This document discusses open educational resources (OER) and the potential for artificial intelligence (AI) to enhance OER. It describes how AI could be used to automatically ingest, clean, structure and preprocess OER from various sources. User modeling and social network modeling are discussed as ways to enable adaptive learning and personalized education. Several existing and potential AI systems are mentioned, such as tools for competency calculation, semantic processing of OER, and intelligent assistants. The growth of OER over time is shown in a graph. Cyc, an early large-scale AI knowledge base, is also summarized.

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OER and Open Education
Powered by AI
Mitja Jermol
UNESCO Chair in Open Technologies for Open Educational Resources
and Open Learning
Jozef Stefan Institute , Slovenia

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Mechanstic system

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Adaptive (social) environment

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What if…
Automatic ingestion,
cleaning, fusion , structuring,
preprocessing
User modelling and
understanding,
social network modelling
Career path Adaptive learning,
education and social
environment
X5oerfeed
Eventregistry
Searchpoint
Scienceatlas
X5analytics
DocumentAtlas
CurriousCat
JobAnalysis
CareerPath
Chatboots
QMiner
TextGarden
X5recommend

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Learner
Information
/Content
Learning channel
Teacher
Human/
Machine
Content structuring and
information understanding
User modelling and
assessment
Real-time
personalisation
Intelligent assistants
AI to the rescue

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0
500
1000
1500
2000
2500
3000
2004 2006 2008 2010 2012 2014 2016 2018 2020
NumberofccResources(millions)
Year
The growth of OER
Open Educational Resources
7Source: Hewlett Foundation
Various isolated sites
Various formats
Different modalities
Language diversity
Unresolved rights
Different levels of quality
Undescribed modes of use
Dead links

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Learning resources open pipeline
Automatic ingestion, cleaning,
fusion, preprocessing
Cross-lingual, cross-modal
Semantic processing
Didactic design and
pedagogical processing
Resources quality
processing
Innovation Research

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OER Website 1
OER Website 2
OER Website 3
OER Website 4 OER Website 5
OER Website 6
OER Website …OER Website n

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OER Website X
OER Website 1
OER Website 2
OER Website 3
OER Website 4 OER Website 5
OER Website 6
OER Website …OER Website n
$.ajax({
url: "http://log2.quintelligence.com/qlog.js", type: 'get', dataType: 'script', cache: true,
success: function() { setTimeout(function() {quintTracker(3);},100); }
});

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23/10/18 X5gon: Pilot case studies
poliMedia case

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Platform Architecture
X5GON: collect and index OER resources

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Visited content
Visit history
Demographics
User model
Learners profiles
Personalization
Targeted
learning
Observe ApplyModel
Learning/teaching
/behavior model
Incentives model
Background
knowledge
User modelling

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Competency calculation

15. edsa-project.eu
Demand Analysis
Sources:
Adzuna, Trovit
Jooble, Indeed,
CareerBuilder, CareerJet
Semantic
annotations
Cross-linguality
Skills
extraction
Locations
extraction
Statistics
Top Skills
0
50000
100000
150000
200000
250000
300000
350000
0
200
400
600
800
1000
1200
1400
1600
1800
Top Skills (December 2017)
OPEN DATA SCIENCE
PROCESS
MINING:
DATA
SCIENCE IN
ACTION
DATA
SCIENTIST
BASIC

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Learning Pathways
Learning Pathway – route
recommended to a
learner through a range of
trainings provided and
recommended by EDSA
partners, which allows
them to build knowledge
progressively.

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PhotoMath

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• 1956 : Birth of AI
• 1974–1980: 1st AI Winter
• In order to be able to think like humans do, the computer needs to have
knowledge about the world and the language like humans do.
• 1984: There is no shortcut (Doug Lenat - Cyc)
• the only way for machines to know the meaning of human concepts is to teach
them, one concept at a time, by hand.
• 1987-1993: 2nd AI Winter
• 2013: Cyc - 500k concepts, 17k relations, 10mio logical facts (bits of
knowledge)
Cyc – an early attempt to real AI

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Cyc
(Doug Lenat)
3-Stage Research Program for AI
1. Slowly hand-code a very large and very broad KB.
2. When enough knowledge is present, the system should actively help with the
KA process. It should be faster to acquire more from texts, databases,
[websites], interactive dialogues, etc.
3. To go beyond the frontier of human knowledge, the system will have to rely
on learning by discovery, to expand its KB domain by domain.

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Cyc Knowledge Base
Thing
Universe
isa
isa
Celestial
Body
isa
located in
Planet
subclass
Earth
isa
Animal
isa
Human
subclas
s
Physics
Money
Mathematics
Chemistry
Time
Learning
FoodVehicles
Event
Education
School
Language
LoveEmotions Going for a
walk
Death
Cat
Euro
Working
Words
Driving
RainStabbing someone
Nature
Tree
Hatred
Fear
Physics
Time
Learning
Vehicles
Event
Education
School
Emotions
Going for a
walk
Death
Cat
EuroWords
Driving
Rain
Stabbing someone
Nature
Tree
Hatred
Fear
Planet
Earth
isa
Human
Physics
Money
Mathematics
Chemistry
Time
Learning
FoodVehicles
Event
Education
Languag
e LoveEmotions Going for a
walk
Cat
Euro
Working
Words
Driving Rain
Tree
Hatred
Fear
Learning
Vehicles
Event
Education
School
Emotions
Euro
Driving
Stabbing someone
Hatred
Fear

