This slides was introduced in the 2020 Ed Tech Forum which was online conference due to COVID-19 pandemic. Abstract: In many areas of society pertaining to education, digital transformation is said to be an irreversible trend. In fact, many of daily lives are tightly connected with digital media, and we are experiencing the need to switch to digital and online in more areas during the period of social distancing due to COVID-19 pandemic. However, it is less persuasive to convert the existing off-line services and analog-based jobs into an online non-face-to-face format since the situation with consumers has changed. Consensus is needed on what problems or changes need to be made in digital transformation. Especially in the field of education, it is necessary to bridge the educational gap that has been left as a challenge for a long time, improve the efficiency of learning, and make automation for mundane tasks which are repetitive and take a long time. Through these efforts we are able to determine what form of digital transformation is needed to solve the complex educational problems. This session reviews the problems that can be solved by utilizing the functions of artificial intelligence in the field of education, and introduces the use cases currently being tried and prospective changes. In the conclusion, we will discuss and share some idea to solve the problems we face, emphasizing that the use of artificial intelligence technology should not be the purpose in itself. Note: I'm afraid the event link was not available anymore, but some of my friends want to see this slides for their use case collection. Even if this upload is very late, but hopefully it can be helpful for your interest or works.

1. What is driving digital transformation for?
Yong-Sang CHO
Focusing on problems and use cases of artificial intelligence technologies
CEO, Ph.D

2. Digital Transformation and
Problems have to be solved

3. • Digitization is defined as the 'technical process' of
"converting analog information into digital form"
(i.e. numeric, binary format, as zeros and ones)
3
<source: https://en.wikipedia.org/wiki/Digital_transformation>
Digitization, Digitalization, and Digital Transformation
Digitalization
of industries & organizaions
Digitization
of information
Digital transformation
of societies

4. • Digitalization is the 'organizational process' or 'business process' of the
technologically-induced change within industries, organizations, markets and
branches
• as known as the Internet of Things, Industry 4.0, machine to machine
communication, artificial intelligence and big data, etc.
4
<source: https://en.wikipedia.org/wiki/Digital_transformation>
Digitization, Digitalization, and Digital Transformation
Digitalization
of industries & organizaions
Digitization
of information
Digital transformation
of societies
• Digitization is defined as the 'technical process' of
"converting analog information into digital form"
(i.e. numeric, binary format, as zeros and ones)

5. • Digital transformation is described as
"the total and overall societal effect of digitalization".
• Digitization has enabled the process of digitalization, which resulted in
opportunities to transform and change existing business models, consumption
patterns, socio-economic structures, legal and policy measures, organizational
patterns, cultural barriers, etc.
• Digitization is defined as the 'technical process' of
"converting analog information into digital form"
(i.e. numeric, binary format, as zeros and ones)
5
Digitization, Digitalization, and Digital Transformation
Digitalization
of industries & organizaions
Digitization
of information
Digital transformation
of societies
• Digitalization is the 'organizational process' or 'business process' of the
technologically-induced change within industries, organizations, markets and
branches
• as known as the Internet of Things, Industry 4.0, machine to machine
communication, artificial intelligence and big data, etc.
<source: https://en.wikipedia.org/wiki/Digital_transformation>

6. 6
<source: https://en.wikipedia.org/wiki/Digital_transformation>
Conflicts of learning experiences (at the same time)
School system on curriculum standards
AI assistant and AI tutor (with high touch)
Adaptive learning and seamless learning experiences
(on content platforms)
Digitalization
of industries & organizaions
Digitization
of information
Digital transformation
of societies
Extra curriculum subjects, such as coding & AI education
Private education for English and Math (with Ed Tech)
Private tutoring using learning analytics (on Big Data)

7. Tyton Partners
“Anyone who has ever been in a classroom – where as a student or instructor –
knows that not all students procced at the same pace.”

8. One size does not fit all
Tyton Partners
“Anyone who has ever been in a classroom – where as a student or instructor –
knows that not all students procced at the same pace.”

9. 9
“Iron Triangle” in physical environment
Quality
Cost Access

10. 10
Hard Skills Soft Skills
(a.k.a interpersonal skills)
#Degree #Certification #Programming
#Proficiency and Operation #etc
Balancing issue regarding subjects
# Communication # Flexibility # Leadership
# Teamwork and Time Management # etc

11. Finding solutions using EdTech

12. 12
Adaptive learning enables to diagnose individual learners' weak points and provide specific intervention,
predict learning outcomes to establish effective learning pathways,
and recommend personalized learning resources with interesting and fun elements.
Resource
Curricula
Analytics
Why we have interested in adaptive learning?
Diversity, Fun,
Preference and Needs, etc.
Efficiency, Aptitude,
Feedback and Recommendation, etc.
Personalized Pathway,
Knowledge Space, etc.

