China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY

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China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
China’s AI Adaptive
Learning Industry
March 2021

             China’s AI Adaptive Learning Industry Whitepaper I   1
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
Abstract                                                                        04

 I   OVERVIEW OF CHINA’S AI ADAPTIVE                                            05
     1.1 The Development of China’s AI Adaptive Learning

     1.2 Market Size of China’s K-12 AI Adaptive Learning

     1.3 Competitive Landscape of China’s K-12 Offline AI
     Adaptive Learning Industry

     1.4 Key Success Factors of China’s K-12 AI Adaptive

 II OVERVIEW OF CHINA’S OMO AI ADAPTIVE                                         28
     2.1 Background of OMO AI Adaptive Learning

     2.2 Business Models for OMO AI Adaptive Learning

     2.3 Exploration Direction of China’s OMO AI Adaptive

 III CHINA’S AI ADAPTIVE LEARNING MARKET                                        40
     3.1 Development trends of China’s AI Adaptive Learning

     3.2 Future of China’s AI Adaptive Learning Industry

                       China’s AI Adaptive Learning Industry Whitepaper I   2
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
China’s AI Adaptive Learning Industry Whitepaper I   3
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
With the breakthrough from computation                        This white paper will begin with the analysis of
power and algorithm capability, Artificial                    the industry status-quo, a definition of the
Intelligence (“AI”) has been evolving quickly in              intelligent level, an estimation of the market
simulating human intelligence and penetrating                 size of China's K-12 AI Adaptive Learning
multiple aspects of our daily life. “AI Adaptive              industry and a summary of key success factors.
Learning” is a major achievement after AI is                  Furthermore, Online-Merge-Offline (“OMO”)
applied to the education industry, which truly                business model will be discussed with its
helps to implement the teaching philosophy of                 background, form and future direction. In the
"teaching in accordance with one's aptitude".                 end, we will raise our preliminary thoughts
                                                              about how AI adaptive learning will evolve
In recent years, China has been encouraging
                                                              from social environment and competition
the implementation of digital and intelligent
                                                              landscape perspectives.
transformation in the field of education. In
2017, the State Council issued the                            Key insights and findings of this white paper:
“Development Plan for the New Generation of
                                                              ►   AI Adaptive Learning refers to the
Artificial Intelligence”, which defined the
                                                                  educational products in which artificial
pivotal position of AI in education. Shanghai is
                                                                  intelligence can take charge of the teaching
one step ahead of the others by issuing the
                                                                  activity to provide students personalized
“Shanghai Education Informatization 2.0
                                                                  learning experiences.
Action Plan (2018-2022)" to clarify the future
direction of intelligent education, in which "AI              ►   We estimate the China K-12 AI Adaptive
Adaptive Learning" was mentioned for the first                    Learning market will reach around USD 40
time.                                                             billion (RMB 270 billion) by 2025. The low-
                                                                  tier cities’ offline education channel will be
The market potential of AI Adaptive Learning is
                                                                  the main sub-segment of penetration.
growing rapidly with diversified service
offerings. Meanwhile, due to the high technical               ►   The market competition landscape is highly
barriers and changing application scenarios, its                  concentrated for now, with more than half
market definition and user recognition remain                     of the market share in the hands of a few
unclear. Under such situations, EY-Parthenon                      top players. However, it is also expected
publishes this white paper with an analysis on                    that more tech companies, traditional
the industry status-quo and future trends. We                     education institutions, and online education
hope this white paper could help market                           platforms will announce their entry into the
participants and users get a deeper                               market in the future.
understanding of AI Adaptive Learning and
                                                              ►   The key success factors of AI Adaptive
move the industry forward together.
                                                                  Learning enterprises are AI technology
                                                                  optimization, content development, and
                                                                  user data accumulation.
                                                              ►   OMO AI Adaptive Learning is a new
                                                                  exploration direction that can promote deep
                                                                  integration of multi-scene teaching and
                                                                  solve the shortcomings of traditional online
                                                                  and offline education.
                                                              ►   In the future, China's AI Adaptive Learning
                                                                  industry will show four trends: More
                                                                  traditional entrants, more comprehensive
                                                                  subject coverage, more education stage
                                                                  coverage and more approach oriented.

                                China’s AI Adaptive Learning Industry Whitepaper I   4
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
                    Overview of China’s
                    AI Adaptive
                    Learning Industry

China’s AI Adaptive Learning Industry Whitepaper I   5
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
1.1 The Development of China’s AI
Adaptive Learning Industry
In recent years, artificial intelligence (AI), big                              1.1.1 Development Background
data analysis, cloud computing and other
                                                                                 The idea of “AI Adaptive Learning” is derived
emerging technologies gradually penetrated
                                                                                from “Adaptive Education,” both of which
into all walks of life, driving the transformation
                                                                                adhere to the principle of teaching students in
of our society from the “Internet era” to a new
                                                                                accordance with their aptitudes. Adaptive
“intelligent era.” Meanwhile, AI-empowered
                                                                                Education came into the light when
application has been put in practice in all
                                                                                programmed learning theory and the teaching
aspects of the education industry and evolved
                                                                                machine were introduced in the 1920s. With
from single-scene and single-point teaching
                                                                                the development of information technology and
tools to educational products that integrate
                                                                                the popularizing application of AI in the past
multi-scenes and cover all links, and eventually
                                                                                years, AI Adaptive Learning had formed when
realizing a personalized and innovative
                                                                                AI was adopted in the core links in teaching.
teaching experience.
                                                                                Adaptive educational products have gone
Under this context, the concept of “AI Adaptive
                                                                                through 4 phases: Traditional Teaching,
Learning” came into being. AI Adaptive
                                                                                Efficiency Improvement, Decision-aid and AI
Learning refers to the educational products
                                                                                Adaptive Learning, during which the level of
that are based on the use of intelligent
                                                                                intelligence has been progressively improved,
technologies such as AI, big data analysis, and
                                                                                and more teaching links have been covered.
IoT, combined with a large amount of user data,
to provide a unique and personalized learning
experience for students in accordance with
their aptitudes. By simulating the process of
one-on-one human teaching, AI Adaptive
Learning alleviates the shortcomings of
traditional education, such as the inefficiencies
in large classes and the high cost of one-on-one

                                             2000                            2012                           2016
                     Traditional Teaching       Efficiency Improvement              Decision-aiding             AI Adaptive Learning
                             Stage                       Stage                          Stage                          Stage
                          Large class                   Dual-teacher class              Adaptive test               AI adaptive learning
 Intelligent Level

                                                                                                             

                         Small class                  Online broadcast                question database          AI teacher
                         One-on-one class             Intelligent                    Expression                 …
                                                        correction                      recognition
                         …
                                                       …                              AI teaching
                                                                                       …


                              Peripheral                                                                                     Core
                                                            Covered Teaching Processes
Sources: Deck research, EY-Parthenon analysis

