Chat with Squirrel Ai Learning | ASU+GSV Summit 2024
Summary
TLDRこのスクリプトは、 scoro AIという会社が提供するAIアダプティブ学習システムについて紹介しています。scoro AIは、数学や科学など、子供たちに幅広い科目でアダプティブ学習を提供しています。彼らのプラットフォーム「L」は、学習内容の構築とハードウェアの提供を含む大きなアダプティブモデルです。その主な製品であるスマート学習タブレットは、ディストリビューターを通じて市場に販売されており、学生が自己学習を通じて学ぶためのシステムです。また、scoro AIは、中国の2,000以上のディストリビューターと60,000以上の公的な学校を支援し、2,400万人以上の学生がそのシステムを使用しているとされています。さらに、 scoro AIは、AI技術の進歩を最大限に活用し、教育の難しさに対処する取り組みを行っています。
Takeaways
- 🌟 スコロAIは、子供たちに適応型学習を提供し、数学、科学などの科目があります。
- 📚 scoro AIは、AI適応学習を9年前に開始し、今年で10周年を迎えます。
- 💡 Lという新しいプラットフォームは、大規模な適応モデルです。コンテンツビルディングとハードウェアを提供しています。
- 📈 scoro AIは、中国の2,000人のナショナルディストリビューターと60,000以上の公立学校を支援し、2,400万人以上の学生がシステムを使用しています。
- 🏆 scoro AIは、Deoの研究によりトップ50の急速成長テック企業の一つであり、MITテクノロジーレビューによって最も賢い企業の50社にランクされました。
- 📝 scoro AIは、AI適応学習と大規模モデルに関する研究で50以上の論文を発表し、さらに50を書く予定です。
- 🔍 scoro AIは、カリフォルニア大学、スタンフォード研究インstitute、中国のアカデミーと共同研究ラボを設立しています。
- 🧠 AIエージェントは、オープンAIのシステムと適応システムの間で動作し、評価、推奨、予測などの機能を提供します。
- 📊 scoro AIは、学生の学習行動データをリアルタイムで収集し、学習目標のナノレベルの分解を使用して、学生の学習ギャップを特定します。
- 🌐 scoro AIは、中国とアメリカの両方のデータセンターを使用し、データの安全性とプライバシーを重視しています。
- 🏢 scoro AIは、中国にサービスセンターを持ち、米国市場に進出し、数学のコンテンツを今年、来年は科学などの他の科目のコンテンツを構築予定です。
- 📱 スマート学習タブレットとデジタルパッドを使用して、学生は手書きの数学解答を入力する必要がなく、システムはすべての学習セッションを内部で処理します。
- 👩🏫 scoro AIの物理センターでは、デバイスの販売と学生の自己学習の監視、データ分析が行われ、オンラインスーパーバイズも提供しています。
Q & A
スクーロAIのサービスは何を提供していますか?
-スクーロAIは、子供たちに合わせた学習を提供しています。数学、科学などの科目があり、さらに他の科目にも拡大していく予定です。また、AIアダプティブ学習を用いて、学生が自分に合った学習パスを見つけることができます。
スクーロAIのプラットフォーム「L」とは何ですか?
-「L」はスクーロAIの新しいプラットフォームで、大きなアダプティブモデルをシステムに取り入れています。コンテンツのビルディングが行われ、さらにハードウェアも提供しています。
スクーロAIのスマート学習タブレットはどのような機能を備えていますか?
-スマート学習タブレットは、AIアダプティブ学習モデルを搭載し、学習コンテンツを提供します。また、ペンやスピーカーなどの周辺機器も用意されており、学習をサポートする機能が集約されています。
スクーロAIのビジネスモデルはどのようなものか説明してください。
-スクーロAIは、中国には2,000人のナショナルディストリビューターがあり、60,000以上の公立学校がアダプティブ学習システムを使用しています。また、スマート学習タブレットは過去1年間に200,000台以上の販売実績があります。
スクーロAIはどのようにして学生の学習効果を向上させていますか?
-スクーロAIは、AIアダプティブ学習を用いて学生が自分に合った学習パスを見つけることができます。また、教師の役割は、学習内容との直接的な対話よりも、監督とデータ分析に変わっています。
スクーロAIの学習システムはどのようにして学生の学習を個別化していますか?
