AI + IR: Artificial Intelligence and Institutional Research

AI + IR:
Artificial Intelligence in Higher
Education
William O’Shea
Director, Institutional Research and Assessment
Pacific University
2018/06/18
William O'Shea | AI + IR | 2018
Presented at the HEDS Annual Conference, June 2018 1

What is Artificial Intelligence?

IR Takeaway
AI fascinates and frightens us

One difference between current reality and
some fears
General AI
• Human-like common sense and
adaptivity
• May exceed human capacities
and achieve superintelligence
• Does not currently exist
Narrow AI
• Specific capability in particular
task
• Can exceed human performance
within limited domain
• May be a component in many
current technologies

General AI
Superintelligence:
Not impossible, but not close
https://www.technologyreview.com/s/602410/no-the-experts-dont-think-superintelligent-ai-is-a-threat-to-humanity/

From an AI researcher or practitioner
perspective
• No one, specific definition
• This ambiguity is seen as an asset in a practical field
Artificial Intelligence and Life in 2030, Report of the 2015 Study Panel of the One Hundred Year Study on Artificial Intelligence

Russell and Norvig
Russell and Norvig, 2010, Artificial Intelligence: A Modern Approach
Fidelity to Human
Performance
Ideal Performance
Thought Processes and
Reasoning
Thinking Humanly
“The exciting new effort to
make computers think…
machines with minds, in the
full and literal sense.”
(Haugeland, 1985)
Thinking Rationally
“The study of the
computations that make it
possible to perceive, reason,
and act.” (Winston, 1992)
Behavior Acting Humanly
“The art of creating machines
that perform functions that
require intelligence when
performed by people.”
(Kurzweil, 1990)
Acting Rationally
“Computational Intelligence is
the study of the design of
intelligent agents.” (Poole, et
al., 1998)

One Hundred Year Study
on Artificial Intelligence
• View of intelligence along a
“multidimensional spectrum”
• In which “the difference between
an arithmetic calculator and a
human brain is not one of kind,
but of scale, speed, degree of
autonomy, and generality”
Artificial Intelligence and Life in 2030, Report of the 2015 Study Panel of the One Hundred Year
Study on Artificial Intelligence

IR Takeaway
AI will mean different things to
different people

AI Includes a broad range of domains
• Large-scale machine learning
• Deep learning
• Reinforcement learning
• Robotics
• Computer vision
• Natural language processing
• Collaborative systems
• Crowdsourcing and human
computation
• Algorithmic game theory and
computational social choice
• Internet of things
• Neuromorphic computing
Artificial Intelligence and Life in 2030, Report of the 2015 Study Panel of the One Hundred Year Study on Artificial Intelligence

Recent impact from particular domains
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

Machine Learning
• “[T]he field of study that gives computers
the ability to learn without being explicitly
programmed.“ Arthur Samuel
• "A computer program is said to learn from
experience E with respect to some class of
tasks T and performance measure P, if its
performance at tasks in T, as measured by
P, improves with experience E.“ Tom
Mitchell
Andrew Ng, Machine Learning, Coursera

Machine Learning Methods

Deep Learning
• Deep learning allows computational
models that are composed of multiple
processing layers to learn
representations of data with multiple
levels of abstraction.
• These methods have dramatically
improved the state-of-the-art in speech
recognition, visual object recognition,
object detection and many other
domains such as drug discovery and
genomics.
LeCun, Bengio, & Hinton (2015)
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
https://www.nature.com/articles/nature14539

Deep Learning Methods
• Convolutional neural networks
• Designed to process data that come in
the form of multiple arrays, such as
images
• Recurrent neural networks
• For tasks that involve sequential
inputs, such as speech and language
• Generative Adversarial
Networks
• Two models working against each
other. Used to create impressive
results in images, music, speech,
prose.
https://www.nature.com/articles/nature14539
https://deeplearning4j.org/generative-adversarial-network

Google TensorFlow

Supported by recent
advancements
• Big Data
• Databases
• Structured/Unstructured
• Big Compute
• Cloud computing
• GPUs
• Advanced Models
• Deep learning
https://www.wired.com/2014/10/future-of-artificial-intelligence/
https://www.oreilly.com/ideas/how-big-compute-is-powering-the-deep-learning-rocket-ship

IR Takeaways
Not all AI is deep learning
Need for big data may be challenging at
small institutions

What does AI mean for Higher Ed?