                                                 China’s AI Adaptive Learning Industry Whitepaper I         6
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
1.1.2 Intelligence Levels of Adaptive                           ►   L4 - Advanced AI Tutoring (AA): AI is
Education                                                           adopted in the teaching link, and it leads the
According to the degree of intelligence,                            whole education process. Students receive
adaptive education can be ranked into six levels                    education from AI teachers on terminal
(L0-L5). Only when artificial intelligence enters                   devices, while real teachers are responsible
the core "teaching" link (L3-L5) can it be called                   for final checks and correction. These
“AI Adaptive Learning".                                             products are typically named as AI Adaptive
                                                                    Learning Platform or AI Teacher.
►   L0 - Traditional Teaching (TT): Traditional                     Representative companies include IBM
    real-person teaching in all links without                       Watson, BYJU’S, Realizeit abroad, and
    automation tools.                                               Squirrel Ai in China (some product lines
►   L1 - Internet Teaching (IT): Human-led                          include real teachers).
    teaching in all links, with the addition of                 ►   L5 - Full AI Teaching (FA): It is the ultimate
    digital systems such as online testing,                         form of AI Adaptive Learning. At this level,
    remote platform, etc. to improve teaching                       AI can fully simulate the teaching process of
    efficiency. Representative forms include                        excellent human teachers without
    online live classes, MOOCs, etc.                                intervention. The current international
►   L2 - AI Tools (AT): AI has been applied in                      representative is Korbit, an online AI
    some non-teaching links to further improve                      Adaptive learning platform founded in 2018.
    the efficiency, such as photo search, voice                     It was born out of the artificial intelligence
    testing etc., but the teaching is still led by                  lab led by Yoshua Bengio, a Turing Award
    real teachers. Representative products                          winner and renowned AI professor. This
    include Zuoyebang, Yuanfudao etc.                               platform can automatically teach courses
                                                                    including data science, machine learning and
►   L3 - Partial AI Teaching (PA): AI was                           artificial intelligence. Squirrel Ai, a leading
    adopted in the teaching process to provide                      enterprise in the industry, has also achieved
    systematic analysis and recommendation to                       the L5 level with some product applications
    assist teachers in decision-making, but the                     (such as AI Foundation Courses and free
    teaching process was still dominated by                         courses partnered with Ding-Talk) with the
    human teachers. Companies usually offer a                       wide application and fruitful data retrieved
    complete set of teaching solution, typically                    from China K-12 education adoption.
    named AI-assisted platform or AI assistant,
    achieve full-process teaching data access                   It is worth noting that though, in theory, AI can
    and diagnosis before-, during- and after-                   simulate or even surpass excellent teachers,
    class. Representative companies include                     and be applied to all aspects of education, we
    ALEKS, Knewton, Coursera, Khan Academy                      believe that under current AI’s technical level,
    abroad, TAL and iFLYTEK in China.                           it cannot replace the role of real teachers in
                                                                supervision, communication, motivation and
                                                                personality training. Therefore, it might be an
                                                                ideal form of education to have AI responsible
                                                                for teaching and real teachers for cultivating.
                                                                Looking beyond, with the technological
                                                                breakthrough, it is not ruled out that there will
                                                                be a higher level of intelligence that can
                                                                independently complete all the work of
                                                                teaching and cultivating.

                                  China’s AI Adaptive Learning Industry Whitepaper I   7
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
Teach                                Educate

                                          Definition          Diagnose     Plan       Teach      Practice   Feedback       Cultivate       Example
                                                                                                                                          Traditional
                      L0-TT Traditional Teaching
AI assists teaching

                                                                                                                                           offline education

                                                                                                                                          1-on-1, livestream,
                       L1-IT Internet Teaching
                                                                                                                                           MOOC, recorded

                                                                                                                                                                        Intelligent Level
                                                                                                                                          Zuoyebang
                      L2-AT AI Tools
                                                                                                                                          Yuanfudao

                      L3-PA Partial AI Teaching                                                                                           Knewton
AI leads teaching

                                                                                                                                          Iflytek

                                                                                                                                          IBM Watson
                      L4-AA Advanced AI Teaching                                                                                          Squirrel AI

                                                                                                                                          Korbit
                      L5-FA Full AI Teaching                                                                                              Squirrel AI
                                                                          AI Adaptive Learning                                                                   High
                          Human teacher       Internet tool       AI

            Sources: Deck research, Interviews, EY-Parthenon analysis

                      1.1.3 Overseas AI Adaptive Learning Market                              United States: Pioneer in AI Adaptive Learning
                      Overview                                                                with mature technical background, excellent
                                                                                              content, and clear IP rights. However, due to
                      With the rise of big data, AI and other
                                                                                              the multi-race, diverse student background,
                      technologies, AI Adaptive Learning has moved
                                                                                              academic performance is not the greatest
                      from theory to practice. In the early 2000s, the
                                                                                              indicator of education, it is difficult to realize
                      first AI adaptive learning product was born in
                                                                                              large-scale To-C promotion of AI Adaptive
                      the United States. Companies such as Knewton,
                                                                                              Learning products. At present, the AI adaptive
                      Realizeit have come into the public’s attention,
                                                                                              learning products in the United States are
                      which marked the start of AI Adaptive Learning
                                                                                              mainly focused on K-3 preschool education and
                      in commercial uses. The Indian market began
                                                                                              higher education. However, due to inability to
                      to emerge in 2010, with a few start-ups came
                                                                                              realize large-scale monetarization, a number of
                      to light. At present, due to the variance in
                                                                                              market players have been acquired by large
                      teaching content, technical level, and cultural
                                                                                              publishers. A representative of such is Knewton.
                      background in different countries, market
                      development diverges as well.

                                                               China’s AI Adaptive Learning Industry Whitepaper I      8
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
Europe: started later than the United States                         India: the last to enter the game, but due to
but has a similar level of the research base.                        India’s large population, the popularity of
Whereas, due to the complex language                                 Internet, unified K-12 content and urgent
background in Europe, To-C business is also                          demand for good grades, the development of
hard to achieve on a large scale. Limited by the                     AI adaptive learning products in the Indian
narrow domestic market, many European                                market has achieved large-scale promotion,
market players have entered the U.S. market                          primarily towards K-12 students. At present,
by cooperating with American universities,                           India is the most similar AI Adaptive Learning
among which a representative is Realizeit, an                        market to China, among which the classic
AI Adaptive learning platform enabler-                               representative is Byju's.

                                         Case Study of BYJU'S

1. Background                                                        Based on students’ proficiency and aptitudes,
                                                                     BYJU’S produces 5-minute videos for each
As a leading company, BYJU’S was founded in
                                                                     knowledge point and reorder them to provide
2011 aiming to provide students with an online
                                                                     customized learning paths. In terms of business
learning application covering math, physics,
                                                                     model, BYJU’S provides free courses at the
chemistry and biology as well as standardized
                                                                     beginning, then charges afterwards. This way,
test such as JEE, NEET, CAT, IAS, GRE, and
                                                                     it can cultivate users’ behavior and bring in
GMAT. BYJU’S main products are Early Learn
                                                                     large amount of registered users.
App for G1-G3 students and The Learning App
for G4 – G12 students.                                               3. Accomplishments
2. Features                                                          To date, BYJU’S has accumulated 75 million
                                                                     registered users, covering 1,701 cities, and is
As one of the most popular learning APP in
                                                                     one of the largest online education company in
India, BYJU’S major target user is individual
                                                                     India. It has extended its partnership worldwide.
students, dedicated to promoting students’
                                                                     BYJU’S is currently valued at USD 10.8 billion,
autonomous learning in their spare time. The
                                                                     with 19 financing projects completed,
product’s most prominent feature is its
                                                                     accumulating to a total of USD 2,688 million, of
exquisite videos.
                                                                     which are many well-known PE/VCs such as
                                                                     Sequoia Capital, Tencent, etc.

