01⏱ 25–40 min
Start without jargon
Understand the big changes without coding.
For : Curious readers
Learning objective Distinguish WebAI’s creation, discovery and action dimensions, then learn to trace answers to evidence.
Topics to explore
- What is WebAI? ↗
- Understanding the Generative Web ↗
- RAG: retrieval-augmented generation ↗
- SEO, AEO and GEO: key differences ↗
View five progressive activities
- Read two pages on the same topic, one authored and one generated.
- Identify three verifiable claims and their sources.
- Locate date, author and status metadata.
- Use a lab without confusing simulation with live AI.
- Write a 200-word summary stating uncertainties.
Expected artifact : A reading worksheet comparing facts, interpretations and unsupported claims.
Success criterion : Beginners can identify primary evidence, explain agent roles and avoid unsupported conclusions.
Reference for this path ↗ Continue to practical workshops ↗ 02⏱ 45–60 min
Teach the AI-driven Web
Compare content, sources and mechanisms in class.
For : Educators
Learning objective Design a learning activity that assesses concept understanding instead of rewarding fluent generated text alone.
Topics to explore
- What is WebAI? ↗
- RAG: retrieval-augmented generation ↗
- C2PA and content provenance ↗
- Accessibility and AI: design for everyone ↗
View five progressive activities
- Choose course-aligned sources and tasks.
- Compare two contradictory generated answers.
- Require justification using original evidence.
- Explain generation limitations and media provenance.
- Assess evidence-led reasoning with a transparent rubric.
Expected artifact : A lesson sequence, assessment rubric and source pack for learners.
Success criterion : Learners explain whether a citation supports a claim and distinguish synthesis from evidence.
Reference for this path ↗ Continue to practical workshops ↗ 03⏱ 60–90 min
Build responsible experiences
Design an experience and verify its impact.
For : Creators and designers
Learning objective Build useful, accessible and evidenced generative Web experiences with review before publication.
Topics to explore
- Understanding the Generative Web ↗
- Accessibility and AI: design for everyone ↗
- WebXR and generative experiences ↗
- Prompt injection and agent security ↗
View five progressive activities
- Define genuine needs and user personas.
- Choose HTML structure and UI without mandatory AI dependence.
- Prototype with sourced content.
- Test contrast, keyboard, performance and compatibility.
- Document licenses, attribution and revisions.
Expected artifact : An accessible prototype with sources, fallback and a testing report.
Success criterion : Visitors reach the information without a model and generated content remains clearly identified.
Reference for this path ↗ Continue to practical workshops ↗ 04⏱ 90–120 min
Build an agentic Web
Explore protocols and tool control.
For : Developers
Learning objective Orchestrate tools and agents while separating capability descriptions, execution permissions and actual outcomes.
Topics to explore
- RAG: retrieval-augmented generation ↗
- Understanding MCP ↗
- Understanding A2A ↗
- WebMCP: an interface for Web agents ↗
- Prompt injection and agent security ↗
View five progressive activities
- Create a side-effect-free retrieval tool.
- Document its arguments using JSON Schema.
- Compare the roles of MCP, A2A and WebMCP.
- Implement confirmation before consequential operations.
- Test untrusted content and inspect traces.
Expected artifact : A documented tool schema, permission diagram and reproducible execution checks.
Success criterion : The tool rejects unauthorized operations and reports observed outcomes without claiming nonexistent connections.
Reference for this path ↗ Continue to practical workshops ↗ 05⏱ 60–90 min
Evaluate models and agents
Move from demos to reproducible evidence.
For : Researchers and analysts
Learning objective Build research evaluations separating retrieval, answer faithfulness, agent task success and security.
Topics to explore
- From Transformers to multimodal models ↗
- Evaluating agents and their limits ↗
- Knowledge graphs and JSON-LD ↗
- C2PA and content provenance ↗
View five progressive activities
- Define research questions, hypotheses and metrics.
- Build a dataset with known rights.
- Include negative and contradictory cases.
- Measure components, uncertainty and failures.
- Publish methods, versions and limitations.
Expected artifact : A dated evaluation protocol, detailed results and failure analysis.
Success criterion : Results are reproducible with limitations rather than broad superiority claims.
Reference for this path ↗ Continue to practical workshops ↗ 06⏱ 50–75 min
Deploy with care
Balance practical value, risk and documentation.
For : Organizations
Learning objective Deploy AI applications with risk governance, documentary controls and human oversight.
Topics to explore
- RAG: retrieval-augmented generation ↗
- Prompt injection and agent security ↗
- C2PA and content provenance ↗
- Accessibility and AI: design for everyone ↗
View five progressive activities
- Identify use cases and accountable roles.
- Map data and tool permissions.
- Evaluate privacy, security and accessibility.
- Define metrics and approval gates.
- Set up monitoring and correction processes.
Expected artifact : A risk register, permission matrix and review process.
Success criterion : The team can explain decisions, suspend automation and correct failures.
Reference for this path ↗ Continue to practical workshops ↗ Nothing matched. Try another term or reset filters.