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Artificial
Intelligence
What Is Artificial Intelligence?
At its core, artificial intelligence is the ability of a
computer or machine to learn from its environment
and make decisions based on the data it collects.
AI systems are designed to be able to process large
amounts of data quickly and accurately, allowing
them to make decisions faster than humans can. AI
systems can be used for a variety of tasks, such as
recognizing objects in images or understanding
natural language.
How Does Artificial Intelligence
Work?
Artificial intelligence (AI) is a rapidly growing field of
computer science that focuses on creating intelligent
machines that can think and
act like humans.
AI systems are typically composed of two main
components: an algorithm and a dataset. The
algorithm is the set of instructions that tells the
system how to process data and make decisions.
The dataset is the collection of data that the system
uses to learn from and make decisions based on.
For example, an AI system might use a dataset of
images to learn how to recognize objects in photos.
Types of Artificial Intelligence
Reactive machines
Reactive machines perceive present external
information and plan actions accordingly. The
machines perform specialized duties and only
understand the task at hand. The machines’ behavior
is consistent, given a repeated situation. In the
1990s, IBM developed a reactive machine
named
Deep Blue to play competitive chess, predicting chess
moves by identifying each piece’s board placement.
Limited memory
Limited memory machines can harness recent
observations to make informed decisions. The
machines consider observational data in reference to
their pre-programmed conceptual framework. The
observational data is retained for a limited period
and then forgotten.
Theory of mind
Theory of mind machines can form thoughts and
make decisions in reference to emotional context; thus,
they can participate in social interaction. The
machines are still in the development stage;
however, many exhibit aspects of human-like
capability. For example, consider voice assistant
applications that can comprehend basic speech
prompts and commands but cannot hold a
conversation.
Self-awareness
Using artificial intelligence in education
The potential of using artificial intelligence in
education to enhance learning, assist teachers and
fuel more effective individualized learning is exciting,
but also a bit daunting. To even have an intelligent
conversation about AI in education, one must first
push past imaginary science-fiction scenarios of
computers and robots teaching our children,
replacing teachers and reducing the human element
from what is a fundamentally human activity.
One of the leading writers on the benefits of artificial
intelligence in education, Matthew Lynch (“My Vision for
the Future of Artificial Intelligence in Education”), is careful to
explore the potential pitfalls along with the benefits,
writing that “the use of AI in education is valuable in
some ways, but we must be hyper-vigilant in
monitoring its development and its overall role in
our world.”
Benefits of AI in Education
Ideally, writes Lynch in The EdAdvocate, “AI does not
detract from classroom instruction but enhances it
in many ways.” He summarizes five intriguing
potential pluses of integrating AI in education:
Personalization:
“It can be overwhelmingly difficult for one teacher to
Tutoring:
AI systems can “gauge a student’s learning style
and pre-existing knowledge to deliver customized
support and instruction.”
Grading:
Sure, AI can help grade exams using an answer
key; but it can also “compile data about how
students performed and even grade more abstract
assessments such as essays.”
Feedback on course quality:
For example, if many
students are answering a
question incorrectly, “AI can zero in on the
specific
information or concepts that students are missing,
Examples of AI in Education
Inspired by a challenge from “an old school teacher
who thinks that AI is ruining education,” Matthew
Lynch reviews a wide range of topics in a piece
titled “26 Ways That Artificial Intelligence Is Transforming
For example:
Adaptive Learning: “Used to teach students basic
and advanced skills by assessing their present skill
level and creating a guided instructional experience
that helps them become proficient.”
Assistive Technology: AI can help special needs
students access a more equitable education, for
example by “reading passages to a visually
impaired student.”
Early Childhood Education: “AI is currently being
used to power interactive games that teach children
basic academic skills and more.”
Data and Learning Analytics: “AI is currently
being used by teachers and education
administrators to analyze and interpret data,”
enabling them to make better-informed decisions.
Scheduling: Helping administrators to schedule
courses and individuals to manage their daily,
weekly, monthly or yearly schedules.