     $9M             $25M           $75M            $65M           $30M            $40M            $100M      $2.24B

      Series A        Series B       Series C        Series D        Series E        Series F      Series G   Pre-IPO

   Sources:, BYJU’S Website, Desk Research, Interviews, EY-Parthenon analysis

                                       China’s AI Adaptive Learning Industry Whitepaper I      9
China's AI Adaptive Learning Industry Whitepaper - March 2021 - EY
1.1.4 Major Application in China                                                ►     China’s K-12 education is closely tied to
                                                                                        Gaokao (the college-entrance exam), which
  Due to the different educational cultures
                                                                                        leads to a higher willingness to pay for K-12
  between China and the U.S., the areas of
                                                                                        education from Chinese parents. EY-
  application are quite different even though
                                                                                        Parthenon estimates the size of China’s
  China’s AI adaptive learning industry bought
                                                                                        overall education market has been growing
  lots of experience from US in the early stage. In
                                                                                        steadily at a CAGR of 22% in recent years.
  China, the major application area focuses on
                                                                                        Meanwhile, K-12 education outgrows at a
  the K-12 education, mostly due to two reasons:
                                                                                        CAGR of 30% among other sub-segments.
  ►   Compared with the other education                                                 Long-term speaking, although the overall
      segments, K-12 education in China is more                                         education market shrunk in 2020 due to the
      exam-oriented and knowledge point driven.                                         COVID-19 outbreak, we remain optimistic
      As a result, students’ learning effects are                                       that the market will soon recover, with K-12
      quantifiable, making K-12 education the best                                      education still being the fastest growing.
      landing point for initial application. While
      education in US focuses more on the
      thinking process than pure knowledge input,
      leaving little room for the application of AI
      Adaptive Learning.

China's Education Market Size
       Unit: Billion USD

             Pre-school         K-12          Vocational         Higher         Other
                                                                                                                                     CAGR     CAGR
                                                                                                                                     17-19   20E-25E

                                                                                                               445                   22%      18%

                                                                                                  375          18%                   15%      21%

                                                                                      316         18%

                               257                                     267            17%
                               15%                         225            17%                                                        30%      22%
                                             191           17%                                    49%
      172         16%                                                                 48%
      17%                       48%                                       47%
                  46%                                      45%                                                                       11%      15%
      42%                                    44%                                                               13%
                                                                                      13%         13%
                          13%                                14%
      15%         13%                        15%                                                               12%                   18%      10%
                          15%                                15%                    14%          13%
      16% 9%      16%                        17%           16%
                      9%   9%                    8%       8% 8%                      7%           7%            7%                   22%      15%
      2017       2018    2019               2020E   2021E   2022E                  2023E        2024E          2025E

Sources: Deck research, Interviews, EY-Parthenon analysis
Note: The figures are converted from RMB into USD according to the exchange rate of RMB against USD (647:100) on February 18, 2021

                                                China’s AI Adaptive Learning Industry Whitepaper I        10
1.2 Market Size of China’s K-12 AI
Adaptive Learning Industry
As mentioned above, China's K-12 AI Adaptive                  Compared to the offline education market, the
Learning industry is undergoing a period of                   online market is a relatively new and small
rapid development. Many existing players and                  market, derived from the digital transformation
new entrants are sparing no effort to conduct                 of K-12 education in recent years. It is
quick entry and business monetization in this                 estimated that the size of China’s K-12 online
market.                                                       education market is expected to reach 63
                                                              billion by 2025 at a CAGR of 35%, with the
1.2.1 Market Size of China’s K-12 Education
                                                              following key drivers:
China’s K-12 education market can be divided
                                                              ►   Demand side: the problem of unequal
into online and offline channels. The offline
                                                                  distribution of educational resources in
market is formed by traditional education
                                                                  different regions has long existed. Online
institutions, offering face-to-face teaching. The
                                                                  education products have effectively solved
online market is an emerging market generated
                                                                  the pain point of the lack of high-quality
by moving part or full of the teaching activity
                                                                  educational resources in remote and rural
online in recent years.
The offline education market is the main
                                                              ►   Supply side: the launch of many online
battlefield of K-12 education institutions
                                                                  educational products has transformed the
traditionally. We estimate the offline education
                                                                  traditional teaching scenarios, breaking
market has reached approximately USD 115
                                                                  through the limitations of time and space in
billion in 2019, attributable to the following
                                                                  offline education and bringing new
driving factors:
►   Demand side: Chinese households’
                                                              We believe the offline market will still be the
    disposable income continues to rise, and the
                                                              main battlefield of K-12 education in the long
    consumption of educational products is also
                                                              run, which can be referred to from what
    growing, driven by traditional channel
                                                              happened in the retail industry. The online
                                                              retail business has shown explosive growth in
►   Supply side: Education institutions began to              the past decades. However, according to the
    focus on low-tier cities, the potential                   National Bureau of Statistics, its proportion in
    demand for high-quality educational                       the total sales of consumer goods only
    recourses in the sinking markets is further               accounts for 25% of consumption made offline.
    released.                                                 The main reason is that online retail cannot
                                                              make up for the same consumer experience
                                                              offline. Similarly, in the education sector,
                                                              customers will pay more attention to the
                                                              teaching experience when choosing the best
                                                              education product. Offline teaching tends to be
                                                              more effective by offering direct interaction,
                                                              supervision and motivation, thus offline
                                                              education will remain the first choice for
                                                              parents and students.

                                China’s AI Adaptive Learning Industry Whitepaper I   11
China’s K-12 Education Market Size

      Unit: Billion USD
                                                                                                               224                   CAGR     CAGR
                                                                                                                                     17-19   20E-25E
           Online Education               Offline Education
                                                                                                 184            63                   30%      22%
                                                                                    150           48
                                122                                    124           35                                              73%      35%
                   96 5                                   102           26
                                             84           20
      72    2                                                                                                  161
                                             14                                                  136
                                116                                                 115
                   91                                                   98
                                             70            82
     70                                                                                                                              29%      18%

    2017         2018         2019        2020E         2021E        2022E        2023E         2024E          2025E

Sources: Deck research, Interviews, EY-Parthenon analysis
Note: The figures are converted from RMB into USD according to the exchange rate of RMB against USD (647:100) on February 18, 2021

                                                China’s AI Adaptive Learning Industry Whitepaper I        12
China’s Offline K-12 Education Market Size

      Unit: Billion USD
                                                                                                               161                   CAGR     CAGR
                                                                                                                                     17-19   20E-25E
           1st&2nd Tier Cities            Low-tier Cities                                        136
                                                                                                                                     29%      18%
                               118                                                  114
                   92           33                                                    29                                             20%      16%
      71                                                                25
                   28                        70
                                                           22                                                  121
      23                                     19
                                85                                                    85
                   64                                                   73
                                             51            60
      48                                                                                                                                      18%
                                                                                                                                     33%      19%

    2017         2018         2019        2020E         2021E        2022E        2023E         2024E          2025E

Sources: Deck research, Interviews, EY-Parthenon analysis
Note: The figures are converted from RMB into USD according to the exchange rate of RMB against USD (647:100) on February 18, 2021

  Taking a closer look of the K-12 offline                                        ►   Supply side: EY-Parthenon calculates that
  education market from the city levels, it is                                        the current share of large education
  found that more than 70% of the market is                                           institutions in the lower-tier market is only
  concentrated in the low-tier cities, reaching a                                     around 2%. Facing market saturation in first-
  scale of USD 1.5 billion and a CAGR higher than                                     and second-tier cities, more and more of
  that of first- and second-tier market with the                                      these players begin to explore into the low-
  following key drivers:                                                              tier cities to maintain their growth
                                                                                      momentum, which will bring a new round of
  ►   Demand side: according to the National
      Bureau of Statistics, there are 180 million
      primary and secondary school students in                                    Greater market potential of China K-12 offline
      China, of which more than 70% come from                                     education can be found in low-tier cities, which
      low-tier cities. The huge student base lays                                 implies that how to compete for these students
      the foundation for the market prospects.                                    and lay out an efficient route will be the key
                                                                                  strategic direction of market players in the
                                                                                  near future.