-スクーロAIは、学生がテストを行うことで学習状態を把握し、強みや既に習得している学習目標、未習得のギャップを特定します。その後、個々の学生に合わせた学習コンテンツが提供され、学習セッションが進むごとに学習目標が動的に変化します。
スクーロAIの学習データはどのように活用されていますか?
-スクーロAIは、学生の学習データを収集し、学習行動や歴史を記録、リアルタイムでの分類と予測を行い、学習内容の最適化に活用しています。また、学習データは学生の感情的な表情や学習プロセスを理解するのに役立ちます。
スクーロAIの学習システムはどのようにして学生の間違いを特定し、フィードバックを提供していますか?
-学生が解答を提供する際には、デジタルパッドを使用して解答を記入します。システムはこれらの解答を分析し、学生が問題を正しく答えられない理由に基づいてフィードバックを提供します。また、問題解決の方法を示すビデオも提供されます。
スクーロAIはどのようにして学生の学習を監督していますか?
-スクーロAIは、学習センターでの自習を監督するとともに、オンラインでの監督も行っています。学習セッション後、スーパーバイザーは学生の進捗や学習状況をダッシュボードから確認し、必要に応じてサポートを提供します。
スクーロAIはどのようにして学生の学習効果を予測していますか?
-スクーロAIは、学生が行ったテストの結果や学習行動をもとに、学習効果を予測します。また、学習データアナリストは、データに基づいて学生が今後どれだけの学習時間を必要とするかを予測し、フィードバックを提供します。
スクーロAIの学習システムはどのようにして学生の学習目標を細分化していますか?
-スクーロAIは学習目標をナノレベルまで細分化し、学生が習得していない学習目標を特定します。これにより、学生は小さな学習目標に焦点を当て、より効率的に学習することができます。
スクーロAIはどのようにして学生が異なる科目間のつながりを学ぶことができますか?
-スクーロAIは、異なる科目間のつながりを学ぶために、学習目標の間の関係性を理解し、クロス科目の知識を組み合わせる知識グラフを用います。これにより、学生は異なる科目間のつながりを理解し、総合的な学習を深めることができます。
Outlines
😀 公司紹介とAI学習プラットフォームの概要
scoro AIは、数学、科学などの科目で子供たちに応じる学習を提供する企業です。新しいプラットフォーム「L」は、大規模なアダプティブモデルです。製品はスマート学習タブレットで、ディストリビュータを通じて市場に販売されています。AIアダプティブ学習は、学生が自己学習の道を見つけるのに役立ちます。
📈 ビジネスモデルと実績
scoro AIは中国で2,000人のディストリビュータと協力し、60,000以上の公立学校を支援しています。スマート学習タブレットは、昨年に200,000台以上の販売を記録しました。また、技術的な進歩により、MIT技術レビューによって「最も賢い企業」のランキングにランクされました。
🧠 AIアダプティブエンジンの3つのレイヤー
scoro AIのAIアダプティブエンジンは3つのレイヤーから構成されており、目標、コンテンツマップ、学習経路、そして間違いの理解に関するオントロジーが含まれています。さらに、学習記録スター、リアルタイムインベントリコレクターが学生の学習行動を記録し、次の学習コンテンツを予測します。
📚 ナノレベルの学習目標とコンテンツ
scoro AIは、学習目標を極めて細かく分割することで、学生が非常に小さな学習目標を認識し、効果的に学習ギャップを埋めることができます。これにより、学生は短時間で効果的に学習することができます。
🌐 学習デバイスと機能
scoro AIはスマート学習タブレットをはじめとする学習デバイスを提供しています。これらのデバイスには、アダプティブ学習と大規模モデルシステムが組み込まれており、さらに他のテクノロジーやアプリケーションも搭載されています。これは安全で健康的な学習環境を提供し、親が子供の学習状況を監視することができます。
🤖 AIエージェントの構造とデータの活用
scoro AIは、オープンAIを使用してAIエージェントを構築しており、学生とのインタラクションをサポートしています。また、学生から収集されたデータを使って、学習体験を改善し、製品を時間とともに向上させています。データの安全性とプライバシーについても重視しており、国ごとのルールに従ってデータ分析を行います。
📝 数学学習の相互作用と物理センターの役割
scoro AIの数学学習では、デジタルパッドを使用して解答を記入します。これにより、解答を再入力する必要がなくなります。また、学習センターでは、デバイスの販売や学生の自己学習の監視、データ分析が行われます。オンラインでの監視も行い、学習の進捗や問題点をオンライン会議で話し合うことができます。
🏢 学習センターの機能とオンラインスーパーバイジョン
学習センターは、デバイスの販売と学生の自己学習のサポートを提供しています。また、オンラインスーパーバイジョンを通じて、学習の進捗や問題点をリモートでサポートすることができます。これは、学生が自宅で学習する際にも適用され、効果的な学習サポートが提供されています。
Mindmap
Keywords
💡scoro AI
💡アダプティブ学習
💡Lエンジン
💡スマート学習タブレット
💡自己学習
💡MCM(モデル思考力と方法論)
💡ナノレベルの学習目標
💡AIエージェント
💡データプライバシー
💡デジタルパッド
💡オンラインスーパーバイズ
Highlights
scoro AI is an adaptive learning company focusing on math, science, and expanding into more subjects.
