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Global Machine Learning in Education Market Research Report 2024
Synopsis
Machine learning has the potential to support aspects of teaching and learning that are currently time consuming and difficult to manage, such as individual project work, collaboration, tutorials and self-directed learning.
The global Machine Learning in Education market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
North American market for Machine Learning in Education is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Machine Learning in Education is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for Machine Learning in Education in Intelligent Tutoring Systems is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of Machine Learning in Education include IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning and Fishtree, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Education, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Education.
Report Scope
The Machine Learning in Education market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning in Education market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning in Education companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
IBM
Microsoft
Google
Amazon
Cognizan
Pearson
Bridge-U
DreamBox Learning
Fishtree
Jellynote
Quantum Adaptive Learning
Segment by Type
Cloud-Based
On-Premise
Segment by Application
Intelligent Tutoring Systems
Virtual Facilitators
Content Delivery Systems
Interactive Websites
Others
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning in Education companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Machine learning has the potential to support aspects of teaching and learning that are currently time consuming and difficult to manage, such as individual project work, collaboration, tutorials and self-directed learning.
The global Machine Learning in Education market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
North American market for Machine Learning in Education is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Machine Learning in Education is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for Machine Learning in Education in Intelligent Tutoring Systems is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of Machine Learning in Education include IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning and Fishtree, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Education, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Education.
Report Scope
The Machine Learning in Education market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning in Education market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning in Education companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
IBM
Microsoft
Amazon
Cognizan
Pearson
Bridge-U
DreamBox Learning
Fishtree
Jellynote
Quantum Adaptive Learning
Segment by Type
Cloud-Based
On-Premise
Segment by Application
Intelligent Tutoring Systems
Virtual Facilitators
Content Delivery Systems
Interactive Websites
Others
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning in Education companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Index1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Machine Learning in Education Market Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Cloud-Based
1.2.3 On-Premise
1.3 Market by Application
1.3.1 Global Machine Learning in Education Market Growth by Application: 2019 VS 2023 VS 2030
1.3.2 Intelligent Tutoring Systems
1.3.3 Virtual Facilitators
1.3.4 Content Delivery Systems
1.3.5 Interactive Websites
1.3.6 Others
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning in Education Market Perspective (2019-2030)
2.2 Machine Learning in Education Growth Trends by Region
2.2.1 Global Machine Learning in Education Market Size by Region: 2019 VS 2023 VS 2030
2.2.2 Machine Learning in Education Historic Market Size by Region (2019-2024)
2.2.3 Machine Learning in Education Forecasted Market Size by Region (2025-2030)
2.3 Machine Learning in Education Market Dynamics
2.3.1 Machine Learning in Education Industry Trends
2.3.2 Machine Learning in Education Market Drivers
2.3.3 Machine Learning in Education Market Challenges
2.3.4 Machine Learning in Education Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning in Education Players by Revenue
3.1.1 Global Top Machine Learning in Education Players by Revenue (2019-2024)
3.1.2 Global Machine Learning in Education Revenue Market Share by Players (2019-2024)
3.2 Global Machine Learning in Education Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Machine Learning in Education Revenue
3.4 Global Machine Learning in Education Market Concentration Ratio
3.4.1 Global Machine Learning in Education Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning in Education Revenue in 2023
3.5 Machine Learning in Education Key Players Head office and Area Served
3.6 Key Players Machine Learning in Education Product Solution and Service
3.7 Date of Enter into Machine Learning in Education Market