                                                China’s AI Adaptive Learning Industry Whitepaper I        13
1.2.2 Market Size of China’s K-12 AI Adaptive                  ►   Technology: the development of hardware
Learning                                                           and software had improved the application
                                                                   effect of AI Adaptive Learning. In recent
EY-Parthenon calculates the market size of
                                                                   years, the performance of computing chips
China’s K-12 AI Adaptive Learning industry
                                                                   has greatly improved on the cost-
which has reached USD 4.6 billion in 2019 and
                                                                   effectiveness, which provides the foundation
is expected to reach USD 40 billion by 2025
                                                                   for wide application of AI Adaptive Learning.
at a CAGR of 62%. Our projection is based on
                                                                   With the advancement of AI, deep learning
the following factors:
                                                                   and algorithm model, AI Adaptive Learning
►   Policy: National and local policies have                       had moved from a theoretical level to
    continually defined the strategic positioning                  practical application. Besides, the high-
    of AI Adaptive Learning. At the national                       quality labelled data gradually accumulated
    level, the State Council issued the “New                       during the interaction between user and
    Generation Artificial Intelligence                             application process, which further improves
    Development Plan” in June 2017, proposing                      the algorithm accuracy and enables the
    a new education system that includes                           product effect to be proved.
    intelligent learning and interactive learning
                                                               ►   Supply: the intensified competition in the
    and promoted the application of AI in the full
                                                                   education industry has enhanced the cost-
    process of teaching, management and
                                                                   reducing and efficiency improving features
    resources construction, bringing AI
                                                                   of AI Adaptive Learning. In recent years,
    education to a national strategic level for the
                                                                   with the entry of more online players, the
    first time. In April 2018, the Ministry of
                                                                   traditional education industry has been
    Education issued the “ Action Plan for
                                                                   abundant with more novel product forms
    Educational Informatization 2.0 ” to
                                                                   and business models, leading to a more
    vigorously promote intelligent education,
                                                                   competitive market. AI Adaptive Learning
    carry out the construction of intelligent
                                                                   technology, aiming at replacing human
    teaching environment, and promote the
                                                                   teachers, can be a way to reducing cost and
    application of artificial intelligence in
                                                                   improving efficiency for institutions, and
    education. On a local scale, in September
                                                                   additionally, providing differentiated
    2018, the Shanghai Education Commission
                                                                   features in terms of customer acquisition
    issued “Shanghai Education Informatization
                                                                   and teaching.
    2.0 Action Plan (2018-2022)”, which
    suggested “to explore new education form
    based on Knowledge Graph and AI adaptive
    technology, and to create unified-standards
    shanghai solution to crowdfund and
    crowdsource academic resources. This plan
    explicated the explorative direction of AI
    Adaptive Learning.

                                 China’s AI Adaptive Learning Industry Whitepaper I   14
China’s AI Adaptive Learning Market Size

  Unit: Billion USD                                                 Online Education             Offline Education

  Penetration of Intelligent adaptive education:
     4%           4%            4%           5%           6%            8%          11%           15%          19%
                                                                                                               42.1                  CAGR     CAGR
                                                                                                                                     17-19   20E-25E

                                                                                                                                     31%      62%
                                                                                                                                     97%      55%
                                                                                   17.1                        86%
                                                                       10.6        14%
                                                          6.5          15%                       87%
                                             3.8          15%
                   4.5                                                              86%
            4.3                              17%
     2.6        3%     4%                                              85%
         2% 97%    96%                                    85%                                                                        30%      63%
     98%                                     83%
    2017         2018         2019          2020E        2021E        2022E         2023E       2024E          2025E

Sources: Deck research, Interviews, EY-Parthenon analysis
Note: The figures are converted from RMB into USD according to the exchange rate of RMB against USD (647:100) on February 18, 2021

  We foresee the penetration rate of AI Adaptive                                 base of K-12 offline institutions in low-tier
  Learning products in China’s K-12 education                                    cities and their high demand for AI adaptive
  market will increase from 4% to 19% in the                                     learning solutions. On the other hand, the high
  next five years with a faster penetration rate in                              customer acquisition cost in the online
  the offline segment compared to online. By                                     channels makes fewer players put the online
  2025, the offline market is expected to                                        channel as their major focus.
  account for 86% of China’s K-12 AI Adaptive
  Learning market, mainly because of the huge

                                                China’s AI Adaptive Learning Industry Whitepaper I        15
1.2.3 Penetration Trends of AI Adaptive                                                    Based on China’s K-12 population, EY-
 Learning in K-12 Education                                                                 Parthenon first estimates the quantity of K-12
                                                                                            education institutions in China, considering the
 In order to analyze the route of AI Adaptive
                                                                                            adoption rate, annual tuition fee and revenue
 Learning’s penetration into the offline channel,
                                                                                            scale of K-12 education institutions in first- and
 we pursued further analysis of the penetration
                                                                                            second-tier cities. It is estimated that there are
 rate in the offline segment by different types of
                                                                                            around 1 million K-12 education institutions in
 offline institutions.
                                                                                            China: 500,000 to 800,000 institutions with an
                                                                                            annual revenue less than USD 30 thousand,
                                                                                            followed by 50,000 to 100,000 small
                                                                                            institutions with an annual revenue between
                                                                                            USD 30 thousand - 3 million and 700 -1,400
                                                                                            medium-sized institutions with annual revenue
                                                                                            between USD 3 - 75 million. There are only 20
                                                                                            large-scale institutions with annual revenue
                                                                                            above USD 75 million in China, accounting for
                                                                                            about 0.1% of the market.