Facilities Management: AI is effective at
“monitoring the status of power, Wi-Fi and water
services; alerting the facilities management workers
when problems arise.”
Overall School Management: AI is currently being
used to manage entire schools, powering student
records systems, transportation, IT, maintenance,
scheduling, budgeting, etc.
Writing: Not only does Lynch assert that AI is
already at work helping students improve their
writing skills, he confesses, “I am currently using a
grammar and usage app to help me write this
article.”
Running down his list, Lynch also cites current
uses of AI in education that include:
- Classroom/Behavior Management
- Lesson Planning
- Classroom Audio-Visual
- Parent-Teacher Communication
- Language Learning
- Test Prep
- Assessment
- Learning Management Systems
- Gamification for Enhanced Student Engagement
- Staff Scheduling and Substitute Management
- Professional Development
- Transportation
- Maintenance
- Finance
- Cybersecurity
- Safety and Security
Examples of
how artificial intelligence is currently being used in higher education
include:
- Plagiarism Detection
- Exam Integrity
- Chatbots for Enrollment and Retention
- Learning Management Systems
- Transcription of Faculty Lectures
- Enhanced Online Discussion Boards
- Analyzing Student Success Metrics
- Academic Research
- Connected Campuses
AI in Education [Inclusion and
Universal Access]
Bernard Marr explains that AI tools can enhance
inclusion and universal access to education in a
number of ways, including:
Helping to “make global classrooms available to all,
including those who speak different languages or
who might have visual or hearing impairments”
Overall, it is hoped that AI will ultimately help
educators make continued progress in addressing
the broad range of physical, cognitive, academic,
social and emotional factors that can affect student
learning and ensure that all students have equal
opportunity in education, regardless of their social
class, race, gender, sexuality, ethnic background
or physical and mental disabilities.
AI in Education
[Individualized Learning]
There is also considerable optimism around the
idea that, as artificial intelligence becomes a more
integral part of the classroom, teachers will be
better equipped to offer an individualized learning
experience for every student.
According to an article in The Atlantic, (“Artificial
Intelligence Promises a Personalized Education for All”),
artificial intelligence holds the potential to “enhance
human teachers’ abilities to tailor lessons to each
student without knocking their class schedule off
track,” eliminating the need for educators to “teach
to the middle,” as often happens when their
students have a range of skill levels and learning
abilities.
Rose Luckin, a professor of learning-centered
design at University College London, is quoted as
saying that, “The real power of artificial intelligence
for education is in the way that we can use it to
process vast amounts of data about learners,
about teachers, about teaching and learning
interactions.” Ultimately, AI can “help teachers
understand their students more accurately, more
effectively.”
Artificial intelligence in education?
At its most basic level, AI is the process of using
computers and machines to mimic human
perception, decision-making, and other processes
to complete a task. Put differently, AI is when
machines engage in high-level pattern-matching
and learning in the process.
There are a number of different ways to understand
the nature of AI. Two types of assessment include
rules-based and machine learning-based AI. The
former uses decision-making rules to produce a
recommendation or a solution. In this sense, it is
the most basic form. An example of this kind of
system includes an intelligent tutoring system
(ITS), which can provide granular and specific
feedback to students.4
Machine learning-based AI is more powerful since
the machines can actually learn and become better
over time, particularly as they engage with large,
multilayered datasets. In the case of education,
machine learning-based AI tools can be used for a
variety of tasks such as monitoring student activity
and creating models that accurately predict student
outcomes.
While machine learning-based AI is still in its
infancy, the approach has already shown impressive
results when it comes to complex solutions not
governed by rules, such as scoring students’ written
responses or analyzing large, complex datasets.
Within AI, there are other important distinctions,
largely based on the technological use cases.
One subfield revolves around natural language
processing, which is the use of machines to
understand text. Technology such as automated
essay scoring uses natural language processing
to grade written essays.
Also important within AI are recommender and
other prediction systems that engage in data-driven
forecasting. For example, Netflix currently uses an
AI-based recommender system to suggest new films
to its users.
