Methods
- Citation resolution checks
- Code and output comparison
- Automated critique
- Sensitivity review
- Human-attested release gates
Data
Internal research workflows, logs, and preserved artifacts.
Results
Experimental. Conclusions are not finalized.
Limitations
Findings depend on the specific models, prompts, software roles, and research tasks examined.
- Code availability
- Internal experimental system.
- Data availability
- Internal workflow artifacts.
- AI disclosure
- AI-assisted software supported literature retrieval, code generation, analysis, critique, and drafting. Deterministic systems produced and checked estimates. A human researcher retained responsibility for the question, design, interpretation, and release.
- Reproduction status
- Experimental validation.