The company's new platform, L, is a large adaptive model that serves as the engine for their content building and hardware provision.
scoro AI's smart learning tablet is a key product, sold to distributors who then sell to customers.
The L system facilitates student self-directed learning, reducing the need for human teachers to interact directly with learning content.
Teachers' roles have shifted to supervising and data analysis, aiding in student learning outcomes.
scoro AI has addressed challenges in traditional education by personalizing learning paths through AI adaptive learning.
The company has a presence in 60,000 public schools and has served over 24 million students.
scoro AI has sold over 200,000 smart learning tablets in the past year.
The company has been recognized by Deloitte as one of the top 50 fast-growing tech companies.
scoro AI has published over 50 papers in AI and educational conferences and is working on another 50.
The company has established joint labs with institutions like Stanford Research Institute and China's Academy.
scoro AI's adaptive engine has three layers: goals, learning records, and real-time data collection, with a focus on multimodal behavior analysis.
The company uses a large adaptive model that includes a multimodal education large language model and knowledge graphs for different subjects.
scoro AI has developed AI agents to provide evaluation, recommendation, and prediction services, ensuring accurate information for students.
The company's engine breaks down learning objectives into nano-level detail for precise identification of learning gaps.
scoro AI's system collects real-time student data, including emotional and behavioral responses, to tailor learning content.
The company has physical centers in China that sell devices, supervise learning, and provide data analysis for students.
scoro AI is entering the US market, developing content for math this year and planning to expand to science and other subjects next year.
The company uses open AI APIs for student interactions and has an AI agent structure that interfaces between the open LLM and their adaptive system.
Transcripts
[Music]
so yeah good morning and I hope you all
enjoy the sunshine here in San Diego and
my name is Pearl and I'm with scoro AI
so welcome to the session uh scoro AI
has been producing uh adaptive learning
for for kids we have subjects in math
science and we're going to Explore More
into more subjects so um I'll have
Jolene here making uh introduction
session to the
company hi everyone um it's a very
freestyle today's session so you know
it's called chat with squir AI so anyone
who is interested with like to have a
chat so the first half of the session uh
I will do some introduction about the
company the product the technology and
business model that we are doing and
then the second half if anyone has any
questions free feel free to ask so it's
really really
freestyle um so as per mentioned uh
scroll AI learning is the name of the
company so basically we use AI adaptive
learning from 9 years ago and then we
adapt
uh large model into the
system so the com um currently our new
platform is called L which is large
adaptive model so that's the engine we
have and then on top of the engine we
have the contents building and then
provide Hardware so the hardware uh we
will show later there's another slide
showing it's kind of ecosystem of all
different uh types of the hardware the
main one will be the smart learning
tablet and then with the Smart Learning
tablet is our product and then we sell
to the Distributors and then
Distributors will sell to the customers
so basically we are not in school
program so we are additional Learning
System for the
market and more important is the whole L
system is student self-directed learning
which means no no human teacher needs to
be teaching or interacting with the
learning contents itself but the roles
of the teachers have been changed in our
whole system which is supervising and
doing data analyzing for the students
and then to help the students um you
know the learning
outcome so I think um the first pages I
I can actually just skip most of the
people in this conference everyone knows
the difficulties at current um
traditional education like um the same
speed in the classroom but the students
actually having different
personalized uh learning needs and also
in some areas it's the same situation in
China us and anywhere um around the
world that we don't have ai sorry we
don't have Superior teachers and well
educated teachers everywhere maybe New
York maybe San Diego maybe you know La
maybe be Shanghai but not some rural
areas and also other countries
and and another thing is most of the
schools somewhere um they teach
students sorry they teach students of
the knowledge only but the more
important is the student need to be
trained with ability mod of thinking you
know their skills not the knowledge only
so with the current Cent difficulties in
traditional education in the classroom
even though with online learning we call
them traditional learning
already it it can't really customize
each different students learning needs
so with AI adapt learning that's why we
started nine years ago and this year is
actually our 10th year so our goal was
to change these current difficulties to
help the students to find the
personalized learning path when we say
