Presented May 2016 at the European Distance Learning Association EADL 2016, Nicosia, Cyprus

1. Artificial Intelligence in E-learning
(AI-Ed): Current and future
applications
Roy B. Clariana (Rclariana@psu.edu)
Penn State University
www.PSU.edu
EADL 2016, Nicosia, Cyprus
May 26-27, 2016
1
http://tinyurl.com/RBC-EADL2016
http://www.personal.psu.edu/rbc4/EADL2016.pptx

2. Pennsylvania State University
Residential enrollments
• 40,000 undergraduate
• 10,000 graduate
PSU World Campus
• 3,000 undergraduate
• 3,000 graduate
Penn State ranked 42nd in
the world in 2016, from:
http://cwur.org/2015/
& about me… 2

3. Artificial Intelligence in E-learning
Current
• Intelligence? What is Artificial Intelligence (AI)
• AI, a little history
• AI and Big Data: Same or different
• Deep learning and ‘structure’
• AI in education (AI-Ed)
Future
• Past and future trends
3

4. Intelligence?
Self-actualized car
I’m going
back to
school!
Self-driving car Self- Conscious car
Would I look
better in
red?
Amy Kurzweil
(New Yorker, Apr. 2016)
4
Google’s self driving car

5. HAL 9000
5
Intelligence?
Our imaginary friends often have the best answers:

6. Exemplar AI as imagined 50 years ago
• HAL (Heuristically programmed ALgorithmic Computer) was an AI
character in “2001: A Space Odyssey”, the 1968 epic science fiction
movie produced and directed by Stanley Kubrick (and written by
Kubrick and Arthur C. Clarke). And of course, Alan Turing…
• HAL was based on ILLIAC (Illinois Automatic Computer, a room-sized
machine built at the University of Illinois, at Urbana-Champaign) it
could analyze radar patterns, the effects of atomic blasts, the stability
of materials used in construction, and even the composition of music.
• These programmed tasks involve many subroutines that can be
improved and then be applied in other programs.
50 years: decomposition/deconstruction of ‘artificial intelligence’
with uneven incremental improvement … 6

7. Get real: A modern definition of AI
“For our purposes, we define AI as computer systems that have been
designed to interact with the world through capabilities (for example,
visual perception and speech recognition) and intelligent behaviours
(for example, assessing the available information and then taking the
most sensible action to achieve a stated goal) that we would think of as
essentially human.” p.14
From: Rose Luckin, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier (2016).
Intelligence Unleashed: An argument for AI in Education. Pearson. (A free
publication; University College, London and Pearson)
7
https://www.pearson.com/content/dam/corporate/global/pearson-
dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf

8. Visuwords link
8
http://visuwords.com/artificial_intelligence

9. 9
big data
, big data
Link to Big Data and data mining

10. AI and Big Data, the same thing … almost
• Jolanta Galecka will discuss Big Data (Data Mining, 14:00)
• Big Data/Data Mining: This field is mostly concerned with
extracting information from a vast amount of data. It is not
exactly a technical subject; rather it is application of different
algorithms related to NLP, Machine Learning, and AI. (e.g., Search
applications, Text Summarization, Question Answering systems
(SIRI) etc. are example of this.) count clicks
• For example: Marketing data in the USA has shown:
Prego brand – dog lovers; Ragu brand – cat lovers
10

11. AI-Ed & Big Data viewed across time scales
Cognitive 10-1 – 10 s. Symbolic processes and structures, embodied cognition
Rational 102 – 104 s. (minutes to hours) Achievement, behaviors, identity
Biological < 10-2 s. Neural processes, eye-tracking, reaction time
Organizational > 107 s. (months) Economics, legislation, equity,
policy
Nathan M.J., & Alibali , M.W. (2010). Learning sciences. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 329-345.
Newell A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.
Socio-cultural Historical 104 – 106 s. (hours to days) Practices,
environments, curricula
11

12. AI (Big Data?) is already influencing e-learning
‘intelligent’
assistance
12

13. Voice recognition in Google Docs is AI
13https://docs.google.com/document/d/1OyFLdfbw_NJHAIFO3i7F1IfFZQjZA554mLP52p9BFrg/edit

14. The Semantic web is influencing e-learning
14
http://blog.law.cornell.edu/voxpop/files/2010/02/radarnetworkstowardsawebossmall.jpg

