Ai in Learning Thought Paper

Center of Excellence
Thought Paper
Artificial
Intelligence
in Learning
Accenture Capability Nework

Copyright © 2018 Accenture All rights reserved. 2
Unmanned
Service
Smart Parking
Google Waymo
Ecommerce:
Amazon
Grid
Healthcare: Watson
Home Security
Banking and Insurance
Unmanned
Delivery
Google
Now
Alexa
Netflix
DominoesCortana
Siri
P
A DAY IN OUR LIVES…
Copyright © 2018
Accenture All rights reserved.

Copyright © 2018
Accenture All rights reserved.
A DAY IN OUR LIVES…WITH AI
P
Service Robots
Robotic / Smart
Parking
Self Driving Cars:
Google Waymo
Predictive
Ecommerce:
Amazon
Smart Grid
Predictive
Healthcare: Watson
Home Automation
and Security
Banking and Insurance
Drone Delivery
Google
Now
Alexa
Netflix
DominoesCortana
Siri

ARTIFICIAL INTELLIGENCE IS A REALITY
You may not realize it,
but Artificial Intelligence
IS ALL AROUND US

Artificial Intelligence is the science of making a computer, a computer-controlled robot, or a
software thinking intelligently, in the similar manner the intelligent humans think.
WHAT IS ARTIFICIAL INTELLIGENCE?
ARTIFICIAL INTELLIGENCE
ARTIFICIAL
INTELLIGENCE
COGNITIVE SKILLS
Learning and
understanding
Applying reason and logic
Adapting to situations
Problem-solving
Decision-making
Communication
Artificial Intelligence is the capability of a
machine to imitate intelligent human
COGNITION.

The AI market has grown tremendously in last few years. Experts forecast that annual global
revenue from AI products and services will grow from $643.7 million in 2016 to $36.5 - $100
billion by 2025. Here’s a look into what lies ahead for AI:
AI MARKET GROWTH
By 2018, 62% fast
growing companies
are expected to use
AI, while only 38% of
the companies used
machines in 2017.
62%
By 2020, Gartner
claims that 85% of all
customer interactions
would be mechanized
minimizing the need
for customer service
agents.
85%
By 2025, the AI
software market is
estimated to be
worth a whopping
$89.8 billion
globally.
$90 bn
By 2018, more than 3
million workers
globally will be
supervised by a
“robo-boss.”
3 mn

ARTIFICIAL INTELLIGENCE IS A COLLECTION OF MULTIPLE TECHNOLOGIES
Natural
Language
Processing
Virtual
Agents
Robotic
Process
Automation
Machine
Learning
Deep
Learning
Predictive
Analysis

MACHINE LEARNING
Machine learning is the ability of computers or algorithms to interpret
ambiguous data, such as text, speech, visual, and digital inputs, and
analyze them for meaningful insights.
Machine learning can help in predicting and making
recommendations, detecting anomalies, performing classification,
ranking, and decision making.
Netflix is using machine learning for its video recommendation engine to personalize as much of Netflix as possible
and match consumers to their content.
It is estimated that the recommendation system has significantly increased customer retention and resulted in
savings of about $1 billion a year for the company.

DEEP LEARNING
Deep Learning allows a machine to process large datasets and recognize
patterns for decision making using methods that are similar to human
brain’s neural networks.
Deep learning is used by organizations to analyze and predict online
behavior, organize information, predict outcomes, and boost the
accuracy of other AI technologies, such as image analysis, face
recognition, handwriting recognition, and speech recognition.
PayPal is currently using deep learning to combat fraud and manage risk. The company gathers huge quantities
of data about its customers, including financial data, network information, and machine information and then feeds
this data to deep learning algorithms to spot patterns, fix glitches, and detect frauds.

VIRTUAL AGENTS
Virtual agents are conversational, computer-generated characters or
programs that replace the online customer service representation by
responding to customer queries and questions as well as updating basic
account information in the system.
It delivers voice and text information to the users via a web browser,
kiosk, or mobile interface for resolving their problems.
Amelia is a virtual agent avatar that redefines how enterprises operate by automating and enhancing a wide
range of business processes.
Amelia interfaces with consumers in astoundingly human terms, parsing questions, analyzing intent, and even
sensing emotions to resolve issues more efficiently and effectively than customer service representatives.