personalized I know everyone here is
talking about personalized it's really
personalized so four students in the
classroom and each of you will be
learning your own learning path maybe
you are in the same grade your levels
may be similar but you know from the AI
adaptive and a large Modo system we can
find out each different VI have
different learning gaps and then the
system will recommend to you all the
different learning contents and
practices and problems and not at the
same level even though you might be in
the same grade so that's what AI
adaptive learning can bring to the
students so uh this is a page about our
business model and currently we have um
2,000 National distributors in China and
we've helped over 60,000 Public Schools
um you know using the Adaptive Learning
System and accumulated we have had 24
million students used our system and for
the intelligent devices which is the
Smart Learning tablet we've got over um
200 uh 200,000 pieces already sold in
the past year and we were one of the uh
top 50 uh fast growing at tech company
uh that was the research by Deo and also
we were one of the 50 smartest company
uh ranked by MIT technology review that
was few years ago and also uh one of the
owner we had
was awarded by UNESCO uh we were one of
the AI Innovation companies in
2020 yes 2020
yeah so this picture shows uh we've been
giving speech
and around the world's International
conferences and most important we've got
many papers uh published in AI academic
conferences and AI educational
conferences and now we have over 50
papers and we are writing another 50 so
next year we're going to have over 100
papers published it's all about AI
adaptive with large model uh research
and also we had uh a few joint
um Labs so in
200 um 19 I remember so we had joint lab
with car Mali University and in 2015 we
started working with uh Stanford
Research Institute and also one of the
um China's Academy joint lab and this is
something we were you know really proud
of in from
2 um I think 2019 we started uh we we
found a scroll AI Award with triple AI
and Euro AI so that award was
announced um
to the scientist who can bring out the
technology and the products that will
benefit for our Humanity so that award
was $1 100 million dollars a year and
also we were one of the Stanford NBA um
case study so um let me introduce a
little bit about our engine so this is
the engine about adaptive not adaptive
with uh large model so we've got three
layers
um so this is the first layer so on the
first layer of the Adaptive engine we
have the goals and the goals will be
dynamically changed for the students and
then we have the content map learning
map and mistake reasoning onology so the
content maps are learning objective
learning object itive videos problems
questions and
solutions um all sorts of learning
materials that is digitalized and very
Nano level for the learning objective
and for the learning map is each
different students have their learning
pathway so they have their different
Knowledge Graph and of course for the
learning objective we have the knowledge
graph for all the subjects and for the
mistake reasoning
ontology we because each when the kids
learn they always make mistakes but the
back um you know the background reason
of why they make mistakes are totally
different for example um the answer a is
correct and B C D they are all wrong
answers so when I answer B and you
answer B so the reasons will be
different because our knowledge graph
our knowledge state are different so the
reasons are
different and for the second ler we have
the learning record star which is to
recording um all the learning behaviors
and records and all the histories for
the students learning and also doing the
real time uh and classification and
doing the prediction of the next step
learning um
contents and for the third layer is the
real time invent collector so here we
are collecting not only um
right or wrong or learning speed and
also the multimodal behavior that is
generated from the real human uh the
real students so that is kind of
interaction receiving the data and
transfer to the second
layer and here is the large adaptive
model so after um like one and a half
years ago we started to do res R&D in
the large model so when we say large
model it's not large language model
because we have multimodal Behavior
Analysis so it's not only the
language and here is the framework of
our l so for the first layer we can see
is the application layer and then the
second layer will be the model layer and
the third layer will be the data
layer so for the uh for the application
layers we have the adaptive learning uh
system which I mentioned early and also
you know the first one is basically the
Adaptive learning layers um I mentioned
the three layers and for the model layer
we have this is the main one for our
large adaptive model so we have the
multimodal education L uh large language
model and also the knowledge graph for
the learning objective for different
subjects and also when there different
subjects such as science math they have
cross subject knowledge that components
that can be related to each other so not
only within that subject it could be
cross subjects for the knowledge graph
and also the rack and also the AI agents
so for the AI agents we have different
agents between the Adaptive system and
the large model system because the
agents can can do kind of
