# AI PhD

**Canonical:** https://apis.io/providers/ai-phd-com/  
**Website:** https://ai-phd.com/  
**APIs profiled:** 1

AI PhD offers AI education through a series of beginner-friendly courses and learning paths. It provides live AI‑instructed lessons, a single‑payment model, and a growing catalog of topics covering prompt engineering, AI agents, and automation. The platform aims to make AI accessible without subscriptions, delivering up‑to‑date content and hands‑on projects for learners at any level.

## Kin Score — 10.4 / 100 (minimal)

Scored 2026-11-06 under rubric 0.27.0. Trend: flat (-3.7 from 14.1).

| Facet | Score |
|---|---|
| Discoverability | 57.1 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 18.4 |
| Developer Ergonomics | 0.0 |
| Access Clarity | 10.5 |

Regulatory layer — **Education & Research**: 20.0 (matched via tags).

## Agent readiness — 18.2 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | documented |
| Auth Clarity | served |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |
| Delegated Identity | served |
| Protected Resource Metadata | no |
| Dynamic Client Registration | yes |
| Agentic Commerce | no |
| Pagination Ratio | na |
| Operation Distinctiveness | na |
| MCP Tool Quality | na |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## APIs (1)

- **AI PhD API** — API documentation is not publicly machine‑readable; the provider offers courses via website.

## Security (2)

- **Ai Phd Com Domain Security** — TLSv1.3 · HSTS · DNSSEC · DMARC
- **Ai Phd Com Vulnerability Disclosure** — security.txt · contact published

## Tags

Education, Artificial Intelligence, Courses, Learning, Platform

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/ai-phd-com/). Scores are computed from the provider's own public artifacts under a published rubric.