ROBOTIC PROCESS AUTOMATION
Blue Prism is an enterprise RPA software to help organizations eliminate low-return, high-risk, manual data
entry and processing work. Blue Prism helps organizations in deploying software robots to:
• Build an agile digital workforce
• Increase productivity with less resources
• Improve cost savings and accuracy
• Provide superior customer experience
Robotic Process Automation (RPA) uses software to automate
manual, time-consuming, or routine processes, which can help in
reducing costs, reducing errors, improving efficiency, and
increasing employee and customer satisfaction.
Once instructed and configured, Robotic Process Automation tools
can process transactions, manipulate data, and communicate with
external systems in an autonomous way.

NATURAL LANGUAGE PROCESSING
Natural Language Processing is a method of interacting with
intelligent systems, using a natural language such as English, to
enable them to perform tasks as per instructions.
This technology analyses text to interpret sentence structures, their
meaning and intention, using statistical methods and machine
learning.
Siri is a voice-activated intelligent personal assistant developed by Apple that uses natural language processing
to answer questions and perform actions.
It is capable of voice interaction, music playback, making phone calls, sending messages, setting alarms and
reminders, streaming podcasts, playing audiobooks, scheduling meetings and appointments, and providing
weather, traffic, and other real-time information.

PREDICTIVE ANALYTICS
Predictive analytics is the science of getting computers to predict
and provide recommendations based on previous trends without
being explicitly programmed.
It uses various statistical modelling and machine learning techniques
to analyze historical data and predict future outcomes.
Amazon uses browsing and purchasing history of customers to make recommendations of products that they
may be interested in buying in future. Predictive Analytics helps amazon to:
• Know what the customers are most likely to buy in advance
• Determine the highest price a customer will pay for the products
• Target recommendations and promotions
• Improve supply chain management

AI USE CASES BY INDUSTRY (1/2)
An advertising giant M&C Saatchi
collaborated with Clear Channel
and Posterscope to develop an
intelligent poster campaign. The
poster uses a body-tracking
technology to analyze the people
standing in its vicinity and
assesses up to 12 people at a
time. It uses a combination of
pictures from the “gene pool” to
create and access ads that are
likely to be successful.
Marketing and Advertising
Banks are combining AI
technology with traditional
processes to cut costs and
increase service efficiency.
AI technologies currently
utilized in financial services
include data mining, NLP,
Machine Learning, and text
analytics. The intelligent
engine Wallet AI helps
customers make informed
financial decisions.
Finance
A law firm international city
lawyers BLP uses AI to
capture the details of a
property from a Land
Registry title deed. Major
firms employ e-discovery
systems to sift through
documents, e-mails and
records to identify material
that could be relevant to a
litigation.
Law

AI USE CASES BY INDUSTRY (2/2)
AI is being extensively used in
patient care and diagnostic
systems. For example, IBM
Watson Oncology specializes in
developing patient-centric
treatment plans. In Boston
Children’s Hospital, clinicians
feed genomic data into IBM
Watson to develop a cognitive
system for interpreting a child’s
genome sequencing data.
Healthcare
The AI-powered “Cognitive Tutor”
in the education industry can
monitor and track learner’s
progress and detect the extent of
the learner’s understanding on
the content. Another example is
Cram101, which is an innovative
learning solution that converts
textbook content into interactive
content with chapter summaries,
practice tests, and flashcards.
EducationRetail
The IBM Watson AI provides data
analysis and visualization solution
for analyzing unstructured data
and providing customer insights
to retail organizations. Also, an AI
platform known as inSTREAM can
decipher patterns in unstructured
data, which can be applied to
customers’ emails for better
understanding of their sentiments
and improve customer service.

