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The Higher Education
Competitive Advantage: Accelerated Learning & Artificial Intelligence
Copyright 2002
July 18, 2002
Just as there is no area
of human endeavor that is unaffected by new knowledge, there is no area that is
unaffected by learning. If learning is
the principal skill of this knowledge age, then accelerated learning is the
competitive advantage. Accelerated
learning is the continuously increasing acquisition and use of new knowledge
with little or no loss of understanding.
The significant competitive advantage goes to those individuals and
institutions that have succeeded in accelerating learning. All institutions, especially those in
business, are affected by the speed of learning. Businesses that are quick to learn and use new knowledge have
definite competitive advantages over those that are slower. In this knowledge age, both wealth and
power come from accelerated learning.
The real question is
whether it is possible to accelerate human learning. Can we learn the same skills with the same or better
understanding in less time using new technologies and processes? The answer, as we shall discuss, is
definitely yes.
Accelerated learning will
inevitably change higher educational institutions as they prepare students to
learn “how to learn”. Certainly the
processes and technologies that enable accelerated learning will transform all
institutions and individuals. If
accelerated learning is the primary competency of this new century, then
educational institutions must adopt such techniques and processes, just to
survive.
It is not at all clear that
colleges and universities have accelerated the learning of their students. It is also not clear that colleges and
universities have taught and motivated students effectively to accelerate their
own learning. Assisting students in the
process of accelerating their personal learning has not been one of the
historical roles of higher education and will require radically new academic
strategies, processes and technologies.
The volume of new
knowledge is constantly accelerating.
There are a number of indicators showing the growth in published
knowledge. The increased number of
published journal articles, for example, demonstrates this. There has been a rapid increase in patents
and copyrights awarded. There were more
scientific papers published in the 1990s, for example, than in the 1980s, which
was greater than the 1970s, and so on.
The growth in PhD researchers, creating new knowledge, has
accelerated. There has also been a
growth in companies and institutions (e.g. higher education) that produce new
knowledge.
Knowledge, itself, has
caused the rapid spread of more knowledge.
New knowledge makes technology and processes possible that, in turn,
create further new knowledge. For example,
new knowledge was used to create advanced computer software and electron
telescopes that have, in turn, made it possible to learn about parts of the
universe that were never understood before.
Knowledge of computers,
software, telecommunications and the internet have clearly caused faster
availability of new knowledge. The
faster availability of new knowledge makes it possible to use that knowledge
more quickly to further create additional new knowledge. Discoveries happen more rapidly.
The most successful
learners spend less time accessing knowledge from libraries, for example, and
more time learning. This creates even
greater amounts of new knowledge that we then need to learn. This is the cycle of accelerated learning. Knowledge begets new learning and new
knowledge in a continuously faster cycle.
The advantage has always
gone to the fastest learners. In the
early days of our species the slowest learners died and the faster learners
propagated. Evolution has always provided
humans with the imperative to accelerate learning. Biological, genetic evolution occurred slower than the current
learning acceleration today. Since
information technology increases new knowledge, it also creates the imperative
to accelerate learning.
Perhaps it has only been
in the last century that the rate of learning acceleration has begun to reach
our human limits. Accelerated
learning has caused us to run into the limits of time, attention, energy,
resources, motivation, and memory.
There is some evidence that accelerated learning has also increased our
stress. We don’t have enough time to
learn whatever new knowledge we need to know.
Our time is fixed. We are
genuinely fatigued and stressed by the effort of constantly learning new skills
and knowledge. Our minds sometimes
don’t seem to want to pay attention, drifting to some other task. In short, we
need a new innovation that will greatly increase our learning speed as well as
our motivation, working memory, energy and so on.
What is learning? Although there are many different definitions,
the concept of learning can be found by examining experts in the area. We prefer a definition that includes a
change in a learner’s skills, knowledge, understanding and behavior, obtaining
meaning from experience and prior knowledge, constructing meaning pro-actively,
adapting to the environment, and setting personal goals. Certainly there is little doubt that
learning is also evolutionary. We use the
definition that learning is the act of gaining knowledge and understanding from
study, instruction and experience.
