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市场调查报告书
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1776699

2032 年教育市场人工智慧预测：按组件、部署、交付模式、技术、应用、最终用户和地区进行的全球分析

AI in Education Market Forecasts to 2032 - Global Analysis By Component (Software Solutions and Services), Deployment (Cloud-Based, On-Premises and Hybrid), Delivery Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球教育人工智慧市场预计在 2025 年达到 73.7 亿美元，到 2032 年将达到 442.7 亿美元，预测期内的复合年增长率为 29.2%。

人工智慧 (AI) 正在透过改进教学方法、个人化学习体验和提升管理效率来改变教育。 AI 平台可以根据学生独特的学习风格和学习进度来调整课程，从而加深学生对概念的理解。智慧辅导系统、即时分析和自动评分使教师能够精准定位学生的优势和发展方向。 AI 也透过聊天机器人和虚拟导师等工具实现身临其境型学习，从而提高学生的参与度。此外，随着 AI 的发展，它有潜力透过变得更加包容、便利和高效，改善各种学习环境中的教育。

根据印度全民教育计画的数据，印度目前缺少150万所学校、2.5亿名学生和约100万名教师。人工智慧正透过个人化学习、行政自动化和数据主导的决策提供解决方案。人工智慧正被用于支援DIKSHA和SWAYAM等平台，优化UDISE+和SDMS等资料系统，并改善印度各地的教育可近性和规划能力。

增加教育数位基础设施

全球网路普及率的提高、智慧型装置的普及以及学习管理系统 (LMS) 的日益普及，使得人工智慧与教育的融合成为可能。随着众多教育机构向混合式或全线上学习模式转型，数位工具已成为教育交付的核心。智慧评分系统、预测分析和自动化内容推荐只是人工智慧技术的几个例子，得益于这些基础设施的发展，这些技术可以轻鬆应用。此外，政府和私人机构正在大力投资数位教育基础设施，尤其是在贫困地区，使所有社会经济背景的人们都能更容易使用人工智慧工具。

实施成本高

高昂的实施成本是人工智慧在教育市场应用的最大障碍之一，尤其对于资源匮乏的教育机构和中低收入国家。部署人工智慧系统需要对基础设施进行大量投资，包括高速互联网、尖端电脑硬体、云端服务和授权软体平台。此外，将人工智慧工具融入现有的学习管理系统需要更新数位内容格式、聘用专业技术人员并培训教育工作者。这些经济障碍限制了公平的取得途径，使小型学校和农村教育机构难以大规模实施人工智慧解决方案，从而在教育创新中造成了数位落差。

使用人工智慧建立学习和课程模型

高昂的实施成本仍然是人工智慧在教育市场应用的最大障碍之一，尤其是在人工智慧技术日益普及的今天，教育模式亟需从小培养学生对人工智慧的素养、道德和技能。教育机构开始将人工智慧相关内容纳入STEM课程，为学生在资料科学、机器人技术和机器学习等领域的职业发展做好准备。课程设计者、人工智慧教育平台和培训提供者越来越多地发现机会，可以打造尖端的培训材料、认证和技能发展计划。此外，各国政府和国际组织正在进一步推动以人工智慧为中心的教育作为战略重点，这进一步推动了以智慧数位工具为支撑的课程现代化的需求。

缺乏标准化和规定不明确

人工智慧在教育领域的应用是一个快速发展的领域，但目前尚无广泛接受的最佳实践、标准或法律体制。由于教育部门缺乏关于演算法透明度、资料伦理和人工智慧审核的统一准则，教育机构往往难以满足伦理和法律要求。这种模糊性会阻碍投资，延迟采用，并为滥用和违法行为创造机会。此外，缺乏标准使得难以比较人工智慧工具的有效性和卓越性，这可能导致学习结果不一致。这种模糊性可能导致诉讼风险，并且在监管严格的司法管辖区，也可能导致政府暂停对人工智慧计划的资助。