                                              Method of Calculating the Number of K-12 Education Institutions

                                               Core logic                                                      Segmentation factors
   Number of K-12 education institutions

                                                  K-12 population size                                City level: 1st & 2nd tier/lower-tier

                                           Penetration rate of K-12 education                         City level:1st & 2nd tier/lower-tier
                                                       institution                                    Institution type:Large/Medium/Small/Micro

                                           Per customer transaction of K-12                           City level: 1st & 2nd tier/lower-tier
                                                 education institution                                Institution type :Large/Medium/Small/Micro

                                            Revenue scale of K-12 education
                                                      institution                                     Institution type :Large/Medium/Small/Micro

Sources: Deck research, Interviews, EY-Parthenon analysis

                                                              China’s AI Adaptive Learning Industry Whitepaper I   16
Number and Revenue Scale of China’s K-12 Education Institutions

                                                          Number and portion                                       Total Rev. scale and portion

     Large institutions                                                                                                                      Unit: Billion USD
     (Annual rev. > $78M)                          ~20                                                             ~4.8(~7%)

  Mid-sized institutions
  (Annual rev. $3M - $78M)                            700-1400                                                     ~4.5(~6%)

     Small institutions                                                                                                      ~22
  (Annual rev $300k - $3M)                                      50-100K                                                      (~31%)

           Micro-scale                                                                                                                    ~39
           institutions                                                                500-800K                                           (~55%)
     (Annual rev. < $300k)

Sources: Deck research, Interviews, EY-Parthenon analysis
1) The number of institutions is calculated by brand, that is, if there are multiple branches under the brand, it is calculated by one.
2) The figures are converted from RMB into USD according to the exchange rate of RMB against USD (647:100) on February 18, 2021

Penetration of AI adaptive learning in China’s K-12 Offline Education Institutions
       Large           Mid-sized            Small            Micro            Penetration of AI adaptive learning products                   Unit:Billion USD

               1ST&2nd tier                    Lower-tier                                        1ST&2nd tier                 Lower-tier

                                     2020E                                                                                  2025E

Sources: Deck research, Interviews, EY-Parthenon analysis

                                                    China’s AI Adaptive Learning Industry Whitepaper I           17
Looking into the penetration of AI Adaptive                      20%. As a result, traditional classroom tutoring
Learning products of different city tiers, EY-                   cannot meet the needs of every student, but it
Parthenon found that low-tier cities will be the                 is exactly what AI adaptive learning is capable
focus of penetration in the future due to the                    of.
following reasons:
                                                                 Small and mid-sized education institutions are
►   The lack of high-quality educational                         the main users of AI Adaptive Learning
    resources in low-tier cities can be                          products currently, and we predict it will
    effectively alleviated through the                           gradually penetrate mid and large-sized
    introduction of AI Adaptive Learning                         institutions in the future:
    products. The greatest dilemma faced by
                                                                 ►   Small and mid-sized institutions are
    education institutions in low-tier cities is the
                                                                     generally difficult to recruit excellent
    lack of excellent teachers, which could be
                                                                     teachers and develop curriculum systems,
    replaced by AI Adaptive Learning products
                                                                     which makes them eager to cooperate with
    to solve the pain points.
                                                                     AI Adaptive Learning platforms.
►   The education expenditure level in low-tier                      Traditionally, small and mid-sized institutions
    cities is limited, and AI Adaptive Learning                      in low-tier cities are more likely to face the
    products with higher cost-effectiveness are                      problem of teacher shortage and weak
    more likely to be welcomed. The operating                        curriculum content. Through cooperating
    costs of AI Adaptive Learning products are                       with upstream suppliers and apply adaptive
    much lower than one-on-one or small-class                        content into their teaching, these
    tutoring no matter it is conducted online or                     institutions can build their own competitive
    offline, which makes AI Adaptive Learning                        advantages to realize brand upgrades.
    products cheaper and more appealing to the
                                                                 ►   Traditional large-size education institutions
    low-tier demographic.
                                                                     are more cautious in adopting AI adaptive
►   As customers in low-tier cities need more                        learning, but it will be part of their business
    time to accept new technologies, higher                          portfolio in the end. Though the giants of
    potential will be inspired along with the                        traditional education institutions have
    penetration in the future. At present,                           deployed large R&D resources into AI
    customers from tier 1 and 2 cities have                          Adaptive Learning related products, most of
    better access and willingness of adoption to                     them have not yet been mainstream
    new technologies. However, with more                             business. There are 2 major reasons behind:
    solutions implemented in lower-tier cities, AI                   1) Large-sized institutions are mostly
    Adaptive Learning businesses can bring                           located in first-tier and second-tier cities and
    confidence to the customers there and                            it has good teacher resources; 2) Most large
    increase their adoption rate in the long run.                    institutions are directly operated with heavy
                                                                     asset, which is unlikely to have a rapid
►   Capability gaps among students in lower-
                                                                     expansion in short-term. However, when
    tier cities are larger than higher-tier cities,
                                                                     large-sized institutions are ready to
    which gives AI adaptive learning a better
                                                                     penetrate lower-tier cities, we believe they
    chance to show its benefits in “customized
                                                                     will adopt AI Adaptive Learning as a crucial
    tutoring”. According to the Ministry of
                                                                     part of their business to compete in new
    Education, the proportion of junior high
    students entering in senior high school is
    about 70% in first-tier cities, such as Beijing
    and Shanghai, while the proportion in low-
    tier cities is about 50% with some less than

                                   China’s AI Adaptive Learning Industry Whitepaper I   18
1.3 Competitive Landscape of
 China’s K-12 Offline AI Adaptive
 Learning Industry

                                                                Technology Suppliers                              Content Suppliers
    Suppliers              AI adaptive
                                                      Voice          Image       System       Lower           Question        Lecture     Teaching
                                                      process        process     building     terminal        bank            video       content
                       algorithm suppliers

                                                                          AI adaptive
                                                                  learning platform enablers
                                                                                                                         AI adaptive education

                                                                Education institutions

    Students                                                                       Students

       Core players in AI adaptive learning industry

Sources: Deck research, Interviews, EY-Parthenon analysis

 1.3.1 Overview of China K-12 AI Adaptive                                         ►    Upstream suppliers: including AI adaptive
 Learning Industry Chain                                                               algorithm suppliers, technology suppliers
                                                                                       and content suppliers. AI algorithm
 China’s K-12 AI Adaptive Learning industry
                                                                                       providers typically have higher technical
 chain can be divided into three parts: upstream
                                                                                       advantages, which enables offline education
 suppliers, product development, and education
                                                                                       institutions directly. Technology provider
                                                                                       includes voice-recognition, cloud service and
                                                                                       other application developers. Content
                                                                                       publisher provides question databases,
                                                                                       teaching videos and materials, etc. Choosing
                                                                                       from upstream suppliers could be highly
                                                                                       fragmented since the cooperation model will
                                                                                       be varying according to different capability
                                                                                       distribution among downstream players.

                                                 China’s AI Adaptive Learning Industry Whitepaper I      19
►   Platform enablers: providing content and                 The core players in the chain are adaptive AI
    algorithm platforms. Services include                    algorithm suppliers, platform suppliers and self-
    knowledge graph development, labelling of                operated education institutions. These players
    knowledge points, and user-data-based AI                 can rely on their own platform to help
    algorithm development.                                   traditional education institutions or establish
                                                             their own institutions to serve to the K-12
►   Education institutions: small or mid-sized
    education institutions are the major
    customer of platform enablers for now. They              Meanwhile, most AI Adaptive Learning
    get franchised or licensed from platform                 platforms are adopting B2B business model at
    enablers to get curriculum content, front-               the current stage to achieve profitability and
    end and back-end applications, operating                 gain the first-mover advantage by enabling
    training, marketing support, etc. We also                education institutions with intelligence. Long-
    observed another type of full-fledged                    term speaking, as the market expands and
    education institutions that has their own                matures, we can anticipate more players with
    platform for their users.                                B2C business models will begin to emerge.

