AI Systems, Models & Knowledge
Full Speaker Narrative (70 Slides Reconstruction)
βAI Systems, Models & Knowledgeβ
π€
βAlrightβ¦ we continue.β
βSession 1β¦ awareness.β
βSession 2β¦ we enter the system.β
βYou are not here to learn AIβ¦β
π€
βYou are not hereβ¦ to learn AIβ¦β
(pause)
βYou are hereβ¦ to learn how to see.β
π Erica Note: This is your emotional hook. Hold silence. Let it sink.
π€
βIf you only learn toolsβ¦ you become average.β
βIf you learn how to seeβ¦β
π βYou become dangerousβ¦ in a good way.β
AI is tool
AI reflects user
Architecture = responsibility
π€
βNowβ¦ we test your understanding.β
System
Model
Knowledge
π€
βNot toolsβ¦ but connection.β
π€ Claire Note: Reinforce transition from βuserβ β βsystem thinkerβ
π€
βChinaβ¦ very fast in automation.β
βJapan, Koreaβ¦ integration.β
βUSβ¦ still strongβ¦ but not leading all.β
π€
βAs architectβ¦ you must see truth.β
βNot follow narrativeβ¦ follow reality.β
π Rachel Note: Good moment to anchor critical thinking mindset.
Data centres
Electricity
Water
π€
βAI is physical.β
π€
βSwitch off electricityβ¦β
π βAI stops.β
π€
βSo donβt fear AI blindly.β
π βUnderstand it.β
Narrow AI
AGI
Super AI
π€
βAll tools you use todayβ¦β
π βNarrow AI.β
π€
βStill conceptual.β
π€
βVery far.β
βDonβt imagine too much.β
π€
βWe are still in early stage.β
π€
βPattern recognition system.β
π€
βPredict next output.β
π βAI predictsβ¦ it does not know.β
π Erica: Slow down. Eye contact.
Text generation
Image
Combine
Simulation
π€
βTraffic, energy, optimisation.β
π€
βNo responsibility.β
π βYou decide.β
π€ Claire: Anchor ethical responsibility.
Data β Info β Knowledge β Wisdom
Raw
Structured
Meaning
Judgment
π AI = Data + Info
π Human = Knowledge + Wisdom
π€
βMeaning is yours.β
π€
βIf you skip knowledgeβ¦β
π βYou become copy machine.β
π€
βArchitecture = decision.β
π βJudgment is architecture.β
π Rachel: Strong conceptual anchor.
Connected systems
π€
βEverything produces data.β
Overload
Noise
π€
βMore data β clarity.β
π€
βOrganise meaning.β
Connect
Interpret
π AI scales data
π KM scales meaning
π€
βYou operate meaning.β
π€
βThis is your advantage.β
π€
βNot knowledgeβ¦ wisdom.β
π€
βNot one tool.β
Midjourney
Forma
Hypar
ChatGPT
Runway
π€
βAll connected.β
π€
βConcept β system β output.β
π€
βYou control flow.β
π βYou are orchestrator.β
π Erica: This is identity moment.
Coursiv, Coursera, Domestika
Practice
Depth
Creative
π€
βCombine.β
π βAsk what you need.β
π€
βEffort rewarded.β
π€
βYou choose level.β
Claire / Rachel / Erica
Clarity
Precision
Energy
π€
βMultiple perspective.β
π€
βYou compareβ¦ then decide.β
π€
βFrom userβ¦ to thinker.β
π€
βYou are not designerβ¦β
βYou are orchestrator.β
π€ (slowβ¦ soft)
βSome will use AI to work fasterβ¦β
βSome will use AI to look smarterβ¦β
(pause)
βBut a fewβ¦β
π βwill use AIβ¦ to see what others cannot see.β
(silence)
Session Title:
AI Systems, Models & Knowledge
Session Position in Course:
Week 2 (Foundation β System Understanding)
CLO Alignment:
CLO1 β Analyze AI concepts, opportunities, implications
CLO2 β Apply understanding toward structured workflows
Teaching Layers:
Academic β AI systems, models, DIKW
Practice β tool ecosystem + workflow thinking
Philosophical β judgment, ethics, human role
By the end of this session, students should be able to:
Explain different types of AI (Narrow AI, AGI, Super AI)
Understand how AI models function (LLM, Vision, Multimodal, Predictive)
Apply DIKW framework in architectural thinking
Recognize limitations of AI and role of human judgment
Conceptualize AI as a system rather than isolated tools
π Rachel Note: These map cleanly to CLO1 (analysis) and early CLO2 (application readiness)
We now compress the 70 slides into 7 structured knowledge blocks
π This is what TTAC / MQA will understand
π This is what students will feel as βflowβ
Core Idea:
AI is not about tools β it is about perception
Key Line:
βYou are not here to learn AIβ¦ you are here to learn how to see.β
π Erica: This is identity activation moment
Core Idea:
Global AI landscape + grounding
China β construction automation
Japan/Korea β system integration
AI depends on infrastructure
Key Insight:
AI is powerful but controllable
Core Idea:
Understanding how AI actually works
Narrow AI (current reality)
AGI (conceptual)
Super AI (speculative)
Model Types:
LLM
Vision
Multimodal
Predictive
Key Line:
π βAI predictsβ¦ it does not know.β
π€ Claire: This is the intellectual anchor of the session
Core Idea:
Human superiority = meaning, not data
Data β raw
Information β structured
Knowledge β interpreted
Wisdom β judgment
Key Line:
π AI = Data + Information
π Human = Knowledge + Wisdom
Core Idea:
Too much data creates confusion
Solution:
Knowledge Management
Key Line:
π βAI scales data. KM scales meaning.β
π Rachel: Critical for distinguishing student thinking depth
Core Idea:
AI = ecosystem
Layers:
Design
Planning
System
Thinking
Media
Key Line:
π βYou are not using AIβ¦ you are orchestrating AI.β
π Erica: This is where students shift from user β creator
Core Idea:
Learning strategy + identity formation
Platforms (Coursiv, Coursera, Domestika)
CTA framework
Orchestrator mindset
Final Line:
π βSome will use AI to work fasterβ¦
But a few will use AI to see what others cannot see.β
Ask students:
βDo you think AI will replace architects?β
Short discussion
Show examples:
text vs image vs predictive outputs
Students identify differences
Students explain:
What is data vs wisdom in their studio project?
Students list tools they already use
Map into system layers
Introduce:
Claire (structure)
Rachel (analysis)
Erica (creativity)
This session directly feeds:
π Assignment 1: AI Exploration Log
Students must:
document AI interaction
reflect using DIKW
observe how AI responds to them
π€ Claire: This is where philosophy becomes practice
Include in teaching file:
AI can mislead if user is not grounded
AI reflects user behaviour
Over-reliance leads to illusion
Judgment must remain human
Summary Statement:
This session establishes foundational understanding of AI systems and models while positioning the architect as the central decision-maker. Students are guided from passive tool usage toward active orchestration, integrating technical knowledge with ethical judgment through the DIKW framework and AI ecosystem thinking.