15. Fast connectivity and powerful hardware are
necessary for AI and AI-Ed
15
SO ---
due to so much
improvement in so
many areas
(connectivity, hardware,
software, apps, web
2.0, …) it is hard to
disentangle the
influence of AI on e-
learning

16. Explicit application of AI in Education:
International Journal of AI-Ed
• 25th Anniversary Special Issue; Guest Editors: Monique Grandbastien,
Rosemary Luckin, Riichiro Mizoguchi, and Vincent Aleven
• International Journal of Artificial Intelligence in Education, 26 (1),
March 2016
http://link.springer.com/journal/40593/26/1/page/1?utm_campaign=C
ON27814_2&utm_medium=newsletter&utm_source=email&wt_mc=e
mail.newsletter.8.CON27814.internal_2
16

17. Artificial Intelligence in Education (AI-Ed)
• The application of artificial intelligence to education (AI-Ed) has been the
subject of academic research for more than 30 years (p.18)
• Most of this time has focused on development and research on a
proliferation of “Artificial Intelligent Tutors” that are very expensive to build
and maintain; these AI-Tutors are tightly locked only to specific learning
content (e.g., Algebra, computer programming), these usually show a slight
improvement on average over ‘traditional’ instruction but it varies
extremely from setting to setting
• AI-tutors mainly depend on an expert systems approach, for example, IBMs
DeepBlue beat Gary Kasparov at chess in 1997
• but more recent approaches are incorporating so called “deep learning”,
machine-learning approaches (AlphaGo)
https://en.wikipedia.org/wiki/AlphaGo
17

18. Lesson/course level AI (intelligent tutors)
Adaptive learning systems (i.e., intelligent tutors) in general require the
maintenance and interaction of four models, the expert model that consists
of the information to be learned (e.g., the knowledge structure of the
domain or of experts in the domain), the student model that tracks and
learns about the student (i.e., their structural knowledge or schema), the
instructional model that actually conveys the information, and the
instructional environment that is the user interface for interacting with the
system (p. 105).
Note that adaptive … systems have high development costs and other
inherent potential disadvantages and may be only marginally more efficient
or effective than similar non-adaptive evaluation systems, and so
development cost versus benefit must be considered (p. 104).
Clariana, R. B., & Hooper, S. (2012). Adaptive evaluation systems. In N. M. Seel
(Ed.), Encyclopedia of the Sciences of Learning . Secaucus, NJ: Springer.
18

19. Why 25 years of Intelligent tutoring Systems
haven’t affected your learning enterprise much
• Intelligent tutoring systems are expensive (time and money) both
to develop and implement. Development requires the
cooperation and input of subject matter experts across both
organizations and organizational levels.
• There are substantive factors that limit the incorporation of
intelligent tutors into the real world: highly specific content, the
long timeframe required for development, and the high cost of
the creation of the system components. For instance, encoding
an hour of ITS instruction time takes 300 hours of development
time. Intelligent tutoring systems are not, in general,
commercially feasible for real-world applications. wikipedia 19

20. So… AI has two general approaches
• Expert systems approach – manually build in the ‘intelligence’ (e.g.,
email response systems…) using input-output pattern matching to a
large database of manually created input-output
• Neural network, or machine learning, etc. – accrue/impart in the
‘intelligence’, patterns in the voluminous input are set into the neural
network (did Papert and Minsky kill NN research in the 1980s?)
AlphaGo Example: Go is considered much more difficult for computers
to win than other games such as chess (1997 Kasparov bested by
DeepBlue), because its much larger branching factor makes it
prohibitively difficult to use traditional AI methods
20

21. NN: AlphaGo Match against Lee Sedol
• AlphaGo played South Korean professional Go player Lee Sedol, ranked 9-dan,
one of the best players at Go, with five games taking place at the Four Seasons
Hotel in Seoul, South Korea on March 9-15, 2016. A DeepMind team member
placed stones on the Go board for AlphaGo, which ran through Google's cloud
computing with its servers located in the United States.
• At the time of play, Lee Sedol had the second-highest number of Go international
championship victories in the world and some sources ranked Lee Sedol as the
fourth-best player in the world at the time. AlphaGo was not specifically trained
to face Lee.
• The first three games were won by AlphaGo following resignations by Lee Sedol.
However, Lee Sedol beat AlphaGo in the fourth game, winning by resignation at
move 180. AlphaGo then won the fifth game by resignation.
21
wikipedia