HOW WILL AI BE PLACED IN LEARNING?
As organizations are now transitioning from traditional learning to new age digital learning solutions, it is critical for
the L&D function to adopt AI in developing learning strategies.
AI can provide insights based on the data it gathers
and analyzes, which will enable organizations to
develop a better understanding of learner
behaviors and predict their needs to recommend
content.
It can also automate various processes, and
provide algorithm-driven design and
development.

AI CAN PROVIDE THREE LEVELS OF INTELLIGENCE TO LEARNING SYSTEMS
Assisted
Intelligence
AI assists humans to perform some of their tasks faster and without errors.
Assisted Intelligence aids in improving existing manual tasks, requires constant
human intervention, and works well with defined inputs and outputs.
Example: Automated grading for almost all types of assessments, automated
learning content authoring, LMS uploading, and publishing of courses reduce the
time and effort required.
Augmented
Intelligence
AI enhances human intelligence rather than simulating independent intelligence.
Augmented Intelligence can help employees to perform their tasks faster and better
by enabling them to access knowledge resources and collaborate with fellow
workers, experts and databases using AI techniques such as Machine Learning,
Natural Language Processing, Image Recognition etc.
Example: Machine-assisted solutions – eg. voice-driven and image-recognition-
based performance support tools for servicemen in their field tasks.
AI is developed for the future generation.
Autonomous Intelligence establishes machines that perform in place of humans.
Example: This field is still being researched upon and applications of Autonomous AI
in learning is a future view.
Autonomous
Intelligence

Intelligent
Automation
Use of AI to
combine cognitive
capabilities such
as learn, reason,
solve problems,
and make
decisions with
automation
technologies for
streamlining
complex and/or
routine processes
and deriving
solutions.
Enhanced
Judgement
Use of AI to
support human
intelligence by
analyzing,
evaluating, and
recommending
options for
consideration and
informed decision
making.
Leveraging AI to
bring together
people, devices,
and services. It
provides a
personalized user
experience by
analyzing their
needs and
assisting them by
providing real-
time information
or performing
tasks.
Intelligent
Products
Use of AI to
develop new and
innovative
products and
services that can
sense,
comprehend, act,
and learn based
on situations to
accelerate growth
of an organization.
Enhanced
Trust
Use of AI to drive
compliance,
transparency, and
trust within and
outside an
organization. It
ensures that
employees,
customers, and
other stakeholders
believe in the
benefits of the AI
implementation.
ENTERPRISE VALUE CREATION LEVERS OF AI
Enhanced
Interaction
i

ENTERPRISE VALUE CREATION LEVERS IN LEARNING
Auto-
authoring
Curriculum
Calibration
Intelli-Device
Mentoring
Gamification
Learner Profile
Modelling
AI can enhance the effectiveness of Corporate Training Programs through:

INTELLIGENT AUTOMATION – AUTO-AUTHORING
AI technologies can enable machines to apply cognitive skills, such as learning, reasoning, and
decision-making for automating content design and development. Algorithm-driven design and
analysis engines will help in gathering, analyzing, building, and reviewing content faster, thereby
reducing the cost of development by 80-90%.
How does it happen? What does the future look like?
AI machines can curate and create variations of training content and
test questions to cater to specific learning needs of different roles
using learning materials repository. Another application of AI in
learning is the automatic translation of content in other languages.
Example: IBM’s Watson Content Analytics can gather and analyze
unstructured content in documents, emails, databases, websites, and
other enterprise sources to derive unique relationships. Another
example is the Wordsmith’s platform that uses AI to convert data into
logical coherent text using a template and a structured data set.
In the future, AI is most likely to generate content from a set of rules
and present it in a logical form. It can develop a draft storyboard from
a set of compliance regulations, which can be reviewed and revised as
appropriate by an Instructional Designer.
With the advancement in AI capabilities, it may be possible to provide
content suggestions, fill in content gaps, suggest objectives, create
concept maps, and also create content on its own.
Organizations will benefit from AI as the content creation will be less
expensive, less time consuming, and more accurate.
Auto-authoring