                               China’s AI Adaptive Learning Industry Whitepaper I   20
1.3.2 Overview of China K-12 AI Adaptive                                   ►   L4&L5 - Advanced AI Tutoring & Full AI
  Learning Offerings                                                             Teaching: It leverages AI Adaptive Learning
                                                                                 system to identify weak link for each student
  As shown in the figure above, there are two
                                                                                 via AI deep learning algorithm, knowledge
  major offering categories of K-12 AI Adaptive
                                                                                 evaluation method and knowledge tracking
  Learning in China:
                                                                                 theory before class to generate personalized
  ►   L3 - Partial AI Teaching: It uses AI-based                                 learning path. During classroom tutoring,
      tests to conduct a personalized diagnosis                                  each student will use handy devices (e.g., AI-
      before classroom tutoring, and teachers can                                integrated machine, PC, Tablets, etc.) to
      make lecture notes based on the diagnosis                                  learn based on pre-defined learning path
      report. During tutoring, the human teacher                                 whilst human teacher will be only
      will lead the teaching activity with support                               responsible for the supervision, emotional
      from ad-hoc online personalized exercises to                               counseling of the students. After class, the
      adjust content in real-time. After class,                                  platform will provide personalized exercises
      personalized homework with smart                                           to students and intelligent correction
      correction function will be generated based                                functions to human teachers. Some
      on student’s behavior during class.                                        institutions will provide human teachers to
      Homework result will also be shared with the                               answer unsolved questions after-school as a
      human teacher to provide later explanations                                value-added service to improve tutoring
      to students.                                                               quality. Simultaneously, the system will also
                                                                                 provide the report to parents which contains
                                                                                 multi-dimensional data covering learning,
                                                                                 exercise and behavior to generate a
                                                                                 comprehensive deep view of their kids.

                                Diagnose                  Planning                  Teaching                Practice                  Feedback

                                                   According to the                                 Personalized
                                                                            Real teachers-led,
                                                    standardized                                      question                Analysis of
                         Based on AI                                        with intelligent
                                                    material and                                      recommendation           students' learning
      L3                  competence test,
                                                                                                     Intelligent              situation based
                          a customized                                       technology
   Partial AI             diagnosis is
                                                                                                      correction               on small amount
                                                    teachers make                                    Teachers provide         of data such as
   Teaching               formed
                                                    lecture notes
                                                                             learning data to
                                                                                                      explanation              exercises and
                                                                             help teachers
                                                    tuned to the                                      basing on the            tests
                                                                             make decisions
                                                    class’s level                                     class test result

                                                                                                     Personalized
                                                   The system                                        question                Analysis of
                                                                             AI-led, with real
   L4&L5-                Based on AI               recommends
                                                                                                      recommendation           students' learning
 Advanced AI              competence test,          customized                                       Intelligent              situation based
                                                                             responsible for
                          a customized              learning path for                                 correction               on multi-
Tutoring & Full           diagnosis is              each student and
                                                                             supervision and
                                                                                                     Based on each            dimensional data
 AI Teaching              formed                    teachers can tune
                                                                                                      students, AI carry       of the whole
                                                    on such basis                                     out customized           learning process

Sources: Deck research, Interviews, EY-Parthenon analysis

                                             China’s AI Adaptive Learning Industry Whitepaper I   21
Comparing the two main offerings of AI                                         “AI” teach, which disrupts our prior
  Adaptive Learning, L3 is an enhancement to                                     acknowledgment to traditional education. The
  traditional education by assisting human                                       two different starting points lead to differences
  teachers. L4&L5 are real game-changers to                                      in depth, coverage and application scenarios of
  replace the role of human teachers by letting                                  AI technology.

                                                    L3-                                                    L4&L5-
                                            Partial AI Teaching                             Advanced AI Tutoring & Full AI Teaching
                                                                                                     Collect multi-dimensional data, covering
                                   Collect fewer dimensional data in small                           the whole process of teaching
            Technical               amount
              level                                                                                  Involves multi-link data analysis and
                                   Relatively simple algorithm, uses more                            customized path recommendation, uses
                                    recognition AI technology                                         more strategy AI technology

                                   Real teachers in core teaching links,
                                                                                    VS               AI-led whole process teaching, with real
                                                                                                      teachers responsible for support
            Teaching                assisted by intelligent adaptative tools
             mode                                                                                    Mixed-class-mixed-subject method,
                                   Traditional education institutions                                where students of different levels and
                                    teaching method                                                   subjects can study in one classroom

                                   Schools with sufficient educational                              Schools lacking educational resources:
                                    resources: full-time schools in first- and                        small and mid-sized educational
             scenarios              second-tier cities and traditional                                institutions in low-tier cities
                                    educational institutions

Sources: Deck research, Interviews, EY-Parthenon analysis

                                                 China’s AI Adaptive Learning Industry Whitepaper I     22
1.3.3 Competitive landscape of China’s K-12
 AI Adaptive Learning

                         Market share                 Number of players                                Company feature

                                                                                   >  2,000 partner institutions
                                                                                    Distributed country-wide, with large customer base and
  Leading                                                                            revenue scale
  players                    ~60%                           2-3                     Strength in technology capability, subject coverage,
                                                                                     branding and other aspects, maintain a leading role in the

                                                                                      < 2,000 partner institutions
                                                                                       Started late, mostly start-ups or new business incubated
      Tail                                                                         

    players                  ~40%                           >50                    
                                                                                       by large enterprises
                                                                                       Has not achieved large-scale profit yet, some of which
                                                                                       have growth potential due to advantage in niche segment

Sources: Deck research, Interviews, EY-Parthenon analysis

We estimate there are around 50 – 70 K-12 AI                                  Overall speaking, the current China’s AI
Adaptive Learning platform companies in China,                                Adaptive Learning market is highly
with a total of 10,000 franchise or direct-                                   concentrated (CR2>50% in terms of institution
owned institutions. Based on their market                                     quantity). However, we think the competitive
share and business scale, we further                                          landscape still has the chance to be disrupted in
categorized them into leading players and                                     future due to the following reasons:
                                                                              ►   Rapid market expansion: AI Adaptive
►    Leading players: There are only 2-3                                          Learning has enormous market potential and
     institutions with more than 2000 franchise                                   its penetration in the K-12 education market
     or direct-owned institutions currently,                                      is currently low. The large potential for
     accounting for 60% of the total market.                                      market expansion has attracted players from
     Their institutions are distributed across the                                different industries to enter, resulting in
     country due to their competitive advantages                                  many tail players.
     of technical strength, strong subject
                                                                              ►   High technical barriers: AI Adaptive
     coverage and brand communication. Squirrel
                                                                                  Learning solutions require large investments
     Ai, iFLYTEK are two leading players with
                                                                                  into R&D and data collection at the early
     their market share still growing rapidly.
                                                                                  stage due to high capital and talent needs.
►    Tail players: most players in this group have                                Tail players still have the chance to set up
     dozens to hundreds of institutions for now                                   their own moat if they have the right
     due to getting into the market relatively late.                              capability and sufficient investments.
     However, the players can still deepen in
                                                                              Looking into the future, we expect more
     their niche market by relying on regional
                                                                              emerging players will enter the market due to
     advantages. Backgrounds of these tail
                                                                              the further release of demand in the low-tier
     players vary from technology companies,
                                                                              cities. Particularly, technology companies will
     online education companies and traditional
                                                                              leverage their strength in AI technology and
     education companies. They think AI Adaptive
                                                                              enter into the L4 or L5 AI adaptive learning
     Learning could be helpful to enter the
                                                                              market directly, which may bring more
     market quickly, especially for lower-tier
                                                                              challenges to the current leading players. Tail
     cities. Their solutions are at the early stage
                                                                              players without a good business model and
     and far from scale.
                                                                              enough investments will be difficult to survive
                                                                              in the market. Meanwhile, current leading
                                                                              players may further diverge, with few top
                                                                              players evolving into full-fledged AI adaptive
                                                                              learning providers while others have specific
                                                                              strengths in dedicated areas.