Learning has typically
been associated until recently with organisms, especially our species. Yet is it possible and practical that
machines can learn? While it is not yet
clear if every aspect of learning can be duplicated by machine, there is
already evidence that machines can learn and improve a new skill, can adapt to
a new environment, and actually respond in a manner that was neither
anticipated nor designed by their programmers and creators. It is not yet clear if they can change
their own “goal”.
Cognitive science has
already suggested ways that learning can be made more efficient. There is no evidence that college and
university administrators have learned from the cognitive scientists. Cognitive scientists have told us that
experts are those individuals who have the ability to manage cognitive
resources more efficiently
when solving problems in a knowledge domain.
They can reach their conclusions more quickly and with a greater
accuracy. There are known bottlenecks
that students have to negotiate to progress from basic to intermediate to
expert proficiency. Learning
efficiency is the speed with which we learn and are able to use some new
knowledge within a subject domain.
Learning efficiency is the critical factor in accelerating
learning. In other words, accelerated
learning depends upon speeding novice learners into experts.
It has long been known
from scholars that working memory capacity is a limiting factor in our ability
to process information. Working memory
includes the resources needed to remember the results of mental operations for
short periods of time after processing that information. Sometimes this is called short-term memory. It is a bottleneck in our cognitive
ability. The choke point in human
learning is working memory, which is quite small. Therefore we have
begun to depend to a greater extent on our information systems to remember and
expand our working memory. Perhaps
learning technology can be used to bypass or enhance working memory. For example, we already use the memory
feature on our phones to remember the telephone numbers of our friends and
family. This may be to “buffer” short
term knowledge that can be called at the time it is needed. Improving working memory will certainly
accelerate human learning if everything else remains even.
Colleges and
universities have and will continue to employ more efficient and effective,
machine assisted learning technologies and processes. Such conventional, state-of-the-art systems and algorithms do
not learn themselves, but assist teachers or learners. They may include, for example, multimedia
courseware, e-learning, distance learning, simulations, virtual reality,
automated assessment, etc. Learning
platforms today increase both the variety and combination of
learning formats (multimedia; audio, etc.) and provide the learner timely and
useful feedback and interactivity.
Today such systems provide remote learning opportunities when and where
a student is ready to learn and by providing faster access (e.g. a video of a
lecture; a book from the library, etc.).
Higher education has already
moved into machine-assisted-learning through web based enterprise-wide learning platforms such as
BlackBoard.com, WebCT.com, WebBoard.com, and Prometheus.com, (recently acquired
by BlackBoard from George Washington Universtiy). Current learning technologies are primarily teacher
driven.
Current learning technologies are not
capable of making any useful inferences to help the learner, only the
inferences programmed by the teacher/designer. They cannot create new rules for solving problems. The human teacher or tutor makes all of the
useful inferences and adds all of the rules that have been coded into the
algorithms. The human, either learner
or tutor, picks when, where, and how to learn.
There are skills and processes that can
increase the brain’s ability to learn faster such as memorization techniques,
action learning (by doing), and team learning. The manner in which learning rules in a specific discipline are
applied is the difference between an expert and a novice. For example, the expert in chess is able to
see the pattern of the board while the novice typically only sees individual
pieces. The expert knows which moves
are the most important while all of the moves to a novice have approximately
the same importance.
Today a person can play a game such as
chess, poker, hearts, pool, and so on, against some of the best human players in
the world anytime using current technology.
(e.g. Yahoo games). A person
can constantly play against very different players, some of them much better. The players are diverse, varying widely in
age, sex, ethnic background, etc. The real advantage of such technology is that
an individual can learn by doing (or in this instance, more precisely
playing). Computer networks make such
games possible by pitting remote players against each other. Furthermore, the network keeps track of
performance and permits an individual to learn from other more advanced
players. Games are much more than
entertainment. They can be serious case
studies to explore intelligence, learning, cognitive psychology, and human
interaction.