22. Two ‘take away’ principles
Artificial intelligence approaches must embrace some pretty
complicated computation problems – natural language processing,
speech recognition, speech production, machine vision, probabilistic
logic, planning, reasoning, many forms of machine learning
1. Although these countless AI bits are related in certain ways, strides
are made in each area – but not uniformly – these sub-areas surge-
and-lag at different trajectories
2. There is underlying “structure” or “knowledge graphs” or
“knowledge structure” that makes some of these tasks immensely
easier
22

23. Knowledge Structure  Artefact Structure
• These ‘knowledge graphs’ capture or represent the inherent patterns/
structures that exists between words (concepts) of language, these
are highly explanatory
23
This structure in our
artefacts (books,
conversations, movies,
images, …) has a
reciprocal influence

24. Visually – structure convergence
24Ferstl, E.C., & Kintsch, W. (1999). Learning from text: structural knowledge assessment in the study of discourse comprehension. In van Oostendorp and Goldman (eds.), The
construction of mental representations during reading. Mahwah, NJ: Lawrence Earlbaum.
needs
concerns
feelings
empowerment
relationship
motivation
focus
productivity
pay
plan
contingency
classical
efficiency
humanistic
measure
leadership
management
success
individual
company
TQM
quality
customers
goal
work
environment
employee
service
needs
concerns
feelings
empowerment
relationship
motivation
focus
productivity
pay
plan
contingency
classical
efficiency
humanistic
leadership
management
success
individual
company
TQM
quality
customers
goal
work
environment
employee
Text base
Updated situation model
(post list recall)
needs
concerns
feelings
empowerment
relationship
motivation
individual
productivity pay
plan
contingency
classical
efficiency
humanistic
measure
leadership
managementsuccess
focus
company
TQM
quality customers
goal
work
situation
employee
Situation model
(pre list recall)

25. 25

26. 26
1966 lecture at Johns Hopkins

27. Knowledge graph
27

32. Warning: on December 11, 2015, there was a
disturbance in the force …
32

33. Voice recognition as a critical subroutine AI bit
• Voice recognition is an important area of AI (and thus AI-Ed) that has
made incredible gains recently (20 years till now)
• Do you remember “Dragon Speak”? Drs. James and Janet Baker
developed an early speech recognition system (DRAGON) in 1975 that
used hidden Markov models, a probabilistic method for temporal
pattern recognition, but hardware and software then were
insufficient to handle the problem of word segmentation (e.g.,
determining the boundaries of words during continuous speech
input). Users had to say and pause, say and pause, and English only
• I bought NaturallySpeaking 1.0 in 1997 with stars in my eyes, but I
could never get it to understand my speech
33
BT in 1995 had pretty good voice recognition

34. Tongue twister for SIRI (mainly hard coded replies)
SIRI launched October 4, 2011
with regular cloud updates
Currently:
Arabic, Chinese, Danish,
Dutch, English, Finnish,
French, German, Hebrew,
Italian, Japanese, Korean,
Malay, Norwegian,
Portuguese, Russian,
Spanish, Swedish, Thai, Turkish
34
Siri is a spin-out from the SRI International
Artificial Intelligence Center, and is an
offshoot of the DARPA-funded CALO project.
http://www.ai.sri.com/timeline/

35. Siri, Cortana (Halo?), Alexa, Ok Google, Viv
• AI digital assistants – personalize with a name or not? Male or
female?
• Amy or Andrew Ingram (https://x.ai/ calendaring digital assistant,
notice the play-on-words n-gram)
• But more critical than this HCI (human computer interface), how do
these digital assistants work?
• Most current ones are mainly expert systems, ‘hard coded’, but all will
shift towards NN deep learning approaches (machine learning)
35

36. For example, Viv (http://viv.ai/)
• Viv is an artificial intelligence platform that enables developers to
distribute their products through an intelligent, conversational
interface. It’s the simplest way for the world to interact with devices,
services and things everywhere. Viv is taught by the world, knows
more than it is taught, and learns every day.
• YouTube video from Disrupt NY 2016v May 9 - 11, 2016
http://techcrunch.com/2016/05/09/siri-creator-shows-off-first-
public-demo-of-viv-the-intelligent-interface-for-everything/
36