                                                China’s AI Adaptive Learning Industry Whitepaper I   23
1.4 Key Success Factors of China’s
K-12 AI Adaptive Learning
                                         Key Success Factors of
                                          AI Adaptive Learning

            AI technology                           Content                               User data
             optimization                        development                             accumulation

Although operating models vary among                         ►   Strong and market-leading technical talents:
different AI Adaptive Learning industry players,                 In the international academic community,
the key success factors can be summarized in                     the development of frontier AI theory is
three common elements: AI technology                             changing, and the effect is becoming more
optimization, content development, and user                      and more intelligent all the time. Given that
data accumulation.                                               AI is a field with high technology barrier,
                                                                 global talents are always in need, and AI
1.4.1 Key Success Factor 1: AI Technology
                                                                 Adaptive Learning companies should ensure
                                                                 the strength of their AI R&D team by
The AI algorithm used by the AI Adaptive                         introducing more talents with diverse
Learning system is referred as Strategic AI                      academic background and maintain a close
Algorithm which is different from Recognition                    relationship with academic institutions to
AI Algorithm used in face recognition, image                     stay ahead in both theoretical and practical
recognition, speech detection and other                          means.
applications and puts forward a stricter
                                                             ►   Multiple technical routes in AI algorithms to
requirement for algorithm structure and
                                                                 maximize performance: In the course of
training. Furthermore, the effect of customized
                                                                 years' research of AI Adaptive Learning,
learning diagnosis and learning path planning
                                                                 many kinds of AI algorithm theories have
largely depends on the excellence of the AI
                                                                 been formed. Each one is more suitable for
algorithm model. For that reason, AI Adaptive
                                                                 solving a specific problem in the teaching
Learning players are investing heavily in
                                                                 process. The practice has proved that there
developing AI algorithm and strive to achieve
                                                                 is no one-size-fits-all AI algorithm model for
the optimization of AI algorithm technology
                                                                 all application scenarios. Under the complex
with the following key considerations:
                                                                 and random scenarios of different students,
                                                                 it may be the only way to realize the
                                                                 ultimate goal of adaptive education by
                                                                 adopting various algorithm models and

                               China’s AI Adaptive Learning Industry Whitepaper I   24
1.4.2 Key Success Factor 2: Content                          to the algorithm of AI adaptive learning system
Development                                                  during curriculum development. In essence,
                                                             traditional curriculum content needs to be
Curriculum content development has always
                                                             optimized and reshaped, particularly in the
been closely linked with the competitiveness of
                                                             following aspects:
traditional K-12 education institutions. For AI
Adaptive Learning players, they should not
only know how to teach students with
knowledge points, but also how to adapt
teaching process

                    Refined knowledge                                      Highly localized
                           map                                            teaching material

                                       Wider coverage of subjects

                                        Low                          High
                                          R & D level of course

                               China’s AI Adaptive Learning Industry Whitepaper I   25
►   Refined knowledge map to maximize                         ►   Highly localized teaching material:
    personalization: China’s K-12 education                       Presently, the industry players develop the
    system is mostly exam-oriented. The                           teaching materials mainly based on several
    measurement of student’s capability highly                    mainstream versions of textbooks from
    depends on the mastery of relevant                            different provinces in China. However,
    knowledge points. Students cannot get a                       considering of widely differentiated
    higher score without the establishment of a                   textbooks and question types among
    complete knowledge graph. Fine granularity                    different provinces in China, it will maximize
    of subject knowledge points is essential for                  students’ learning efficiency if more
    an AI Adaptive Learning solution to assess                    appropriate teaching materials can be
    student’s capability accurately and provide                   developed to match with their daily-used
    personalized learning content efficiently. If                 textbooks.
    the content reserve is limited (not enough
                                                              ►   Wider coverage of subjects: Current China
    materials) and the knowledge points are not
                                                                  AI Adaptive Learning players are still
    precise enough (a video corresponds to
                                                                  fragmented in targeting primary and middle
    multiple knowledge points and last more
                                                                  school students, with more focus on science
    than 5 minutes), there will be only a few
                                                                  subjects. As a result, many parents need to
    fixed learning paths or content to
                                                                  use multiple platforms at the same time to
    recommend for students, which is difficult to
                                                                  cover their children’s whole learning period,
    realize the effect of the real customized
                                                                  which can be unfavorable to both customer
    learning. Therefore, to improve the fineness
                                                                  cultivation and customer loyalty. Companies
    of knowledge points, some leading
                                                                  with broader coverage of subjects will get
    companies choose to design nano-level
                                                                  higher customer satisfaction and credits.
    knowledge graph through research and
    develop related teaching content (such as
    short videos, animation, etc.) on their own.
    The whole R&D process involves a large
    amount of investment and time input but can
    set up a high barrier and enlarge the gap
    with competitors once completed.

                                China’s AI Adaptive Learning Industry Whitepaper I   26
1.4.3 Key Success Factor 3: User Data                          From this point of view, platform that wants to
Accumulation                                                   build up the competitive advantage should
                                                               expand their coverage to the whole learning
User data is becoming a common barrier in the
                                                               process as soon as possible to start user data
digital era, which also works in the AI adaptive
                                                               accumulation. On the contrary, those question
learning area. The large amount of student and
                                                               databases and homework apps only have
teacher data generated through teaching
                                                               access to student’s behavioral data in one
activity could lead to a huge competitive
                                                               learning link, and the static student portrait is
advantage in product and operation
                                                               difficult for the system to generate real-time
►   Achieving customized teaching requires the
                                                               ►   Proper mining and utilization of user data
    storage of the entirety of learning process
                                                                   generated during the application of AI
    data, in which AI Adaptive Learning
                                                                   Adaptive Learning can bring great
    platform has a natural advantage. The data
                                                                   commercial value to the company. In
    set needed for AI algorithm training comes
                                                                   current AI Adaptive Learning solutions, user
    from the whole learning process of each
                                                                   data such as personal data (name, age, etc.),
    user, including feedback and interactive data
                                                                   learning data (knowledge structure and
    between learner and teacher. It helps to
                                                                   progress, etc.) and behavior data (time to
    generate the portrait of a student in real-
                                                                   answer a question, etc.) are mainly used for
    time through this continuous and multi-
                                                                   learning status diagnosis and learning route
    dimensional data accumulation, which helps
                                                                   planning. Other interactive data (e.g.,
    to predict their reaction and carry out
                                                                   expression, ECG, etc.) plays a supplementary
    personalized learning path planning. Data
                                                                   role increasing learning quality. Companies
    collection has encountered stricter policies
                                                                   can fully leverage the value via data mining
    and regulations nowadays, and the only way
                                                                   to optimize their product or even monetize
    for companies to get access to valuable data
                                                                   the data in cross-industry to bring more
    is through their own products.
                                                                   value-add to itself.