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Cyc generated clauses
• The typical bird has 1 beak, 1 heart, lots of feathers,…
• Hearts are internal organs; feathers are external protrusions
• Most vehicles are steered by an awake, sane, adult,… human
• Tangible objects can’t be in 2 (disjoint) places at once
• Badly injuring a child is much worse than killing a dog
• Causes temporally precede (i.e., start before) their effects
• A stabbing requires 2 cotemporal and proximate actors
• etc…

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CYC natural language
- Linguistical Info. about a set of hundreds of thousands of terms
- Language-independent
‫اﻟﻘﻠم‬ ‫اﻟﻛﺗﺎﺑﺔ‬-Arabic Penitentiary
EnglishWord-Plume
EnglishWord-Pen
FrenchWord-
Plume
WritingPen
BirdFeather
Authoring
……

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Example from KB

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Cyc System Architecture

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WHAT IS BEHIND (CYC)
amount known
rateoflearning
CYC
(Based on the slide by Doug Lenat)
CC

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Semantic Chat: Social Conversational
Crowdsourcing
- Predefined options (menus)
- Knowledge guided input
- Free text input
Questions Answers
Human Machine Human Machine
KB
- NL Generation (Cyc)
- Patterns

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Levels of learning
• Learning new instances:
– Person: Luka, Dane, Janez
• Learning new concepts
– SlovenianPerson -> Person
• Learning new relations
– [Person] ResidesInRegion [Region]
• Learning new rules
– ([Person1] likes [Person2] AND
[Person2] likes [Person1]) ->
[Person1] friends [Person2]
CC, Oren Etzioni et al.
(TextRunner), Taylor M. et al. …
CC, Huairen Lin et al., Di Iorio
et al., Mitchell, T. et al., …
Oren Etzioni et al., Luis von Ahn et
al., Eckert, K. et al., Mitchell, T. et
al.,
?
Easy
Trickier
Hard to
do it
right
Hard

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Example: Instances
• Relation: (organizationHead [Person] [Organization])
• Arg1Isa: Organization
• Arg2Isa: Person
• NL: “[Org] is headed by [Person].”
• Example: Inštitut Jožef štefan is headed by ___.
• User fills the pattern with i.e. „Imaginary Joe“
• System implies: (isa ImaginaryJoe Person)

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Example: Concepts
• Relation: (havingFoodOrDrinkAtEvent ?Person
?Visit ?FoodOrDrink)
– Arg1Isa: Person
– Arg2Isa: Event
– Arg3Genl: FoodOrDrink
– Pattern: “[Person] had
[FoodOrDrink] at his [visit of place].”
• Example: Luka had ___ on his visit of
Egoist on 18 Sept.
– Answer: Strange coffee
– System implies: (sublclass StrangeCoffee FoodOrDrink)
FoodOrDrink
Food
Pizza
Drink
Coffee
Cappuccino
Alcoholic
Drink
Beer
Food Or
Drink
Strange
Coffee
Food
Pizza
Drink
Coffee
Cappuccino
Alcoholic
Drink
Beer
Strange Coffee

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Food Or
Drink
Strange
Coffee
Food
Pizza
Drink
Coffee
Cappuccino
Alcoholic
Drink
Beer
Example: Concepts
• Default positioning the graph is not accurate
enough.
• Better positioning with asking more
questions
– Is Coffee with milk type of Drink?
– Is Coffee with milk type of Coffee?
• Better positioning based on
string presentation.
– Because of word “coffee” in
Coffee with milk we can ask directly
whether it is a type of Coffee

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How it can be used in education
• Used as a knowledge assessor of its users
• Acting as a study companion
• By providing the knowledge back to its users
• By acting as a Encyclopedic question answering system

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AI: Open knowledge vs open content
OPEN SOURCE AND OPEN KNOWLEDGE AI
WEB, IoT
Learning
Checking
Learning
Checking
Learning
Teaching

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Micro-credentials and Qualifications
https://0xcert.org/
https://ecta.io/
Projects (Blockchain):
https://www.blockcerts.org/
https://www.myequals.net/
https://gledos.io/

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Links
H2020, FP7 Projects:
• x5gon (https://www.x5gon.org/),
• MOVING (http://moving-project.eu/),
• EDSA (http://edsa-project.eu/),
• translectures (http://www.translectures.eu/)...
Additional information:
• http://ailab.ijs.si, http://www.ijs.si, http://ct3.ijs.si, http://sensorlab.ijs.si/
• http://www.ouslovenia.net
• http://eventregistry.org, http://enrycher.ijs.si, http://newsfeed.ijs.si
• http://http://traffic.ijs.si/, http://streamstory.ijs.si/
• http://videolectures.net, http://opencast.org/matterhorn/
• http://searchpoint.ijs.si, http://scienceatlas.ijs.si
• http://qminer.ijs.si
• Mobility Patterns on GooglePlay
• iDiveriNews on App Store
• Curious Cat on GooglePlay