WebAI/Research and exploration pathways
WEBAI KNOWLEDGE ATLAS · EDITORIAL PREVIEW

Research and exploration pathways▦

Structured routes to explore primary sources, compare tools and review architectures by use case.

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

  1. What is WebAI? ↗
  2. Understanding the Generative Web ↗
  3. RAG: retrieval-augmented generation ↗
  4. SEO, AEO and GEO: key differences ↗
View five progressive activities
  1. Read two pages on the same topic, one authored and one generated.
  2. Identify three verifiable claims and their sources.
  3. Locate date, author and status metadata.
  4. Use a lab without confusing simulation with live AI.
  5. 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

  1. What is WebAI? ↗
  2. RAG: retrieval-augmented generation ↗
  3. C2PA and content provenance ↗
  4. Accessibility and AI: design for everyone ↗
View five progressive activities
  1. Choose course-aligned sources and tasks.
  2. Compare two contradictory generated answers.
  3. Require justification using original evidence.
  4. Explain generation limitations and media provenance.
  5. 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

  1. Understanding the Generative Web ↗
  2. Accessibility and AI: design for everyone ↗
  3. WebXR and generative experiences ↗
  4. Prompt injection and agent security ↗
View five progressive activities
  1. Define genuine needs and user personas.
  2. Choose HTML structure and UI without mandatory AI dependence.
  3. Prototype with sourced content.
  4. Test contrast, keyboard, performance and compatibility.
  5. 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

  1. RAG: retrieval-augmented generation ↗
  2. Understanding MCP ↗
  3. Understanding A2A ↗
  4. WebMCP: an interface for Web agents ↗
  5. Prompt injection and agent security ↗
View five progressive activities
  1. Create a side-effect-free retrieval tool.
  2. Document its arguments using JSON Schema.
  3. Compare the roles of MCP, A2A and WebMCP.
  4. Implement confirmation before consequential operations.
  5. 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

  1. From Transformers to multimodal models ↗
  2. Evaluating agents and their limits ↗
  3. Knowledge graphs and JSON-LD ↗
  4. C2PA and content provenance ↗
View five progressive activities
  1. Define research questions, hypotheses and metrics.
  2. Build a dataset with known rights.
  3. Include negative and contradictory cases.
  4. Measure components, uncertainty and failures.
  5. 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

  1. RAG: retrieval-augmented generation ↗
  2. Prompt injection and agent security ↗
  3. C2PA and content provenance ↗
  4. Accessibility and AI: design for everyone ↗
View five progressive activities
  1. Identify use cases and accountable roles.
  2. Map data and tool permissions.
  3. Evaluate privacy, security and accessibility.
  4. Define metrics and approval gates.
  5. 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 ↗