                                                                      ►   Name
                                                                      ►   Age
                                                       Personal       ►   …
                            ►   Expres
                            ►   ECG
                            ►   …                                                          ►   Time to
                                Interactiv                                  Behavioral     ►   …
                                  e data                                      data

                                                                      ►   Knowledge
                                                                      ►   Learning
                                                                      ►   …

                                 China’s AI Adaptive Learning Industry Whitepaper I   27
                                                                            Overview of China’s
                                                                            OMO AI Adaptive

                        1 Constraints on space and                                                           1 Poor learning experience
Limitation of Offline

                                                                                                                                           Limitation of Online
                                                                              Lack of high-
                        2 Capital-intensive operation


                                                                              quality teacher is
                        with high marginal cost                               the bottleneck of
                                                                                                             2 High customer acquisition
                        3 Small institutions lack                                                            and marketing cost
                        systematic development of
                        teaching material                                     Insufficient
                                                                              customization of               3 Customer’s willingness to
                        4 Small institutions lack brand                       class and high                 renew is low
                        construction                                          cost of 1-on-1

    Sources: Deck research, Interviews, EY-Parthenon analysis

                                                        China’s AI Adaptive Learning Industry Whitepaper I   28
2.1 Background of OMO AI Adaptive
       2.1.1 Limitation of Online/Offline Education                                   traditional offline education. However, offline
                                                                                      education has maintained its prevalence
       Emerging from the wave of “Internet+”,
                                                                                      throughout the decades since online education,
       innovative online education modes, such as
                                                                                      although made up some weaknesses of offline,
       MOOC and live streaming, have brought
                                                                                      also has its own limit. In essence, both online
       disruptive changes to the traditional education
                                                                                      and offline education have their own limitation
       industry through a break to the limit of time
                                                                                      in terms of business model and user experience.
       and space. It was once assumed that the
       capital-light online business model would
       certainly overtake, or even replace most of the

                        1 Constraints on space and                                                           1 Poor learning experience

                                                                                                                                           Limitation of Online
Limitation of Offline

                                                                              Lack of high-
                        2 Capital-intensive operation


                                                                              quality teacher is
                        with high marginal cost                               the bottleneck of
                                                                                                             2 High customer acquisition
                        3 Small institutions lack                                                            and marketing cost
                        systematic development of
                        teaching material                                     Insufficient
                                                                              customization of               3 Customer’s willingness to
                        4 Small institutions lack brand                       class and high                 renew is low
                        construction                                          cost of 1-on-1

 Sources: Deck research, Interviews, EY-Parthenon analysis

                                                        China’s AI Adaptive Learning Industry Whitepaper I   29
►   Limitation of offline education:                                 ►    Small-sized institutions lack brand
                                                                          building: Although the number of small
     ►   Constraint on time and space:
                                                                          and medium-sized institutions in China's
         Convenience is one of the main factors
                                                                          K-12 education market is large, they are
         considered by parents and students since
                                                                          too scattered due to the lack of branding,
         offline education requires students’
                                                                          with less attraction to consumers and
         physical presence. The demand for short
                                                                          therefore lower revenue. Non-brand small
         distance and suitable class time leads to a
                                                                          players are at a significant disadvantage
         smaller target customer group since only
                                                                          in acquiring customers due to
         a nearby population can be reached,
                                                                          parents/students’ preference to brand
         creating a ceiling for customer acquisition.
                                                                          popularity and reputation. However,
     ►   Capital-intensive operation with a high                          brand building takes a long time, and it’s
         marginal cost: Offline education                                 challenging to quickly establish trust in a
         institutions usually adopt a capital-                            large consumer base. Small education
         intensive operating model, with a large                          institutions are in urgent need of
         investment in teachers, venues,                                  enhancing their brand influence within a
         equipment, etc., making it more                                  short time.
         challenging for scale expansion with slim
         profit margin and high marginal cost.
                                                               Cost Structure of K-12 Offline Education Institutions
     ►   Small-sized institutions lack systematic
         development of teaching material:
         According to EY-Parthenon analysis,
         more than 90% of offline education                                   ~20%
         institutions are small and medium-sized
         players. These institutions usually lack                                          ~35%
         systematic content development
         methodology, with unstable education                                                           ~15%
         quality depends entirely on teachers’ own
         experience, making it difficult to
         guarantee students’ learning effect.                                                                              ~15%

                                                                Revenue       Profit     Teacher        Rent Marketing Other cost
                                                               Sources: Deck research, Interviews, EY-Parthenon analysis

“   A good teaching content development system should be highly standardized and localized.
    Only giant educational institutions can realize this. Small and micro training institutions do
    not have such capability. As a result, in small institutions, teaching quality highly relies on
    teachers themselves rather than institutions’ R&D capability. Once the good teacher leaves,
    students are easily lost as well.

                                                                      ——Expert from Traditional Offline Institution

                                   China’s AI Adaptive Learning Industry Whitepaper I     30
►   Limitation of Online Education:                                                 However, due to fierce competition and
                                                                                      dissatisfying user experiences, what huge
      ►   Poor learning experience: Student-
                                                                                      marketing investment generates is not a
          teacher interaction is limited as students
                                                                                      proportionate growth in customer base
          take classes online, which may negatively
                                                                                      but a surge in customer acquisition cost.
          affect their attention, learning efficiency
                                                                                      Research has shown that the current
          and class atmosphere. In particular,
                                                                                      average customer acquisition cost of
          children in K-12 group have relatively
                                                                                      online education is around 500-1,200
          weak self-control and need to form good
                                                                                      USD per capita, far exceeding the around
          learning habits under the teacher’s
                                                                                      80-160 USD per capita for offline. The
          supervision, which is challenging for
                                                                                      extreme high cost has a significant
          online education. A survey during the
                                                                                      negative impact on online institutions’
          covid-19 pandemic shows that the main
                                                                                      profit even when they have achieved
          drawbacks of online education are related
                                                                                      positive business growth. Most online
          to learning experience, among which
                                                                                      education companies’ financial reports
          66.8% of users believed that the
                                                                                      show that they are actually in a state of
          interaction is limited online; 50.2%
          mentioned that teachers and students are
          not quite adapted to the new mode,                                      ►   Customer’s willingness to renew is low:
          45.5% considered online education with                                      User loyalty is usually low in online
          insufficient supervision, and 37.8% noted                                   education due to the dissatisfied learning
          that their online experience is not that                                    experience and refund issues related to
          user-friendly.                                                              the crash of some startups. According to
                                                                                      an industry insider, the online conversion
      ►   High customer acquisition cost: The
                                                                                      rate of long-term and regular-priced
          development of offline education has
                                                                                      customer is only 10% - 30%, with a
          slowed down due to the pandemic.
                                                                                      renewal rate of 50% - 80%. How to
          Investors’ attention, therefore, shifts to
                                                                                      improve user loyalty is another challenge
          online, with several players gaining
                                                                                      for online players to sustain their
          colossal finance. Capital-abundant players
          advertised heavily and launched price-off
          promotions to achieve rapid growth.
                                                                            Customer Acquisition Cost (USD per capita)

      Survey on the Shortcomings of Online Education

                  Insufficient                          Teachers and
    66.8%         interaction
                                          50.2%         students are
                                                        not adapted

                  Inadequate                            Experience is
    45.5%         supervision             37.8%         not user-
                                                                                       80-160                              500-1,200

Sources: Beijing Normal University’s New Media Research center,
Guangming Daily education research center

                                                                                Offline education                      Online education

                                                                            Note: customer acquisition cost = (total marketing expense + sales
                                                                            expense)/ number of new customers

                                                China’s AI Adaptive Learning Industry Whitepaper I   31
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