Vision-based AI is also an important field that can
help with assessment. A number of assessment
groups have used optical systems to grade students’
work. Instead of a teacher grading a math equation
that a student wrote, for example, the teacher can
snap a picture of the equation, and a machine will
grade it.
Finally, there are AI systems based on voice
recognition. These systems are the backbone of
tools such as Siri and Alexa, and experts have been
exploring ways to use voice-based AI to diagnose
reading and other academic issues.
Despite the innovation that AI supports in
assessment, concerns around bias may prevent
some of these designs from seeing the light of day.
This issue brief will discuss those concerns.
Who is using AI?
Uses of AI in education expand beyond student
assessments and into other tools to support student
learning, often using built-in stealth assessments
that students do not even recognize as a test.
For example, researchers at Carnegie Mellon
University’s Human-Computer Interaction Institute
developed new ways to use rules-based AI through
intelligent tutoring systems.5 Their method allows
students and teachers to create tutors by entering
problems and showing the ITS how to solve them.
Once learned, the computer applies the solution; if
incorrect, the human can fix it.
Thereafter, the computer continues to build the
rules, making the machine capable of applying
solutions to other problems. This feature makes
the tool much faster at building the tutoring system
because humans no longer need to build the rules
in the system. For example, a teacher can build a
30-minute lesson in about 30 minutes—all through
a free tool.6 These systems are much more scalable
than human-based tutoring, providing students with
one-on-one support.
Today, the use of machine-based AI is already fairly
widespread in education. For example, several
testing companies, such as the Education Testing
Service and Pearson, use natural language
processing to score essays. Massive online open
courses allowing unlimited participation through
the web, run by companies such as Coursera and
Udacity, have also integrated AI scoring to analyze
essays within their courses. Most states also
currently use natural language processing to score
the essay portion of their yearly assessment.
Such technology can also be used to drive down
the cost of assessment. Using a mix of machine
learning and natural language processing, several
experts such as Neil Heffernan at Worcester
Polytechnic Institute are looking at ways to
automatically generate new, high-quality test
items around a body of knowledge.
Heffernan calls the items “similar but not the same,”
and he argues that they are key in truly
understanding if a student understands a domain.
In some cases, experts believe that machines will
soon be able to generate assessment questions
that are personalized to a student’s interests.
For a student who loves baseball and is learning
the concept of 5 plus 3, the machines might
generate a problem about baseball (for example,
“The batter hit five line drives and three home runs.
How many total hits did they have?”). These efforts
on item generation also have the benefit of driving
down the costs of assessment.
While natural language processing does not
“understand” language in any technical sense,
it can be used to evaluate the quality of essays in
ways that make formative assessment much more
powerful. For instance, most word processing and
email programs use natural language processing
to suggest greetings or specific words.
Commercial products such as Grammarly also use
natural language processing technology to act as a
virtual writing assistant. These approaches are
particularly important when it comes to improving
formative assessment, and one of the authors of
this issue brief has a forthcoming tool that will
automatically evaluate a student summary of a
reading assignment. Other organizations such as
Revision Assistant and MIWrite also use natural
language processing to evaluate the quality of
argumentative essays.
When it comes to recommender systems, one use
case is credit transfer. Researcher Zachary Pardos
has created recommender systems that help
students transfer credits from community colleges
to four-year colleges. Another use case is
recommending instructional practices after an
assessment. For instance, a recommender system
would outline a specific instructional path for a
student to take after an assessment. This is
important given the often limited practical utility of
many end-of-year state exams.
Such predictive systems, also known as early
warning systems, can help track students who are
in danger of weak academic performance. About
half of public high schools and 90 percent of
colleges use an early warning system to track
student grades, attendance, and other factors to
identify when students veer off track.11 These
systems are powerful because they can rely on
other performance data—such as attendance—to
predict student success, allowing counselors and
other faculty to intervene early.
The benefits and challenges of AI
Artificial intelligence can help students learn better
and faster when paired with high-quality learning
materials and instruction. AI systems can also help
students get back on track faster by alerting teachers
to problems the naked eye cannot see.