COVID-19的影响：

新冠疫情迫使教育产业迅速转向数位化和远距学习，显着加速了人工智慧在教育市场的普及。为了保持教学和评估的连续性，世界各地的教育机构在实体教室关闭期间正在转向人工智慧主导的平台。个人化学习、自动化管理业务以及在虚拟环境中追踪学生表现的即时分析，都得益于人工智慧工具。此外，这场危机也凸显了对包容性、可扩展性和技术支援型教育的需求，促使教育科技公司和政府加大对人工智慧的投资。

机器学习将成为预测期内最大的市场

预计机器学习领域将在预测期内占据最大的市场占有率。为了提供个人化的学习体验，机器学习演算法使平台能够分析大量的学生数据，包括考试成绩、互动模式和学习行为。这些系统能够预测学生的表现、纠正内容并通知教师进行早期疗育。 ML 还支援资源推荐、自动评分和效能分析等后端功能，这使其对于学术和管理使用案例都至关重要。此外，机器学习是教育领域使用最广泛、最重要的人工智慧技术，因为它具有扩充性、透过数据不断改进的能力以及与各种教育工具的兼容性。

预计自适应评估和评分部分在预测期内将以最高的复合年增长率成长。

自我调整评估和评分领域预计将在预测期内实现最高成长率。透过提供与评分标准一致的论文、计划和测验的Brick回馈，人工智慧主导的自适应评估系统正在彻底改变教师评估学生表现的方式，显着缩短评分时间。这些系统使用机器学习和自然语言处理来识别误解模式并提供个人化的改进计划。随着教育转向持续的、基于能力的模式，对智慧、反馈丰富且扩充性的评估工具的需求日益增长，而这个市场是人工智慧驱动的学习生态系统创新和投资的主要驱动力。

占比最大的地区：

预计北美将在预测期内占据最大的市场占有率，这得益于其对教育创新的大量投资、顶尖教育科技公司的强劲存在以及先进的数位基础设施。在积极的政府政策和资金推动下，该地区受益于人工智慧技术在K-12和高等教育以及企业学习环境中的早期应用。美国一些机构和新兴企业率先推出了智慧辅导系统、自动评分工具和个人化学习平台等人工智慧应用，推动了市场扩张。此外，北美对数据主导教育、人工智慧课程整合和平等数位存取的重视，巩固了其在全球教育人工智慧市场的主导地位。

复合年增长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率，这得益于互联网使用率的上升、数位转型步伐的加快以及政府不断加大对教育体系更新的力度。中国、印度、日本和韩国等国家正积极投资以人工智慧为基础的教育技术，以解决教师短缺问题、提升学习成果，并扩大服务欠缺地区和农村地区获得优质教育的机会。蓬勃发展的教育科技Start-Ups生态系统、日益增长的科技型学生数量，以及诸如中国《人工智慧发展蓝图》和印度《2020年国家教育政策》（NEP）等鼓舞人心的国家政策，共同推动了这一领域的快速发展。

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  • 公司简介
    • 对最多三家其他市场公司进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 地理细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 教育领域人工智慧市场（按组成部分）

  • 软体解决方案
  • 服务

第六章 教育领域人工智慧市场（按部署）

  • 云端基础
  • 本地
  • 杂交种

7. 教育领域人工智慧市场（以交付模式）

  • 行动应用程式
  • 网路为基础的平台

第八章 教育领域的人工智慧市场（按技术）

  • 深度学习
  • 机器学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 语音辨识
  • 边缘人工智慧和设备内推理

第九章：人工智慧教育市场（按应用）

  • 智慧辅导系统
  • 虚拟促进者与学习环境
  • 学习分析和推荐引擎
  • 自动化管理和监督
  • 内容传送系统
  • 自适应评估和评分
  • 其他用途