ENHANCED JUDGEMENT – CURRICULUM CALIBRATION
E-learning giants and corporate content experts need solutions that can address the
current and future learning requirements of their participants. AI technologies can be used
to analyze and correlate large volumes of data, such as user preferences and usage
patterns, and draw inferences for developing a comprehensive and effective curriculum.
For an organization with a large number of employees, modifying and
updating the curriculum on a regular basis to match their learner’s
needs is a tedious and time consuming process. It requires a lot of
data to be captured and analyzed and is often lesser prioritized as a
result. AI teaching software can identify areas where participants are
deficient and thereby recommend such areas for curriculum
calibration.
Example: xAPI makes training activity data of learner actions from
desktop browser and mobile space readily accessible.
With the use of right AI algorithms, machines will likely be able to
analyze emails and voice calls to derive insights such as required
competencies or performance gaps in a target audience. This will
become a crucial input for curriculum designing.
Expert systems will be able to examine training activity data, identify
weak spots in a course or curriculum, suggest improvements, as well
as make automatic adjustments to ensure effectiveness of a training
program.
Curriculum
Calibration

ENHANCED INTERACTION – INTELLI-DEVICE MENTORING
AI has made accessibility and scalability a reality in global classrooms. The human-
machine interface of AI has the potential to change the way we access, learn, create, and
retain information. AI introduces a two-way relationship between the learning system and
the participants by implementing intelligent methods for analyzing and assessing
knowledge and abilities as well as conducting surveys and interviews.
AI is also used as conversational agents or chatbots to engage
participants in discussions amongst themselves or with the instructors
and supervisors, which promotes healthy collaboration.
AI can also provide just-in-time support to trainers while delivering
content.
Example: Third Space Learning app uses analyzed audio and written
data to identify qualities of a good teacher. The app can provide real-
time instructions if the tutor deviates from the topic or speaks too fast.
AI is likely to assist tutors in class-room and virtual trainings by
answering questions that require high-order thinking and creativity as
well as hold a conservation. AI will soon help in presenting content,
monitoring responses to evaluation questions, and determining the
best path to follow toward the mastery of a subject.
Intelli-Device
Mentoring

INTELLIGENT PRODUCTS – GAMIFICATION
Game-based motivation, including competitions, performance rewards, and recognition
increases productivity for 90% of workers. Research proves that learner retention can be
up to 9% more using game mechanics. Gamification is the use of game mechanics to
simplify and improve learning, develop skills, edify behaviors, or to increase people
engagement in an organization. Most businesses have begun identifying opportunities to
leverage gamification as an intelligent product for accelerating the growth of the
organization.
AI plays a crucial role in designing strategic games for learning
content as it enables higher retention and recall. Some innovative
ways of using AI in gamification design are power balancing,
difficulty level adjustments, and tailored feedback mechanisms.
Example: Google’s artificial intelligence unit, DeepMind, developed
a software program called AlphaGo to take on a human challenger
in Go chess game. This program uses a Monte Carlo tree search,
which involves sampling the most promising moves and running
through different scenarios.
The Gamification 2020 report by Gartner predicts that gamification
combined with other emerging trends and technologies will have a
significant impact on Innovation, design of employee performance,
globalization of advanced education, gamification of personal
development, and emergence of customer engagement platforms.
Gamification

ENHANCED TRUST – LEARNER PROFILE MODELLING
It is predicted that in the next 5-7 years, the training systems will use AI to analyze the
learner’s strengths and weaknesses, their understanding of the subject, and adapt
themselves by devising individualized learning profiles based on their progress.
AI helps in implementing adaptive learning by analyzing the abilities,
interests, and requirements of different learner groups and assigning
them appropriate learning materials automatically.
Use of AI in adaptive learning will help in catering the learning
materials to the unique needs of the participants by emphasizing on
certain topics, repeating other topics not yet mastered, and teaching
students at a comfortable pace.
Adaptive online learning, as seen in examples such as Khan
Academy, could see a bigger role, as computers will be able to tailor
instruction and assessments based on student performance.
AI will help organizations by preparing a comprehensive learner profile
of the employees using their job performance data. AI can be
integrated with the organization’s ERP system and Learning and
Talent Development Platform to track employees’ daily performance
and customize their learning plans accordingly.
Based on an individual's learning style and constraints, such as
internet connectivity and shift timings, AI will help organizations to map
learning materials to individual learners. It will automatically sync
learning content to individual’s calendar and send communication.
Learner Profile
Modelling

