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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
3069162
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Sreecharan Sankaranarayanan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Other: 1 paper
- Skill Acquisition: 1 paper
- Error Rate: 1 paper
- Training Effectiveness: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| AI assistants let novice programmers ship working code but can erode maintainability: unrestricted LLM use doubled functional success yet produced a 77% failure rate on later maintenance versus 39% for a scaffolded teach-back interface, suggesting enforced explanation preserves corrective competence.arxiv | Sreecharan Sankaranarayanan provider id |
2026-02-22 | 5 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 5 cumulative citations. This is a coverage summary, not an author score or h-index.