The merger of simulation
and virtual reality software with e-learning greatly increases the learning
speed for thousands or even millions of people. Virtual reality environments are already being used in higher
education. The best simulators are
designed to teach individuals basic skills (e.g. virtual laboratories) in a
highly interactive, entertaining and graphic rich, 3D multimedia
environment. They are much more
effective tools for learning complex skills, involving less cost, risk, and
time. As advanced as such systems are
however, the systems do not learn nor do they alter their own algorithms. Current learning technologies, no matter
how advanced, lack the ability to progress without human intervention…at least
for now.
The future is the
marriage of current learning technologies and artificial intelligence to
accelerate learning. Artificial
intelligence (AI) is the science of making machines do things that would
require intelligence if they were done by humans. AI is composed of a collection of different software and sometimes
hardware. AI software includes expert
systems, artificial neural networks, genetic algorithms, sensory capabilities,
natural language processing, data mining, machine learning, and others. One type of AI software is often used with
another. For example, natural language
processing is often used with artificial neural networks.
There are three
questions for this paper. Can AI be used to increase learning
effectiveness, i.e. improve the level of understanding for better use of that
knowledge? Can AI be used to make sure that a concept is fully understood
in all of its particulars so that it can be used more quickly? Can AI be
used to accelerate human learning and use? The answers to these questions are critical to the future of higher
education. The authors are suggesting
that it is probable that the answer to all three questions is yes.
This paper is not
intended to discuss every type of AI algorithm nor the vast array of potential
applications. The future of AI will
certainly take care of itself, so the big issues of whether conscious machines
can be built (Alekzander says yes) and whether machines will be more
intelligent than humans in this century (Ray Kurzweil says yes) are interesting
and legitimate questions for the philosophers but not for this paper.
The important point about
AI is that companies are now using it in real applications today and higher
education can also. Electric utilities
are using AI systems to diagnose power quality (PQ) problems to supplement
human expertise that is in short supply.
Several Internet retailers are using AI to monitor shoppers and make
recommendations for future purchases.
AI, in the form of an artificial neural network, is being used to find
the genetic mutations that create drug resistance in HIV, the AIDs virus. One AI process examines the genes of HIV
infected patients and determines the best mix of drugs for treatment. This application would be too lengthy and
too expensive to be performed by human beings but is only possible because of
the pattern recognition ability of AI, specifically artificial neural networks
(ANN). AI can perform pattern
recognition, e.g. recognizing a face or a voice or patterns in data. There are many, many examples.
AI can also generate
different “genetic algorithm mutations”.
A checkers program, for example, can try different moves and strategies
and play these against one another, learning better and better strategies. The program keeps the most successful
strategies and kills off the less successful ones. AI can quickly perform thousands or even millions of iterations
to find the best result.
AI
can amplify existing current learning technologies and therefore accelerate
student learning. AI can help students learn
faster and with greater understanding.
Students will be able to acquire needed skills, including critical
thinking skills, much faster. AI will
increase student motivation to learn.
The amount of learning proportionate to the amount of effort will
increase.
The AI advantage to faculty is that they will have
more time for higher level research and mentoring. AI might be used to construct 90% of the examinations and the
grading, for example. AI might be used
to determine which students are falling behind and suggest why. AI is already being used to help grade
essay examinations at the high school level.
AI can be used to detect
patterns and make inferences. AI
learning technology could adapt the learning environment to personalize the style
to the individual learner and adapt simulations based upon the conditions and
the learner’s style and behavior. For
example, AI could be used to evaluate a student in a human anatomy simulator. It might analyze whether the student can
identify each organ and maladies. It
may also be used to “read” and evaluate essays written by the student and
identify creative expressions. AI has
been used to create poetry, for example, that could not be differentiated from
poetry created by a typical student.
AI will not only improve science and technology, it will improve
humanities and the arts as well.
AI can
deduce "causes" of knowledge “that should exist”. For example,
when you observe a cup, you can deduce that it was made to hold some kind of
liquid or powder. If the causes are unknown then it can deduce that such
knowledge should be discovered. For
example, you observe an artificial object that you have never seen
before. You may deduce that it had a purpose, e.g. art, function, etc.
and then seek to learn what that purpose may be.