AI USE CASES IN LEARNING (1/2)
Mika is an AI-based
tutoring tool for providing
one-to-one attention to
students. The app is
modelled to suit a
student’s unique learning
process, track their daily
progress, and adapt
lessons to overcome their
weak areas.
Netex Learning allows
teachers to design
curriculum and customize
student materials across
various digital platforms
and devices in a learning
cloud platform. They can
provide tools for video
conferences, digital
discussions, personalized
assignments, and learning
analytics to show each
student’s growth visually.
CTI is an AI company that
uses deep learning to
create customized
textbooks. The teachers
import syllabi into a CTI
engine, which learns the
content and uses
algorithms to create
personalized textbooks
and courses based on the
core concepts.

AI USE CASES IN LEARNING (2/2)
BiLAT is a game-based
learning environment to
practice skills in
conducting meetings and
negotiations in a specific
cultural context. It enables
learning via interaction
with AI-driven simulated
characters, who respond
to learner’s actions and
provide unsolicited hints
about the appropriate
action.
The ARGUNAUT project
combines AI analysis
module with the online
realtime monitoring
module and helps
students to stay on topic,
elicit contributions from
all members of the groups,
and generally guide the
students toward fruitful
discussion and
collaboration.
Thinkster Math is a
mathematics tutoring app
with a personalized
teaching style. The app
presents each student
with problems appropriate
to their skill set and then
analyzes how they’ve
derived an answer. It
improves each students’
logic process by providing
video assistance as well as
immediate and
personalized feedback.

WHAT AI OFFERS TO LEARNING?
AI driven game-based training can
make learning more fun and
interactive. It can also help
increase retention of content and
improve performance of the
learners.
Inclusive
It can provide trainers and
learners the tools that allow
them to respond not only to
what is being learnt, but
also to how it is being
learnt, and how the learners
feel.
Personalization
Flexibility
It can help learners develop the
knowledge and skills that
employers are seeking, and it
can help trainers create more
sophisticated learning
environments.
AI has the potential to make learning
accessible to more people. In places,
where trainers are unavailable, a
robust AI system can be used to
teach learners with minimal or no
engagement from a human trainer.
Engaging
AI offers the possibility of learning that is more personalized, flexible, inclusive, and engaging.

FUTURE OF AI IN LEARNING
2018
By 2018, 20% of all
business content will be
authored by machines
according to Gartner
Incorporation.
2021
According to Research
and Markets, the use of AI
in the education sector will
grow to 47.5% through
2021.
2035
AI’s economic output in
16 industries will be $14
trillion and corporate
profitability will increase
by an average of 38%.

SUMMING UP, THE FUTURE HAS ALREADY ARRIVED!
With the fast pace advancements that we’ve already witnessed, AI in the future is expected to:
Provide personalized tutorial to the learners based on their
existing knowledge, experience, strengths, and weaknesses.
Create new forms of intelligent e-learning platform
allowing students to explore, collaborate, and as test
their knowledge in multiple scenarios
Free trainers from monotonous and time consuming tasks,
thereby helping them to empathetically attend to the
learners by.

30
THANK YOU!
This Thought Paper was created in the Think Tank Center of Excellence operating in Talent
Development and Learning Practice of Accenture Capability Network. If you would like to
discuss about Artificial Intelligence in Learning, feel free to reach out to us.
Developers:
Shweta Panwar shweta.panwar@accenture.com
Debamitra Dasgupta debamitra.dasgupta@accenture.com
Sponsors:
Devanshi Sahay devanshi.sahay@accenture.com
Marek Hyla marek.hyla@accenture.com

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