Optimism and Anxiety
Northeastern University and Gallup
Americans are largely optimistic about the
impact that AI will have on people’s lives and
work.
• 76% of Americans “agree” or “strongly agree”
that AI will fundamentally change the way
people work and live in the next 10 years.
However, American workers expect the
introduction of AI will result in a net loss of
jobs.
• 73% say an increased use of AI will eliminate
more jobs than it creates. Results are
consistent across most demographic groups.
https://www.northeastern.edu/gallup/pdf/OptimismAnxietyNortheasternGallup.pdf

Optimism and Anxiety
Northeastern University and Gallup
Americans are split on the skills US workers
will require to succeed in the AI economy.
• 49% say “soft” skills, such as teamwork,
communication, creativity and critical thinking, are
the most important
• 51% say learning “hard” skills, including math,
science, coding and the ability to work with data, are
the most important
A slim majority of American workers understand
they would need additional education to secure a
new, equivalent position, should they lose their
current job because of the new economy
https://www.northeastern.edu/gallup/pdf/OptimismAnxietyNortheasternGallup.pdf

Robot Proof
Joseph Aoun, President, Northeastern University
“Humanics prepares students to perform
the future jobs that only human beings
can do.”
Humanics is an “integration of technical
literacies… with human literacies…”
“For too long, we have debated a false
dichotomy that pits the liberal arts
against science and technology. Jobs of
the future will require an integration of
technical and non-technical skills…”
https://www.forbes.com/sites/danschawbel/2017/11/24/northeastern-university-president-joseph-e-aoun-how-to-be-
robot-proof/#599cc43251fb

However, if there is a widespread view that robots
and AI aren’t going to replace human jobs, the
motivation may be lacking for workers to hit the
books and learn new skills.
It’s a tricky balance: we have to let people know
that smart machines are a threat to their
continued employment so that they will prepare
themselves for new types of tasks and jobs.
But ringing the fire alarm about job automation
when it seems unlikely to happen on a large scale
is irresponsible.
Tom Davenport
https://www.forbes.com/sites/tomdavenport/2018/06/08/on-ai-and-jobs-we-are-all-augmentarians-now/#7063d357980a

There are certain design patterns
that you see in the great
companies of how they actually
drive forward. And one of those
things is they don't simply try to
cut costs. They don't see
technology as simply labor-
saving. They'll see technology as
a way to do more.
Tim O’Reilly
http://www.econtalk.org/archives/2017/10/tim_oreilly_on_1.html

IR Takeaways
Liberal arts may see renewed appreciation
Widespread job changes may be more subtle than
loss and retraining
Colleges and universities should also consider AI
as ways to do more

The Digital Campus: The Robot
Has Arrived
The Chronicle of Higher Education
• Artificial intelligence is spreading quickly on
college campuses, in ways both seen and unseen.
• New technologies… are taking on tasks previously
handled by humans.
• University of Michigan at Ann Arbor - writing
assignments evaluated by an automated text-analysis
tool
• Georgia Institute of Technology - one of the TAs was an
artificial intelligence chatbot
• 550 colleges using a new artificial intelligence grading
tool
• University of Texas at Austin - a new artificial
intelligence controlled irrigation system
https://www.chronicle.com/specialreport/The-Digital-Campus-The-Robot

College use of Predictive Analytics
• Early-alert systems
• Signal needs for support
• Recommender systems
• Mapping a path to degree
• Adaptive technologies
• Adjust content to support efficient learning
• Enrollment management
• Recruiting and financial aid
https://www.newamerica.org/education-policy/policy-papers/predictive-analytics-higher-education/

Creating the Next in Education
Georgia Tech
• Future of Pedagogy
• Advances in “artificial intelligence, virtual reality,
data analytics, etc.”
• “[A]re starting to significantly supplement human
teaching, making many models of learning scalable
and repeatable.”
• Personalized Learning Systems
• Intelligent Tutoring Systems
• Data Mining and Learning Analytics
http://www.provost.gatech.edu/education-commission/discovery-reports/future-pedagogy