evaluation uh recommendation
prediction and that is very important
otherwise everyone knows that from large
language models there's always some
confusions and wrong answers and maybe
for entertainment for adults that's fine
but for students learning the results
the information that the system give to
the students need to be accurate that is
very important and for the data layer
some of the part is from the Adaptive
system which I mentioned uh the learning
map the content map and also the
learning outcomes from the students
learning uh across the whole system and
there's another one we called Nano level
learning objective I think uh there will
there will be a page about the Nano
level and there's thousands of um we
have had over one uh sorry uh 100
million uh learning Behavior data in our
system and here is the engine for the a
agents so we have different agents which
I mentioned early um yeah you can take a
picture and then I will go to next
page so this is the autology of the
learning map learning goals and content
map which is the first layer so this is
the one I just mentioned the Nano level
breakdown of the learning
objective um we were actually at that
year we were the first company who did
really really Nan level for example if
the
normal numbers of the learning objective
from uh subject like common call in us
and common call in China is like 300 and
we other companies would uh have breaken
them down from 300 to 3,000 and what we
have done is from 3,000 to 30,000 so
it's goes to really really Nano level so
why do we go so Nano level because
it's very precise then when we when the
system recognize the students learning
Gap it will be really really tiny
learning objective then the system will
do root tracing to find out that
learning Gap then the students can only
um you know learn that Nano level of
their learning objective and once the
students mastered and then they can come
back so instead of a bigger learning
objective we use Nano level so that will
save lots of time to the students
because sometimes we find this big
learning objective the student cannot
understand cannot Master cannot answer
the question it's not because the whole
learning objective because this learning
objective can be break down into say 50
Nano level it could be 20 out of the 50
of the learning objectives the student
didn't master in the previous years
which means the students only need to
learn those 20 Nano level L then the
student can come back it will save lots
of time for the students so for example
addition and subtra subtraction
subtraction of fractions this is a
bigger uh Big L O and then we can break
down you know to second layer and then
to the third layer so uh from three
layers to nine layers that's the average
layers we've gone it really depends on
which learning objectives
so this is just one example and this is
a system we collect the students
learning data at real Time That's the
third layer of the Adaptive
engine and as I mentioned early we do
have uh learning goals that is set up
for the students once the students has
started learning and doing some testing
in the system we can have the goals and
the goals will dynamically be changed
along with the students learning
progress and also the system can do
prediction for the for the students for
example once I learned 10 hours in the
system and then from the the data
dashboard the data analyst which is the
human teacher can do a prediction based
on the data saying hey Jolene you you're
going to need another 50 hours and then
you can catch up for your current level
Uh current grade in your classroom so
that's prediction we can see from the
screen and then the prediction might be
changed because it really depends on how
long I'm going to use and how effective
I can learn so that will be prediction
for the students the students will kind
of have a plan how long I need to learn
to manage my uh learning and also doing
the recommendation of uh each different
students oh this is one example I want
to show so you can see the graph the
first line is the great sorry the first
line is grade 10 and the bottom one is
grade seven for example I'm a grade 10
students there are some learning
objective that's in yellow dot that I
didn't master so I can't really do some
you know test or answer some questions
or I can't really manage some of my
assignments so why can't do it it's
probably because some of the learning
objective from grade eight or grade
seven I didn't Master at that time what
I need to do is I need to learn those
knowledge Gap so the system will
automatically find out the learning
objective the gaps from grade seven or
eight all students have different gaps
and then I need to go back to those gaps
and learn and master and then I can just
automatically come back back to the
current grade 10 learning
level and there's another example of
three different students we can see the
uh the blue learning objectives are the
ones the students have already mastered
which is 80% mastered and the pink ones
are not mastered which is the gaps and
another students similar level same
grade but the gaps are different the
pink ones are different and the third
one the pink ones totally different
which means we need to have older
students even though in the same
classroom they should be learned
differently so our system is designed
for students not for teachers to teach