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning in Education Breakdown Data by Type
4.1 Global Machine Learning in Education Historic Market Size by Type (2019-2024)
4.2 Global Machine Learning in Education Forecasted Market Size by Type (2025-2030)
5 Machine Learning in Education Breakdown Data by Application
5.1 Global Machine Learning in Education Historic Market Size by Application (2019-2024)
5.2 Global Machine Learning in Education Forecasted Market Size by Application (2025-2030)
6 North America
6.1 North America Machine Learning in Education Market Size (2019-2030)
6.2 North America Machine Learning in Education Market Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Machine Learning in Education Market Size by Country (2019-2024)
6.4 North America Machine Learning in Education Market Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning in Education Market Size (2019-2030)
7.2 Europe Machine Learning in Education Market Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Machine Learning in Education Market Size by Country (2019-2024)
7.4 Europe Machine Learning in Education Market Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning in Education Market Size (2019-2030)
8.2 Asia-Pacific Machine Learning in Education Market Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Machine Learning in Education Market Size by Region (2019-2024)
8.4 Asia-Pacific Machine Learning in Education Market Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning in Education Market Size (2019-2030)
9.2 Latin America Machine Learning in Education Market Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Machine Learning in Education Market Size by Country (2019-2024)
9.4 Latin America Machine Learning in Education Market Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning in Education Market Size (2019-2030)
10.2 Middle East & Africa Machine Learning in Education Market Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Machine Learning in Education Market Size by Country (2019-2024)
10.4 Middle East & Africa Machine Learning in Education Market Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 IBM
11.1.1 IBM Company Detail
11.1.2 IBM Business Overview
11.1.3 IBM Machine Learning in Education Introduction
11.1.4 IBM Revenue in Machine Learning in Education Business (2019-2024)
11.1.5 IBM Recent Development
11.2 Microsoft
11.2.1 Microsoft Company Detail
11.2.2 Microsoft Business Overview
11.2.3 Microsoft Machine Learning in Education Introduction
11.2.4 Microsoft Revenue in Machine Learning in Education Business (2019-2024)
11.2.5 Microsoft Recent Development
11.3 Google
11.3.1 Google Company Detail
11.3.2 Google Business Overview
11.3.3 Google Machine Learning in Education Introduction
11.3.4 Google Revenue in Machine Learning in Education Business (2019-2024)
11.3.5 Google Recent Development
11.4 Amazon
11.4.1 Amazon Company Detail
11.4.2 Amazon Business Overview
11.4.3 Amazon Machine Learning in Education Introduction
11.4.4 Amazon Revenue in Machine Learning in Education Business (2019-2024)
11.4.5 Amazon Recent Development
11.5 Cognizan
11.5.1 Cognizan Company Detail
11.5.2 Cognizan Business Overview
11.5.3 Cognizan Machine Learning in Education Introduction
11.5.4 Cognizan Revenue in Machine Learning in Education Business (2019-2024)
11.5.5 Cognizan Recent Development
11.6 Pearson
11.6.1 Pearson Company Detail
11.6.2 Pearson Business Overview
11.6.3 Pearson Machine Learning in Education Introduction
11.6.4 Pearson Revenue in Machine Learning in Education Business (2019-2024)
11.6.5 Pearson Recent Development
11.7 Bridge-U
11.7.1 Bridge-U Company Detail
11.7.2 Bridge-U Business Overview
11.7.3 Bridge-U Machine Learning in Education Introduction
11.7.4 Bridge-U Revenue in Machine Learning in Education Business (2019-2024)
11.7.5 Bridge-U Recent Development
11.8 DreamBox Learning
11.8.1 DreamBox Learning Company Detail
11.8.2 DreamBox Learning Business Overview
11.8.3 DreamBox Learning Machine Learning in Education Introduction
11.8.4 DreamBox Learning Revenue in Machine Learning in Education Business (2019-2024)
11.8.5 DreamBox Learning Recent Development
11.9 Fishtree
11.9.1 Fishtree Company Detail
11.9.2 Fishtree Business Overview
11.9.3 Fishtree Machine Learning in Education Introduction
11.9.4 Fishtree Revenue in Machine Learning in Education Business (2019-2024)
11.9.5 Fishtree Recent Development
11.10 Jellynote
11.10.1 Jellynote Company Detail
11.10.2 Jellynote Business Overview
11.10.3 Jellynote Machine Learning in Education Introduction
11.10.4 Jellynote Revenue in Machine Learning in Education Business (2019-2024)
11.10.5 Jellynote Recent Development
11.11 Quantum Adaptive Learning
11.11.1 Quantum Adaptive Learning Company Detail
11.11.2 Quantum Adaptive Learning Business Overview
11.11.3 Quantum Adaptive Learning Machine Learning in Education Introduction
11.11.4 Quantum Adaptive Learning Revenue in Machine Learning in Education Business (2019-2024)
11.11.5 Quantum Adaptive Learning Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details
Published By : QY Research
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