In some cases, such as automated essay scoring,
teachers and students do not directly experience
the benefits of the tools. Rather, the state grades
the exams in a faster, more efficient manner. In
other cases, teachers are the direct beneficiaries.
Scholars, such as Scott Crossley at Georgia State
University, are experimenting with ways that natural
language processing-based assessments can be
embedded into writing programs so that teachers
can get data reports on their students’ writing
quality.
Despite these benefits, there are clear concerns.
One major issue is around privacy. How do these
tools protect user privacy? How do schools gain
consent of both students and parents when
introducing them? Should data that have been
anonymized, be shared with researchers and other
external groups?
Another issue is the value of social and emotional
ties and the very human experience of education.
Put simply, AI will not replace teachers. Experts also
point to bias as a drawback of AI. Scores computed
by machines will be based on the results of
thousands of tests. But as noted in this issue brief,
test results can more often reflect a lack of
opportunity rather than lack of ability. Machine
scoring will not be able to make these distinctions.
Artificial Intelligence in the
education sector
There are many ways to apply AI within the education
sector with the most common applications being:
Personalized learning. With AI, learning can be
tailored to specific student abilities, knowledge
levels and interests, resulting in students feeling
more comfortable, motivated and engaged.
Content creation. AI can analyse one topic and
then break that down into more bite-sized learning
formats to accommodate different learning styles.
It can also help with information visualization
through simulated content or even gamification,
designed to suit many different learning
preferences.
Task automation and admin assistance. From
lesson planning to grading homework and exams,
AI can be harnessed to help teachers cut down on
the time it takes them to complete tasks, so they
have more time to spend with their students.
The advantageous side of
artificial intelligence in
educational sector
The bottom line
Thus, artificial intelligence in the school management software
has enormous advantages. But this is not the end
of the show because there are even more
advancements that can come in this technology.
There is an alert of never-ending inventions and
innovations that are on the rise.
Not just AI but even various other new-age
technologies such as augmented reality or visual
reality can also be implemented in the educational
sectors. The managements have to understand that
technologies can be used in the best form to yield
greater results.
AI and Testing:
How to reap the benefits of AI to improve
testing
Three steps will get educators and students
closer to reaping the benefits of AI and its uses
in student assessments.
First, Congress must invest in research to better
understand where and how bias occurs in
testing. Test results should be fair and accurate
reflections of what students know and can do
against a common and fair measuring stick.
But when test results consistently exhibit racial
patterns—and do not reflect true differences
between the groups—they are biased. Bias
could occur in what is being measured or in how
it is being measured and scored. Research can
point to where in the testing process bias is
occurring and help discover remedies.
Second, Congress should invest in the
development of new kinds of technology-driven
assessments. Given the size and scale of
investment needed, this can only come from the
federal government. Thus, Congress should
provide additional funding to states for testing
and related research and development on
cutting-edge technology such as AI-based tools,
learning games, and virtual reality. This could
take the form of increased funding for the Grants
for State Assessments and Related Activities
program in the Every Student Succeeds Act.
Congress should also increase funding of a
little-known program called the Small Business
Innovation Research program, which provides
up to $1.1 million in individual grant awards to
develop education-related learning
technologies. Congress should also orient this
program to have more of a focus on assessment
strategies rather than general education
technology.
Artificial intelligence and assessment:Artificial intelligence can help students learn better
and faster when paired with high-quality learning
materials and instruction.
Vision-based AI systems can also help with
assessment and are being rolled out in a number
of areas. Assessment groups such as Pearson
have used optical systems to grade students’ work,
and some, such as the team at the education
technology firm Bakpax, envision a world in which
teachers use the camera on their cell phones to
take a picture of a child’s homework, which is then
automatically graded.
Finally, there are AI systems based on voice.
These systems are the backbone of tools such as
Siri and Alexa, and experts such as John
Gabrieli,
a neuroscientist at the Massachusetts Institute of
Technology,
and Yaacov Petscher, a professor at
Future Impact of Artificial Intelligence
Rapid advances in artificial intelligence will result
in a profound impact on productivity, employment,
and competition. However, AI’s future integration
into society is a controversial subject.