it's for students to learn so they will
be using the system learn
differently and this is another uh
element I mentioned at the very
beginning traditional Learning Classroom
actually especially in China and maybe
some Asian countries um the students
learn knowledge more more than some
skills from the classroom so we have the
system called MCM which is model
thinking capacity and methodology so
what we do is we divided those learning
objective from different careers for
different MCM learning objective or we
call MCM elements I won't go really
details and those MCM elements can be
from language subject can be from math
can be from physics can be from biology
it can be from any subject so we need to
do the analysis of which subject can
create those mcms and those MCM anims
will have connections with the careers
so that's kind of connection for the
students you learn the knowledge and
also at the same time you learn the
knowledge you can train your mcms and
those M MCM elements will be uh useful
for your future
career and this is um the the product uh
we have so in the middle is the Smart
Learning devices uh Smart Learning
tablet and we also have the pen and uh
the speaker and so on and sorry this is
the price I just want to show uh what we
have different types of the Smart
Learning
devices and those are the fun functions
for the learning uh tablet so we have
the Adaptive with large model system in
the in the learning tablet but we also
have other additional technology and
applications that is useful and even
games for the students it's a close
ecosystem in the learning tablet so it's
very safe and healthy for the students
to use and it's also very good for the
parents to kind of have monitor but
without saying too much but is kind of
monitoring understand your your young
kids what they have gone uh which uh
application they have used and also
another thing is they cannot go outside
of the other uh websites that is not as
restricted and this is the service
center uh we have in China and how how
it looks like we have the uh counters
that to sell the products and also we
have the self-directed Learning
Classroom for the student students to
learn so um this year we are entering US
market and we are designing the contents
for math this year and then next year
for Science and for other subjects so
what we need is we are building the
contents first and then build uh and in
integrate those contents onto our large
adaptive model engine and then the
products will be ready and then we are
going to expand our um centers
later on after the products uh is
pronounced is announced yeah okay thank
you thank you very much and maybe per
you want to come here and answer some
questions if any
yeah yeah I have like a million
questions sorry hi can you hear me okay
can you hear me okay so my name is Dylan
and I'm coming from the Atlanta area and
I just wanted to say first and for
foremost that this is a very very
impressive Enterprise I am blown away by
the amount of time it took to build this
the second thing I'd like to say is you
probably felt so lucky with changes in
the AI landscape because you were in the
perfect position to take advantage of
this type of technology so wow how
fortunate I have many questions for you
and I'll just go with down on the stack
and you can just take this microphone
away from me whenever you're ready okay
so uh this is a question for either of
you um that is what is the uh Primary
llm in your stack is it a custom
fine-tune llm that you use for
interaction with the
student that's part of it do you want to
answer or okay yeah it's it's part of it
so
um yeah I just turned to the l l m page
so it's very important that interaction
with the students so when we talk about
interactions there are different
interactions so one interaction is when
the students is using the Smart Learning
tablet to learn it will generate many
Behavior many data that is not talking
to the system interaction is the
students learning by themselves
interaction that's one interaction and
another interaction is in the system we
have a we call it a AI virt to um
assistant but that assistant is not
to teach the system is to answer the
questions for example um I learned this
I've learned this learning objective I
will say hey um assistant I do have a
question why I got it wrong right and
the assistant will tell you oh maybe
because your prerequisite L was not Mar
so you have to continue learning uh
don't give up you're going to be good
it's more like
emotional encouragement assistant with
the student so that's another
interaction the student really talk to
the system so for the conversation that
little bit of conversation what is the
are you using an open source llm for
providing that communication or are you
at Liberty to talk about it are you
using it's not open source we use open
AI of of course yeah okay got it so
you're using like API calls to chat gbt
4 or yes and but between that there will
be an AI
agent okay between our system to open AI
yeah so I have a question about AI
agents so how are you actually
architecting the AI agent structure are
you using something like autogen or crew
AI or do you have your own system for
creating these llm based agents
we don't have our own system for lrm
because we are not you know that kind of
company and that will requires lots of
funds money to to to uh to train so we
just uh we what we build is our AI
