At the Center for Curriculum Redesign (CCR), we remain as committed as ever to answering our seminal founding question “What should students learn for the 21st century?” The media world is suddenly awash with news about Artificial Intelligence, but CCR has been working on its impact – and that of many other global factors – and consequences for Education, for a decade. We are delighted to offer an expanded Knowledge framework describing how to rethink content in the age of algorithms (from search to A.I.) to build: • Relevance and personalization (for learner motivation) • Versatility (robustness to future uncertainties) • Transfer (actionability in real-world situations, leading to “flipping the curriculum“) All of which are to be achieved via a deep redesign of disciplines and their content: • Modernizing/curating traditional disciplines (STEM, Humanities, Arts), via analysis of: o Essential Content o Core Concepts o Learning progressions • Adding Modern Disciplines (Technology & Engineering; Media (digital journalism, cinema); Entrepreneurship & business; Personal finance; Wellness (physical, mental); Social sciences (psychology, sociology, anthropology, political science, civics, future studies, etc.). • Developing Interdisciplinarity and Themes (Environmental, Global, Civic, Information and Digital Literacies; Systems, Design and Computational Thinking; etc.)

1. What should students
learn in an age of AI?
charles@curriculumredesign.org
@CurrRedesign #4DEdu
Area9 @ DR, June 2018

2. Music exercise – Audio

3. © Center for Curriculum Redesign
A Classic

4. © Center for Curriculum Redesign
Enjoying artistry

5. © Center for Curriculum Redesign
Innovation follows patterns → automatable
Source: Invention Machine “IM Labs”

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Million-fold improvement in 25 years
Neural Nets:
1988: 3 layers
2018: 50+ layers

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Cartoon to reality in six years

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Things computers still couldn’t do:
● Visual recognition
● Speech recognition
● Handwriting recognition
● Sentiment Analysis
● Win at Go
● Drive a car
● Topic analysis
● Play videogames
● Transfer artistic style
● Color black and white pictures
automatically
● Determine if someone in a
picture is sitting or standing.
September 2014

9. © Center for Curriculum Redesign
Within 2 Years…
Things computers still couldn’t do:
● Visual recognition
● Speech recognition
● Handwriting recognition
● Sentiment Analysis
● Win at Go
● Drive a car
● Topic analysis
● Play videogames
● Transfer artistic style
● Color black and white pictures
automatically
● Determine if someone in a
picture is sitting or standing.

10. © Center for Curriculum Redesign
Distribution of workers by use of general cognitive skills
and proficiency compared to computers
0%
10%
20%
30%
40%
50%
60%
70%
No Daily Use Daily Use, Proficiency at
Level of Computers
Daily Use, Proficiency Above
Level of Computers
Current computer
capabilities,
Maybe as 50%
Computer
capabilities
projected for 2026
Source: Table 5.1;
Survey of Adult
Skills (PIAAC OECD)
(2012, 2015)
By 2026 only
14% of adults
would perform
above
computer

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Proportion of workers using
general cognitive skills with
proficiency at or below level
of computer capabilities
0% 10% 20% 30% 40% 50% 60% 70% 80%
Japan
Finland
Netherlands
Slovak Republic
Flanders (Belgium)
Sweden
Estonia
Czech Republic
Poland
Norway
Austria
Australia
Denmark
New Zealand
Germany
Korea
Italy
France
Canada
Northern Ireland (UK)
England (UK)
Ireland
Turkey
Spain
Slovenia
Israel
United States
Greece
Chile
Computer
capabilities
in 2016
Additional
capabilities
projected
for 2026
Source: Table 5.1; Survey of Adult
Skills (PIAAC OECD) (2012, 2015)

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Emergence of New Jobs
Job Pay level
App developer High
Driverless car engineer High
Cloud computing specialist High
Big data analyst/data scientist High
Social media manager Medium
Sustainability manager Medium
YouTube content creators Medium
Millennial generational expert Medium
Drone operators Medium
Uber driver Low
Source: World Economic Forum “Future of Jobs”

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Quantitative or bust…
“We’re headed for a world where you’re either going
to be able to write algorithms and speak that
language, or be replaced by algorithms…”
Bridgewater hedge fund billionaire Ray Dalio
Source: FT January 2017 https://www.ft.com/content/0db7b7fa-f5fe-11e7-8715-e94187b3017e

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Artificial Intelligence

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Taxonomy & Ontology of AI Terms
ARTIFICIAL INTELLIGENCE
Broadest term, applying to any technique that enables computers to
mimic human intelligence, using logic, if-then rules, decision trees,
and machine learning.
– MACHINE LEARNING
Subset of AI that includes statistical techniques that enable
machines to improve at tasks with experience.
• DEEP LEARNING
Subset of machine learning composed of algorithms that permit software to
train itself to perform tasks, like speech and image recognition, by exposing
multilayered neural networks to vast amounts of data.
Source: http://fortune.com/ai-artificial-intelligence-deep-machine-learning/

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Artificial Intelligence – broad categories

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Artificial Intelligence applied everywhere

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The Power of Artificial Intelligence
• Interpretive/Translational:
• Analytical/Synthetic:
• Creative (incrementally):

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A.I. can sketch too !
• Seven artists generated about
8,000 pen strokes
• Disney's software analyzed
distance between facial features

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Curiosity – for A.I. too
Reinforcement learning Genetic algorithms

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A.I. goes to medical school

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Acceleration
but… these are
bounded/structured
problems
Relative
Complexity
1979 1994 2011 2017
X
Chess
X
Jeopardy
X
Go
X
Backgammon
X Poker
Human
Computer
Self-taught Chess X

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On the road to ExoBrain
Neuromorphic chips
Quantum computing
ML/GA algorithms

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Cloud Computing → “Synthetic Neocortex”

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Reality Check
"and underestimate the
effect in the long run.“
“We tend to
overestimate the
effect of technology
in the short run…
Source: Roy Amara, Former President,
Institute for the Future

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Our Limited Imagination
Source: Jean-Marc Côté, 1899

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Name this Actress

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Artistic Style Transfer
X =
X =

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Logical Inferences too

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Learn by itself/from each other
→ Hyperbolic Progression
Deep Learning
machine teaches itself
chess in 72 hours, to
International Master
level

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Opaque: converges how??
50 layers not just 3!