AI can also consider potential or future
"consequences" of some given knowledge. We observe leaves
falling from the trees, so we deduce that winter will shortly be coming.
We can further deduce that we should be prepared for the cold. AI can
infer such consequences based upon knowledge already possessed.
AI
can be used to interactively and continuously assess the learner’s progress
rather than waiting for a periodic exam or for the learner to ask for
assistance. AI can focus on a
particular learner in a personalized manner gathering much more feedback than
is possible with traditional learning processes. AI can restate the question to optimize learner feedback. Does the learner understand the
question? Does the learner understand
the concept? Does the learner follow
all the steps in the right sequence?
Does the learner make the correct inferences? For example, AI might assess a student more frequently when it is
obvious that a student’s learning performance is decreasing.
AI
could learn about the student and their learning goals. AI could keep track of the knowledge learned
through asking questions as well as unobtrusively monitoring
activity. Imagine a system that knows exactly what the student
learned and when and how well it was learned.
Just as the machine can deduce what knowledge it needs to know, it can
also deduce what knowledge the human is missing and what needs to be learned
next. AI can also be used to determine
the likelihood that specific knowledge will be needed in the near term future
based upon past experience. AI learns that once students learn one
area, they eventually will need to know another area.
AI could be used to
update, add to, and modify courseware.
For example,
·
Adapt testing and assessment tools to optimize
learning feedback.
·
Reinforce learning. When a student leaves the material for a while, AI could prompt
test questions and provide reminders to help the student get back up to speed
faster.
·
Make specific suggestions to the learner for
self-improvement.
·
Monitor student performance, learning patterns, and
recommend areas for learner improvement to a tutor or teacher.
·
Solve problems that the human programmer (teacher)
did not anticipate.
·
Optimize learning a specific narrow skill or group
of modules monitoring many learners, what is learned, how long learning takes,
and makes adjustments to speed learning.
·
Reduce time wasted on activities that do not result
in learning.
·
Observe and report the pattern of effectiveness of
different learning tools on different learning styles. It can vary the mix of how and when these
learning tools are used.
·
Learn from the real experience of learners. While an AI tool may not yet change it’s
own algorithm, it may suggest patterns to the programmer/teacher that might
result in new rules. Although the
learning rules do not change, the learning program can “learn from
experience”. Eventually it will be
possible to create programs that change their own algorithm, i.e. they will
learn how to learn.
Where can AI be applied
with the most efficiency and effectiveness to improve learning platforms? The most significant use of intelligent
machine assisted learning is whether it can solve learning problems and improve
learning processes that the learner,
teacher, tutor or programmer did not anticipate. It must solve learning problems that were never envisioned.
AI has already had a
number of successes in various fields of endeavor. It is very clear that AI has already solved problems that its
human programmers did not anticipate.
Unlike checkers programs that were infused with human expertise from the
very beginning, there has been work on a checkers program that learned to play
the game by itself. The program is
genetic in that it tries different winning strategies and plays these against
itself. Then the program takes the most
successful winning strategies and kills off those that were less successful. Many of the programmers were surprised by
the results, noting that it played a better game of checkers than they
did. Similar results were found in
other types of applications.
Currently there are very
few, if any, instances of the successful use of AI in learning and instruction
platforms. Artificial intelligence has
been under research and development for a generation or more but it has not yet
been truly adapted to the learning environment. Higher education institutions must now focus resources on
adapting AI to solve learning problems, some of which the teacher did not
anticipate or notice.
One possible application
of AI with machine-assisted-learning is to create a unique question and answer
administrator similar to an FAQ (Frequently Asked Questions)
administrator. This FAQ administrator
would really be an EAQ (Every Asked Question) administrator. This EAQ administrator would be a
sophisticated “help desk” for a particular course. The EAQ administrator might be the first step for students
obtaining answers to course questions.
It would maintain and update a list of the non-duplicated questions
asked of students in a particular course from any number of teachers and any
number of institutions. The FAQ administrator would refer ambiguity in
questions or answers to the teacher.
The FAQ administrator would analyze patterns of questions and use this
information to suggest areas to the faculty needing content or structure
improvement.