agents so that agents is kind of
isolated from um open AI system but it's
in between the open llm and with our
adaptive system so we do evaluation
classification and and many other you
know functions so that's what we do so
we don't have our own
llm okay yeah um so my next question has
to do with uh how you use data from
users to improve experience and improve
the product over time you are collecting
a lot of Behavioral data you're also
collecting a lot of data over where
students are making mistakes again and
again so how are you using the data that
you ingest to improve the product and
experience over
time yeah that's pretty deep uh
questions from the tag side from the AI
side um so we have let me show you this
um here where is
it the Adaptive that is here yes it's
more like the second layer and the third
layer here so because
we Sorry
here yeah so we collect the data on the
second
layer all the data comes into the system
and the algorithm will need to do
calculations so when we when we say
calculations for
example um we
capture the students emotional facial uh
expression that's one
data
if for example two students right and we
all answer this question and correctly
within three minutes for example but my
facial recommendation was kind of very
happy and concentrate and when per was
doing that she was like so Wonder around
you know that's different data the
system collect to analyze our different
learning behavior and also the system
will need to based on the prerequisite
previous learning uh history on and
learning behaviors and also our
different knowledge states of
understanding the whole chapter of the
learning objective and there's
relationships between each different
learning objective and our learning
history so that could be one part and
also for example for math when we answer
some questions there so the students
need to write down the solutions why you
know the step by steps and we have a pad
so they can write on the uh pad and then
they will be captured as a picture so
even though we both answer that question
correctly but the learning the solution
passway is different between us the
system will understand differently so
all sorts of um f Fe back will be taking
into con account concern right to to
analyze different students knowledge
stats and then for the next step of the
learning we going to have different mhm
yeah learning content to be you know
recommended to us yeah yeah I think I
can have some ad on here first of all we
have two separate servers for the data
for the Chinese students and here in US
students
we're concerned about the data safety
and we follow all the rules for
different countries and also when we
analyz data uh especially for kids and
under age we two countries we have
different rules right here and for China
or but there are some common common
characteristics for both countries of
course we will uh eliminate and covered
all the students name
and their uh special about their privacy
so that's the top priority we always
care in our company we always prioritize
what's the safe also we are also aware
the um we we emphasize on the education
side even though L they have lots of
Advantage as we hear all over across
this conference for this year we are
aware how should we adapt the modern
technology in the use of Education we
want we don't yes we want be ahead of in
the technology to use the most modern or
to use the most State ofth art to help
our students but we always want to
discuss with our peer companies with
peer um what we need to concern first
about our cats because it's about um uh
uh young kids not adults so we
are I think we want to contribute our
effort and we want to share our
technology with all the companies and um
to talk about to discuss to open
discussion with how can we help the kids
while we we are very concerned and keep
everything under control
right like quotation mark under control
for for Education sure well thank you
for that answer Pearl um so I've used a
system that's analogous to this so it's
not your system but I used a system
that's analogous to this that uses the
Adaptive approach plus Ai and for math
learning and I worked with it for about
six weeks and one thing that I noticed
was a shortcoming of the system was that
I had to constantly take my handwritten
math Solutions and type them in to the
system and if I didn't show my work or
show my stages of solution the system
couldn't help me with that you say that
you have a multimodal system I'd just
like to learn more about how the actual
user interacts in a math lesson with
your product and service okay
um we we actually don't call our systems
uh contents as math lessons because when
we call them lesson is more like um
classroom atmosphere so in the in our
system there's no lesson uh it's
like let me put in this way so the
students log into the system first what
they need to do is kind of testing the
testing won't be so long it could be
like 10 to 30 questions it's really
various for different students even it's
the same sub uh same chapter so after
after those little um tests the system
will understand my knowledge State and
what are the strengths what I have
already mastered and what are my gaps
and then I might need to be going back
to the previous uh learning objective to
master and then move step by step so if
for maass subject um after the test it
will be some learning contents started