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Greedy – need lots of data

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Brittle – Easily Confused

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Brittle: Dogs Everywhere!

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Brittle: formatted vs real-life

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Shallow – low transfer

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Humans are very complex

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Just in the hippocampus...

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The Connectome

43. Over-Patternizing

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Question
What will we/our children need
to be successful in an A.I. world?

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Cognitive Domain and Algorithms
Source: Bloom/Anderson
CREATING
Synthesizing
EVALUATING
ANALYZING
APPLYING
UNDERSTANDING
REMEMBERING

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The Impact of Big Data & Machine Learning
Areas in which machines beat humans:
• repetitive/predictive tasks
• tasks that hinge on computational power
• classifying huge amounts of data and inputs
• making decisions based on concrete rules
Biases might be fewer, but still induced by
algorithms and data sets chosen by humans

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But Humans are better at…
• experiencing authentic emotions and building
relationships
• formulating questions and explanations across scales
and sources
• deciding how to use limited resources across
dimensions strategically (including which tasks
machines should be doing and what data to give them)
• making products and results usable for humans and
communicating about them
• making decisions according to abstract values

49. © Center for Curriculum Redesign
Protopia

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Will we need to learn a foreign language?

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Combating e-Misinformation
“We’ll have to rely on societal solutions, such as teaching
kids critical thinking by getting them to take things like
speech and debating classes. They’re competing against
another student and thinking about how to craft
misleading claims, or how to craft correct claims that are
very persuasive.”
Ian Goodfellow
Google Unsupervised Learning Guru

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“An educated mind can: (paraphrased)
• Determine connections
• Spot analogies
• Predict outcomes
• Learn from failure
• Recover from failure
• Seek explanations
• Absorb newness
• Handle exceptions
• Deal with abstraction
• Generalize reasonably
• Be self-aware”
Task-centric view
A.I. Performance 2018?
“Sort of”
Source: Roger Schank “Making minds less well-educated than our own” 2004

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Affective Domain and Algorithms
Source: Krathwohl, Bloom, Masia
INTERNALIZING
ORGANIZING
VALUING
RESPONDING
RECEIVING

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Psychomotor Domain and Algorithms
Source: Simpson
ORIGINATION
ADAPTATION
COMPLEX OVERT
RESPONSE
MECHANISM
GUIDED RESPONSE
SET
PERCEPTION

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Predictions for Human-level AI
Source: MIRI, “Predicting AI”

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Protopia, Not Utopia Or Dystopia
“It is much easier to simulate the reasoning of a
highly trained adult expert than to mimic the
ordinary learning of every baby.”
Alison Gopnik
“NOT using algorithms will become morally
questionable (health care, law, driving, etc.)”
Deloitte report

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Climate change Personal privacy
Financial instability Political partisanship
The road ahead is challenging…
Inequities
Technological disruptions
Religious intolerance

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Reality Check
“I am much more concerned about
natural stupidity
than I am of
artificial intelligence”
My cousin Christian ☺

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21st Century → Versatility

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Renaissance Humans Needed

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→ Self-Propelled Learners
Meta-Learning (Metacognition, Growth Mindset)
is most critical and underappreciated

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Redesign Philosophy
Focus on deeper learning goals of modern education:
1. Relevance (motivation) → Personalization
2. Versatility (robustness)
3. Transfer (actionability & resourcefulness)
• Selective modernizing/emphasis on important
areas of traditional knowledge
• Adding modern(ized) knowledge
• Focusing on Concepts not just Topics
• Interdisciplinarity, using real-world applications
• Development of Skills, Character, Meta-Learning

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4-D Framework
Mindfulness
Curiosity
Courage
Resilience
Ethics
Leadership
Creativity
Critical thinking
Communication
Collaboration
Modernized Disciplines
Interdisciplinarity
Growth Mindset
Metacognition

65. © Center for Curriculum Redesign
Flipping the Curriculum
Transfer
Expertise
Understanding
Knowledge
Information
Data
Transfer
Expertise
Understanding
Knowledge
Information
Data
Algorithms

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Learning WITH the Machines
Deep Learning (A.I.) +
“Deeper Learning” (H.I.)
= Augmented Intelligence

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“…great responsibility follows
inseparably from great power.”
French National Convention of 1793

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www.curriculumredesign.org
Twitter: @CurrRedesign #4DEdu
Charles@curriculumredesign.org
“What should students learn for the 21st century?”
Thank You

69. © Center for Curriculum Redesign
“Educators worldwide need
to rapidly operationalize
these dimensions”
Todd Rose
Harvard University
Download:
http://bit.ly/4DEdu
“A very thoughtful treatment
of the competencies our
students need to thrive in
today’s/tomorrow’s world.
Carol Dweck
Stanford University