Impact on productivity
Increasing economic productivity leads to more
satisfied customers and strengthened corporate
profitability. In the airline industry, AI will drive
customer satisfaction through accurately scheduled
and safer flights. Businesses that harness AI can
improve their value proposition to customers while
improving profitability at the same time.
For example, Delta Airlines leverages machine
learning to provide its customers with a superior
flying experience. Delta analyzes Big Data to learn
about aircraft positions, weather conditions, and
aircraft diagnostics. Hypothetical outcomes and
their probabilities are then identified. The airline
then optimizes its flight scheduling regarding the
potential outcomes.
Impact on employment
Rapid expansion in the artificial intelligence field
will result in more high-paying jobs, which, in turn,
will require more highly-educated employees. The
largest criticism of AI is that it will automate low-
skill jobs and increase the unemployment rate for
less-educated people.
Furthermore, low-skilled employees are more likely
to be minorities due to systematic discrimination.
It is, therefore, argued that artificial intelligence
could reinforce systematic discriminatory practices.
Impact on competition
Companies attempting to achieve a competitive
advantage can leverage AI to optimize their
business. Currently, only innovators and a few
early adopters are integrating AI into their
businesses. Once the economic benefit of AI
integration outweighs the R&D and integration
costs, more companies will adopt the new
technology.
Assume a toy manufacturer decides to use
machine learning to optimize its supply chain
further. The manufacturer can take advantage
of its lowered cost of goods sold and offer toys at a
lower price point to capture market share. This
demonstrates how AI can increase competitive
behavior in the marketplace.
Artificial Intelligence in school
management practices:
An example
Outside of the classroom, AI can help inform school
management decision making, paving the way for
more structure and measured business growth.
Here’s a real-life example of how Artificial
Intelligence can streamline processes as well as
boost the accuracy and efficiency of a school’s
financial operations.
The Cathedral School of St Anne
and St James
The Cathedral School of St Anne and St James
is an independent Anglican co-educational school
in Townsville suburb Mundingburra. With the school
employing over 300 staff, they focus on best
practice and working as a team to provide the best
learning experience to all students. With this high
engagement and attention expectations, the
leadership team thrive to ensure administration
efficiency and data analytical solutions to drive
strategy.
For The Cathedral School, the management and
analysis of school data was extremely time
consuming and tended to require more technical
expertise. While there are a few school management
solutions available in the market, most lack the
analytical capabilities required to truly streamline
operations.
Recognising the value of AI in school management
practices, Sonya Chun Tie, the school’s business
manager, sought more information about how it
could be used to modernise the school’s financial
operations, which led her to
our door.
How Findex helped
The Findex Data Science team had already
developed an automated portal which manages and
integrates school-related data. By tapping into the
functionality of this portal, Findex was able to offer
The Cathedral School a centralized system that
provides real-time data visualizations, benchmarking
and forecasting.
Data Science with Findex
Our in-house team has extensive experience in
designing and constructing Analytical Data Models
(ADM) from multiple, and often complex, data
sources to support effective decision making and
to deliver actionable
insights on demand.
Our solutions not only leverage financial data but
also integrate non-financial data inputs such as
student academic performance and curriculum
related data in combination with external metrics
such as suburb profiles and demographic
information, to give a full picture of what is
happening, why it is happening and what this
means for the future. Ultimately, we help our
clients to enable fact-based decision making,
in turn unlocking
value back to the business.
If you’d like to find out more about how Findex can
help your school streamline its processes and improve
financial operations through the power of AI, you
can request a portal demo today.
Conclusion
Well-designed formative assessments that take
advantage of the latest advancements in
technology can help students learn faster and
better. These mechanisms are also a critical part
of the teaching and learning process.
From intelligent tutoring, stealth assessments,
games, and virtual reality, mini-tests built by
artificial intelligence can provide a wide variety of
ways to use this technology to build engaging
tools.
To get there, the education system needs stronger
investments in the research and development of
new testing technologies that can provide teachers
and students with the tools they need.