Subtitle:
Understanding how the Internet is evolving from a network of webpages into a network of knowledge, AI agents, and machine-understandable systems.

Level: Beginner → Research Engineer → Internet Architect

Duration: 12 months

Goal

Master the technologies, standards, and architectural principles shaping the next generation of the Web, where humans, AI systems, search engines, robots, and autonomous agents all interact with structured knowledge rather than only documents.

Semester I — Evolution of the Web

Module 1. History of the Internet

Study

• ARPANET

• TCP/IP

• DNS

• HTTP

• HTML

• URLs

• browsers

• search engines

• APIs

• cloud computing

Understand how today's Internet was built.

Module 2. Web 1.0 → Web 5.0

Compare

• Web 1.0 (documents)

• Web 2.0 (social platforms)

• Web 3.0 (semantic and decentralized technologies)

• Web 4.0 (AI-assisted services)

• AI-Native Web

• Agentic Web

Study what changed at each stage.

Module 3. Modern Web Architecture

Learn

• frontend

• backend

• APIs

• microservices

• edge computing

• CDNs

• serverless

• event-driven systems

Project

Design the architecture of a modern AI-first website.

Module 4. Open Web Standards

Study

• HTML5

• CSS

• JavaScript

• WebAssembly

• HTTP/2

• HTTP/3

• QUIC

• WebSockets

• WebRTC

Semester II — Semantic and Machine-Readable Web

Module 5. The Semantic Web

Study

• RDF

• RDF Schema

• OWL

• SKOS

• SHACL

• Linked Data

Understand why future systems exchange knowledge instead of only pages.

Module 6. Entities and Knowledge

Learn

• entities

• identifiers

• ontologies

• taxonomies

• knowledge graphs

• linked entities

• canonical concepts

Project

Create a knowledge graph for an organization.

Module 7. Machine-Readable Websites

Study

• Schema.org

• JSON-LD

• structured metadata

• Open Graph

• RSS

• XML sitemaps

• robots.txt

• semantic HTML

Build pages optimized for humans, search engines, and AI.

Module 8. Search in the AI Era

Compare

• keyword search

• semantic search

• vector search

• GraphRAG

• hybrid retrieval

• conversational search

• agent-assisted search

Understand how ranking is changing.

Semester III — AI-Native Web

Module 9. AI-Friendly Content

Study

• semantic density

• atomic content

• modular documentation

• canonical knowledge

• structured writing

• reusable information

Learn to publish information that AI can interpret reliably.

Module 10. AI Agents on the Web

Topics

• autonomous agents

• browser agents

• planning

• memory

• tool use

• workflows

• autonomous navigation

Project

Build an agent capable of completing web tasks.

Module 11. APIs and Knowledge Services

Study

• REST

• GraphQL

• gRPC

• OpenAPI

• Model Context Protocol (MCP)

• webhooks

• authentication

Design services that can be consumed by humans and AI.

Module 12. Vector and Graph Infrastructure

Study

• vector databases

• knowledge graphs

• embeddings

• retrieval pipelines

• semantic indexing

• metadata stores

Project

Create a hybrid search platform using graphs and vectors.

Semester IV — The Future Internet

Module 13. AI Search Ecosystem

Research

• AI assistants

• conversational interfaces

• answer engines

• retrieval-augmented generation

• citation systems

• trust and verification

Understand how discovery evolves beyond traditional search engines.

Module 14. Personal Knowledge Infrastructure

Learn to build

• personal knowledge graphs

• digital memory

• semantic notebooks

• lifelong learning systems

• AI workspaces

Module 15. Open Knowledge Networks

Study

• federated knowledge

• interoperability

• decentralized identity

• open standards

• collaborative knowledge ecosystems

Design systems that exchange structured knowledge across organizations.

Module 16. Internet 2035+

Explore emerging directions.

• AI-first websites

• autonomous digital organizations

• machine-to-machine communication

• multimodal web

• digital twins

• programmable knowledge

• self-maintaining documentation

• continuous knowledge synchronization

Programming Skills

• HTML

• CSS

• JavaScript

• TypeScript

• Python

• SQL

• Cypher

• SPARQL

• GraphQL

Platforms and Technologies

• Git

• GitHub

• Docker

• Kubernetes

• Cloud platforms

• Neo4j

• Qdrant

• Elasticsearch

• PostgreSQL

• Redis

Standards

• HTTP

• HTTPS

• URI

• JSON

• JSON-LD

• RDF

• XML

• YAML

• OpenAPI

• Schema.org

Research Topics

• Semantic Web

• Linked Data

• Knowledge Graphs

• AI-Native Content

• Vector Search

• GraphRAG

• Agentic Web

• Human–AI Interaction

• Information Architecture

• Digital Knowledge Systems

Final Capstone

Design and deploy a complete AI-Native Web Platform featuring:

• a semantically structured website

• machine-readable metadata

• knowledge graph integration

• vector search

• AI agent interfaces

• hybrid retrieval (keyword + graph + vector)

• public APIs

• documentation optimized for humans and AI

• continuous synchronization of structured knowledge

• governance, versioning, and provenance tracking

Competencies after graduation

By completing this program, you will be able to:

• Design websites that are understandable by both people and AI systems.

• Build semantic knowledge infrastructures rather than collections of disconnected pages.

• Integrate knowledge graphs, vector databases, and AI retrieval into modern web applications.

• Create AI-native documentation with high semantic density and machine readability.

• Engineer platforms prepared for autonomous agents, conversational search, and future Internet architectures where structured knowledge is a first-class citizen