13. 13
Automate routine repetitive tasks and
conversations with a large number of
teachers, students, parents, and other
educational stakeholders
Improved predictability of learning
outcomes, drop out rate, and availability
to educational resources
Improved data-driven decision making
capability with minimization of human errors
Improves learning efficiency by providing 1 to 1
personalized learning pathway and resources tailored
to learners' level and disposition / personality
Interfaces in which users and machines or AI
agents process emotions through natural user
interface / experience (NUI/NUX)
Personalization
AI Assistant
AI Tutor
Intelligence
decision-making
Predictive
capabilities
Automation of
mundane tasks
Adopt artificial intelligence to education

14. AI Assistant
(speaker)
AI Tutor
“Passive execution” “Actively execute according to user behavior”
14
o Applied to various content recommendations and interactive activities
o Teaching and learning design, dialogue scenarios, and learning content are important
o In addition to speech analysis, learning analytics and context reflection will be core values.
AI Assistant vs AI Tutor
o Audio content (weather, news, music, etc) execution, IoT control
o Voice recognition and intent identification are the main issues
o Reflection of speech analysis will be the core value
Emphasis on the convenience of voice control
o Voice commands and perform specific actions o Using the functional value of voice commands for learning and tutoring
Emphasis on educational value and efficiency in learning and tutoring

15. Artificial
Intelligence
15
• Predictive Analytics
• Deep Learning • Text To Speech
• Speech To Text
• Image Recognition
• Machine Vision
• Classification
• Translation
• Data Extraction
(FYI) AI functionalities
Machine
Learning
Language
Processing
(NLP/NLU)
Speech
Expert
Systems
Planning &
Optimization
Vision
Robotics
Examples of typical functions

16. 16
Artificial
Intelligence
• Prediction for learning outcomes and
at-risk population
• Recommendation for pathways and
resources
• (Mobile) Learning agent
• Natural User Interface
• Video/Image tagging and search
• Recognition of objects and search
• Eye gaging and detecting emotion
• Q&A
• Language education
• (Reading) Extract context
from books
Machine
Learning
Language
Processing
(NLP/NLU)
Speech
Expert
Systems
Planning &
Optimization
Vision
Robotics
Educational AI usage model classified by functions
• Subject consulting
• Career/Univ. admission
consulting
• Teaching/Coaching
improvement
• Improvement of
learning resources
and services
• Optimization of
infrastructure
• Robot agents
• School safe guards
• Experiment/Practice
assistant tools
Examples of typical functions

17. Case Study
Regarding Digital Transformation

18. Learning Event Driven Analytics for Adaptive Learning
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Reasoning
(Diagnosis of problems)
Feedback & Recommendation
(Practice of reflection)
Learning Analytics
Data Analytics
(Capture learning context)
Learning Activities
(Scheduled Resources & Assessment)

19. 19
Learning Event Driven Analytics for Adaptive Learning
Learning Activities
(Scheduled Resources & Assessment)
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Reasoning
(Diagnosis of problems)
Feedback & Recommendation
(Practice of reflection)
Learning Analytics
Data Analytics
(Capture learning context)
• Session
• Assessment / Assessment Item
• Grading
• Assignable
• Media
• Navigation / View
• Tool Use
• Customized Event
IMS Caliper Analytics
source type
.
.
.
.
(keywords, compensation etc.)
안녕? 반가워.

20. Data Lake
(cleaning context)
Data Mart
(making data sets)
Learning Event Driven Analytics for Adaptive Learning
Data Capture
(IMS Caliper)
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Learning Activities
(Scheduled Resources & Assessment)
Learning Analytics
Reasoning
(Diagnosis of problems)
Data Analytics
(Capture learning context)
Feedback & Recommendation
(Practice of reflection)

21. 21
Learning Analytics
Reasoning
(Diagnosis of problems)
Learning Event Driven Analytics for Adaptive Learning
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Learning Activities
(Scheduled Resources & Assessment)
Data Analytics
(Capture learning context)
Feedback & Recommendation
(Practice of reflection)

22. Learning Event Driven Analytics for Adaptive Learning
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Learning Activities
(Scheduled Resources & Assessment)
Learning Analytics
Reasoning
(Diagnosis of problems)
Data Analytics
(Capture learning context)
Feedback & Recommendation
(Practice of reflection)

23. 23
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Learning Activities
(Scheduled Resources & Assessment)
Learning Analytics
Reasoning
(Diagnosis of problems)
Data Analytics
(Capture learning context)
Feedback & Recommendation
(Practice of reflection)
Learning Event Driven Analytics for Adaptive Learning
Intervention with AI tutor

24. 24
For instance, situation to increase computational skills
there are different types of game, such as arcade, card, and challenge game type,
can be provided to improve computational skills.
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Learning Activities
(Scheduled Resources & Assessment)
Learning Analytics
Reasoning
(Diagnosis of problems)
Data Analytics
(Capture learning context)
Feedback & Recommendation
(Practice of reflection)
Personalized pathway for Math (using serious game)
✏️ Note

25. 25
Learning Activities
(on Personalized Pathway)
AI Student Report
(for learner, parent, and tutor)
Learning Activities
(Scheduled Resources & Assessment)
Learning Analytics
Reasoning
(Diagnosis of problems)
Data Analytics
(Capture learning context)
Feedback & Recommendation
(Practice of reflection)
Personalized pathway for Math (using serious game)
Language education for adaptive level is
possible by applying free type conversation
processing technologies to the storytelling-
based curriculum.
By mixing learning environments by
learning difficulty and free conversation
situations, immersion in each situation can
be increased, and achievement can be
improved through repetitive learning.
The application of large-volume conversational knowledge and processing technologies for
language learning can be expanded through AI learning by learning topic.

26. 26
AI Student Report
(for learner, parent, and tutor)
Learning Event Driven Analytics for Adaptive Learning
Learning Activities
(Scheduled Resources & Assessment)
Learning Analytics
Reasoning
(Diagnosis of problems)
Data Analytics
(Capture learning context)
Feedback & Recommendation
(Practice of reflection)
Learning Activities
(on Personalized Pathway)

27. Thank you