Today, the problem with
FAQ is that the learner has to search to find the same kind of question to see
if it has already been asked. This
takes valuable time. The EAQ
administrator would perform much of this function. The EAQ administrator would also be able to keep a catalog of all
unique questions asked, even those asked infrequently. The EAQ administrator would be looking for
patterns in student learning by which questions they ask, and when and how they
ask them. The EAQ administrator could
even develop better learning assessment tools based upon the questions asked.
AI can amplify highly
interactive, motivating, learning environments, enabling students to
continuously improve their critical thinking skills. For example,
AI might suggest that the student make a reasonable estimate of the answer
before solving a problem. Estimating is a very valuable critical
thinking skill. AI might prompt the student when to use that skill and
when not to use it, i.e. when an estimate is not going to be good enough.
In fact, AI is most suited to reasoning and thinking and therefore is more
suited to improving critical thinking skills than today's interactive but
preprogrammed systems.
AI might also be able to
be used to determine that a significant component is missing from the content,
observe personal learning patterns, recognize the learner’s voice and habits
(e.g. typing skill), determine when to seek more learner feedback, explain
learner errors, find, clarify and resolve ambiguity, and so on. AI might be used as an intelligent learning
scheduler to increase the number of students that can be effectively mentored
by a single tutor.
AI might be used to
effectively communicate between two totally different learning platform
environments. Sharable Content Object
Reference Model (SCORM) is being developed to provide standards for sharing
content among different learning platforms.
For example, a faculty member using WebCT might be able to use some
module created by a colleague on WebBoard.
While this will help cooperation and sharing, there are still going to
be issues of finding and linking modules on different learning management
systems and AI may be an effective supplement to this.
Institutions of higher
education must focus resources and research on Intelligent-Machine-Assisted
Learning. This is both the short term
and long term solution to achieve accelerated learning. It is also the most fertile area for a
university to achieve a significant competitive advantage.
It is certainly true that
AI will not be able to replace the teacher for the near-term future and,
perhaps never. However, AI can
effectively amplify the capability of the tutor, teacher and mentor and
therefore accelerate the learning of students.
AI can also be used to directly assist the learner with more effective
and efficient processes.
For the next couple of
years provosts and presidents will be dealing with budget cuts and accrediting
agencies that are emphasizing outcomes of teaching and learning. AI might be used to increase learning
outcomes without breaking the bank.
Certainly the cost of the AI technology itself would raise overall
costs. However, increased research
funding and increased student revenues may offset the AI costs. If a group of universities use AI to effectively
accelerate student learning, then they might have an attractive advantage over
other universities, everything else remaining the same.
It is in the interests of students to learn more
efficiently and effectively. Better
students may seek out universities with AI learning tools. AI might provide the competitive advantage
necessary to increase the quality of the students.
If AI does accelerate student learning, it would
become the most valuable competitive advantage. For example, if a student completes his/her education by taking
the same required credits faster than at another university, then the
university will be able to handle more students. Furthermore students would also be more satisfied. Lets assume that AI could increase learning
acceleration by 20% over the course of a college education. A student that might now may take 5 years to
graduate, might then take four years.
In the long term, universities without AI, will be
marginalized and begin to be less of a competitor among learning
institutions. A university is not only
competing against every other university but also against every other type of
higher learning opportunity.
Customers, including students, will inevitably use technologies that
improve their experience and increase their learning efficiencies and
effectiveness.
This technology will not
eliminate the need for faculty to teach.
Such technology, will change the teaching role of faculty more to
mentoring and coaching and away from lecturing. Faculty will necessarily become more involved with students and
their learning.
Accelerated learning will
eventually become the primary focus of all higher educational
institutions. In order to achieve
accelerated learning and remain competitive, colleges and universities must
invest, research, and develop learning platforms that integrate artificial
intelligence with the research findings of cognitive scientists. Cognitive science has already suggested
ways that learning can be made more efficient.
Artificial intelligence has progressed to the point where it can
automate some, perhaps many of the processes suggested by the cognitive
scientists. It is time to begin
incorporating the findings of both disciplines into the practice of higher
education.
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