to recommend to me so we call that
session is learning session the learning
session is I need to learn the
previous uh gaps which I have so that
will be pop up like um the system will
pop up some like three minute three
minutes around videos for each n level
of the learning objectives that I didn't
master in the previous levels and then
after this learning Maybe 20 minutes and
then the next St will be
practices practice of uh answering some
problems questions doing exercises and
then during that time there will be some
calculations or Solutions uh that I need
to write down so we have another pad
which is digital pad the students will
just write on the pad not on piece of
paper because if on piece of paper they
will need to be retyped into the system
so all the answers when students does
when students do is just type on the
keyboard and then the answer but when
they do the solutions there will be a
pad and writing on the pad and then will
be saved and captured so that data is
calculated into the system so there's no
extra like paper things that will need
to be retyped in the system and after
the practices the next session will be
the system will evaluation of the
students
learning uh outcome like the result the
result is not the score result but if
the student has mastered the learning
sessions all the learning objectives by
doing the
exercises maybe yes maybe not maybe
partial I don't know and then the next
one will be if the students has
successfully uh mastered all the
learning objective and the student can
be moved to next level of the learning
objectives learning otherwise it could
be kind of uh relearned but when they
relearn uh it will be different contents
even though it's for the same learning
objective but the contents of the
learning will be different and if the
students when they do practices there's
some wrong answers and there will be
popup of the problems solution
recommendation and Analysis and it's
kind of an the teaching uh for the
students how to Ender because the reason
the student cannot answer the problems
correctly could be understanding of the
problems it could be some other things
and this you know that video will show
some um analysis for the students answer
and tell them how to improve you know
this is kind of so everything is done
within the Smart Learning devices it
won't be any outside of like paper and
re retype so it's really student
centered um system it's either the
learning tablet or SAS system which is
the learning account uh based on just
Internet it's all done with some devices
it won't be any um external inputs thank
you additional external inputs um so I
have one last question and then I'll
hand the mic off three minutes yeah last
question is um could you go into detail
about the role of the physical centers
okay
I'll try my best three minutes so in the
physical centers two functions one
function is um the distri the owner of
the centers will sell devices yeah in
China or if it's not Hardware it could
be just a learning account and another
function is to supervising and doing
data analyzing for the students who are
in the centers doing their self-directed
study for example we are in one
classroom from different schools and
different sub subjects and different age
we can just sit there in our own desk
and learning by our own and the teacher
or the supervisor is somewhere else and
monitoring us and there's a dashboard
for the supervisor and the supervisor
can monitor each different students
progress and learning if there's any
like alert like joling is frustrating or
they can the supervisor can also monitor
our facial recognition right what
happens if any student has any concerns
or problems or need support the
supervisor will you know come in and
talk to me and maybe drag me to another
classroom and have some uh uh
encouragement or if are all okay and
then after this like one hour uh I
finish I need to go home or I finish
this session of the learning the
supervisor will have a talk with me as
Jen this is the report we've got
generated from the system I can see
strength weaknesses learning speed blah
blah blah there's so many details about
the report and then maybe doing some
encouragement uh so next step what you
need to do is learning uh you you know
the root tracing system will recognize
you have some other gaps or maybe you
need to move forward you know that kind
of discussion and then I just go home
maybe that's for the centers or if the
students decide to learn at home that's
fine and everything is done at home but
there's no supervising at home the
parents cannot monit supervising at all
right but what the supervisor can do is
uh you are learning from home but I can
also see the report and all the data and
learning behavior from the system I can
call you and we call it online
supervising it's that after you study I
can call you and say let's have a zoom
meeting I found out some questions and
good things about your learning and then
you know you can doing the same things
through the zoom meeting so we call it
online supervision sorry 30 seconds left
yeah I think our session is almost there
but we have another session in the
afternoon so yeah more more than welcome
to come back and drop by we we also want
to talk to you like we like to ask for
advice and suggestion from you as well
all right thank you everyone thank you
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