37. Viv knowledge base
37

38. Microsoft’s AI Chatbot named “Tay” March 23 to 24, 2016
• Microsoft’s AI Chatbot named “Tay” March 23 to 24, 2016
http://fusion.net/story/284537/tay-and-you-microsoft-racism/
• “Microsoft announced Tay earlier this week with great fanfare. It was an AI
bot that could talk like an 18–24 year old and hold conversations with users
on various social media networks. The bot would learn new information
from conversations it had with Twitter followers and incorporate that
knowledge into future conversations, all in the its sassy style.”
• But Microsoft had no control over who Tay spoke with and Twitter can be a
very toxic place. As the worst of Twitter unloaded their racist manifestos on
the bot, it was always learning, incorporating what it was told into its
corpus. So it shouldn’t surprise anyone that Tay turned out, as the teens
might say, “racist af.”
38

39. 39
Image recognition as a critical AI subroutine
http://cs.stanford.edu/people/karpathy/deepimagesent/

40. Narrative composed by AI
40http://www.cs.toronto.edu/~rkiros/adv_L.html
Neural Artistic Captions
Image  story

41. Facial tidbits
• Facial recognition – many approaches, many solutions, many
companies, lots of money at stake (online testing?):
https://en.wikipedia.org/wiki/Facial_recognition_system
• Emotion recognition – The MIT computer that knows what you're
thinking, Jane Wakefield BBC Technology reporter, November 2015
http://www.bbc.com/news/technology-34797189
• FaceBook automatically recognizes people in my Facebook photos
since February 2016 (auto-tagging) using deep learning NN from
face.com (an Israeli startup purchased in 2013)
• HAL – lip reading?
41

42. More tidbits
• Computer, respond to this email. – from the Google research site,
could be used right away in my e-learning course
• Machine Learning in the Cloud, with TensorFlow, March 23, 2016,
Posted by Slaven Bilac, Software Engineer, Google Research
http://googleresearch.blogspot.com.cy/2016/03/machine-learning-
in-cloud-with.html
• Star Trek's Universal Translator? Waverly Labs Pilot Smart Earpiece
Might Be It by Geoffrey Morrison, May 17, 2016,
http://www.forbes.com/sites/geoffreymorrison/2016/05/17/star-
treks-universal-translator-waverly-labs-pilot-smart-earpiece-might-
be-it/3/#4d86247b4f3b
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43. FaceBook translation as AI-Ed
Using machine learning,
Facebook is now serving 2 billion
text translations per day.
Facebook can translate across
40 languages in both directions,
such as French to English.
MOOCs using FaceBook for
discussions can benefit from
these translations.
43
http://techcrunch.com/2016/05/23/facebook-translation/

44. AI-On-A-Chip: Hardware specifically for AI
• AI-On-A-Chip Soon Will Make Phones, Drones And More A Lot
Smarter by Robert Hof, Forbes May 7 2016,
http://www.forbes.com/sites/roberthof/2016/05/07/ai-on-a-chip-
soon-will-make-phones-drones-and-more-a-lot-
smarter/#49880c42149b
44

45. AI-Ed: My predictions
• Increased proliferation of NN AI apps (mainly non-education)
across the spectrum of the mass market, because mass
adoption allows for recouping investment cost and profits.
• OpenAI -based applications developed by 3rd party
developers will creep into online learning settings, and
already are creeping in
• So AI will ‘happen’ almost unintentionally in your e-learning
setting.
45

46. AI-Ed: My predictions
• Instructors will probably use AI bots to monitor online
discussions
• However, frankly, bots could reply to online text-based
discussions in ways that are likely to be indistinguishable from
human input and may be even better than what the student
would say
• So online discussions could devolve into students' bots texting
each other while the students will probably interact in a back
channel if at all (e.g., snapchat, etc.:
http://www.screenretriever.com/Popular-Moble-Apps-for-Teens
46

47. AI-Ed: My predictions
• But a little later, a killer app using AI that integrates across
this spectrum, AI using AI (an analogy – a computer program
that uses subroutines, a heuristic that selects the apposite
algorithm from a set of many) [similar to tech convergence: 3
devices (phone, email, camera) become 1 device]
• Ultimately some predict a life time personal assistant – It
learns your preferences and speech patterns and so is tuned
to you (e.g., ala daemon, Philip Pullman's trilogy His Dark
Materials)
47

48. Questions?
Thank you!
Roy B. Clariana (RClariana@psu.edu)
http://tinyurl.com/RBC-EADL2016
48