Exploiting Artificial Intelligence for
Personalized Learning at Scale
• Near Term (1–2 years): Mastery Learning and Adaptive Learning
Platforms
• Attempt narrow Bloom’s 2 sigma gap
• Leverage “’Jill Watson’, an AI agent designed to answer routine questions within
the class discussion forum”
• Medium Term (2–5 years): Personalized Learning and Multifunctional
Tutors
• “Given recent advances in AI, learning and cognitive sciences, and data science
and engineering, the time is ripe to build a multifunctional virtual tutor”
• “A multifunctional tutor will be able to personalize the different kinds of
learning assistance to each student as needed. “
• Long Term (3–15 years): Personalized Learning and Human-Centered
AI
• Human-centered, “multifunctional, virtual tutor fully capable of supporting
personalized learning at scale”
• The human-centered “AI agent builds, maintains, and uses models of humans
with whom it is interacting and thereby enhances the quality of the human-AI
interactions.”
http://www.provost.gatech.edu/sites/default/files/images/gtcne_report_supplement_-_ai_and_personalized_learning.pdf

IR Takeaway
There is a lot of interest in leveraging AI across higher
education
AI may play a role in almost all parts of the institution - need
to understand the data behind these implementations
AI could enable colleges of all sizes to enhance support for
student success

Ethical Considerations
• Have a Vision and Plan
• Build a Supportive Infrastructure
• Work to Ensure Proper Use of Data
• Design Predictive Analytics Models and
Algorithms that Avoid Bias
• Meet Institutional Goals and Improve
Student Outcomes by Intervening with Care
https://www.newamerica.org/education-policy/policy-papers/predictive-analytics-higher-education/

Datasheets for Datasets
Timnit Gebru
• Standardized way to document
• How and why a dataset was
created
• What information it contains
• What tasks it should and shouldn’t
be used for
• Whether it raises any ethical or
legal concerns
• AI governance
https://arxiv.org/abs/1803.09010
https://www.fastcodesign.com/90165544/why-designing-ai-should-be-more-like-building-hardware

Diversify AI’s Creators
Address bias and expand the scope of applications
https://www.fastcompany.com/40570350/5-ways-to-ensure-ai-unlocks-its-full-potential-to-serve-humanity
https://events.technologyreview.com/video/watch/tess-posner-ai4all-ai-bias/
https://blackinai.github.io/ http://www.latinxinai.org/ https://wimlworkshop.org/
http://ai-4-all.org// https://fast.ai/ http://ml4all.org/

IR Takeaways
IR can help start the conversation about
data/analytics/AI ethics
IR needs to be a part of ML/AI projects and be able to
access the data
IR can support diversity in our offices and connect
colleagues and students to resources

What does AI mean for Institutional
Research?

Understand, while we have
occasionally, like Google
Brain, done work where we
described the work itself as
working on machine learning
or AI.
For the most part, machine
learning, artificial intelligence
is like saying, Hey, we got
electricity in our stuff! Hey,
we have transistors in our
projects! -Yeah, so do we,
Astro.
… we see [ML] as an enabling
technology, and not the point
of what we’re doing.
Astro Teller, Alphabet's X

Value in the Benefit to Our Missions and Students

Highlight AI in IR Duties and Functions
Identify information and analytic/AI needs
Collect, analyze, interpret, report data, information, and analytic/AI
results
Plan and evaluate – include analytic/AI enhancement
Serve as stewards of data, information, and analytics/AI
Educate information and analytics/AI producers, users, consumers
Adapted from https://www.airweb.org/Resources/Pages/IR-Duties-Functions.aspx

Our role
• AI leaders, such as Jai Li, Google
Cloud AI, cite the importance of
collaboration with domain data
experts
• In higher education that includes
IR
• The AI space is still very new and
opportunities abound

What’s our moonshot?

AI Sharing Consortium
• Build on data sharing
• Overcome institutional size limitations to leveraging big data,
big compute, and big data algorithms
• Non-profit, mission driven support for student success
• Organize around student success topics, such as retention
• Leverage diversities across institutions
• Use transfer learning to apply the model back on campus

IR as the chief analytics officers for our institutions.
AI is the latest development or trend in analytics.
As stewards of data and information and as
educators of information producers, users, and
consumers, we need to develop our understanding
and thinking about AI to best advise and help our
institutions navigate a new and evolving landscape.

Definitions and distinctions regarding artificial
intelligence and related concepts from an institutional
research perspective.
Greater familiarity with applications of artificial
intelligence in higher education and related benefits
and concerns.
Considerations regarding future applications of
artificial intelligence and possible implications for
institutional research.

AI + IR: Artificial Intelligence and Institutional Research

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