第 10 章：教育领域的人工智慧市场（按最终用户划分）

  • K-12教育
  • 高等教育
  • 企业培训与学习
  • 教育出版商
  • 政府、非政府组织与非正式学习平台
  • 其他最终用户

第 11 章：按地区分類的教育人工智慧市场

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十三章 公司概况

  • Amazon Web Services, Inc.
  • Salesforce Inc
  • Carnegie Learning, Inc.
  • Google LLC
  • Microsoft Corporation
  • Intel Corporation
  • Siemens AG
  • NVIDIA Corporation
  • Cisco Systems
  • Oracle Corporation
  • DreamBox Learning, Inc.
  • Cognizant
  • IBM Corporation
  • Fishtree Inc.
  • Blackboard Inc.
Product Code: SMRC30030

According to Stratistics MRC, the Global AI in Education Market is accounted for $7.37 billion in 2025 and is expected to reach $44.27 billion by 2032 growing at a CAGR of 29.2% during the forecast period. Artificial Intelligence (AI) is transforming the education sector by enhancing teaching methods, personalizing learning experiences, and improving administrative efficiency. AI-powered platforms enable students to better understand concepts by tailoring lessons to their unique learning style and pace. Intelligent tutoring systems, real-time analytics, and automated grading help teachers pinpoint their students' areas of strength and growth. AI also makes immersive learning possible with tools like chat bots and virtual tutors, which increases student engagement. Moreover, AI has the potential to improve education in a variety of learning contexts by becoming more inclusive, accessible, and efficient as it develops.

According to Education for All in India, With 1.5 million schools, 250 million students, and a shortage of approximately 1 million teachers, AI offers solutions through personalized learning, administrative automation, and data-driven policymaking. AI is being used to enhance platforms like DIKSHA and SWAYAM, and optimize data systems such as UDISE+ and SDMS, improving accessibility and planning across India.

Market Dynamics:

Driver:

Increasing educational digital infrastructure

AI integration in education is now possible owing to the worldwide increase in internet penetration, the spread of smart devices, and the growing use of learning management systems (LMS). Digital tools have taken center stage in the delivery of education as many institutions transition to hybrid or fully online learning models. Intelligent grading systems, predictive analytics, and automated content recommendation are just a few examples of the AI technologies that can be easily adopted owing to this infrastructure development. Additionally, governments and private organizations are making significant investments in digital education infrastructure, especially in underprivileged areas, increasing the accessibility of AI tools for people from all socioeconomic backgrounds.

Restraint:

High costs of implementation

The high cost of implementation is one of the biggest barriers to the AI in the education market, especially for underfunded institutions and low- and middle-income nations. A significant investment in infrastructure, such as fast internet, cutting-edge computer hardware, cloud services, and licensed software platforms, is necessary for the deployment of AI systems. Furthermore, incorporating AI tools into current learning management systems frequently calls for updating digital content formats, hiring specialized technical staff, and training educators-all of which drive up costs. These financial obstacles limit equitable access and create a digital divide in educational innovation by making it challenging for smaller schools and rural institutions to implement AI solutions at scale.

Opportunity:

Creation of AI-powered learning and curriculum models

The high cost of implementation is one of the biggest barriers to AI in the education market, especially with the need for educational models that teach AI literacy, ethics, and skills from an early age, which is growing as AI becomes more pervasive in daily life. In order to prepare students for careers in data science, robotics, and machine learning, educational institutions are starting to incorporate AI-related content into STEM curricula. Curriculum designers, AI education platforms, and training providers now have more chances to produce cutting-edge training materials, credentials, and skill-development initiatives. Moreover, the need for curriculum modernization backed by intelligent digital tools is being further increased by governments and international organizations that are promoting AI-centric education as a strategic priority.

Threat:

Insufficient standardization and uncertainty in regulations

The use of AI in education is a quickly developing field without widely accepted best practices, standards, or legal frameworks. Because the education sector lacks unified guidelines on algorithmic transparency, data ethics, and AI auditing, institutions frequently struggle to meet ethical and legal requirements. This ambiguity may discourage investment, postpone adoption, and create opportunities for abuse or legal infractions. Furthermore, the lack of standards makes it challenging to compare the effectiveness and caliber of AI tools, which may result in uneven learning outcomes. This ambiguity can lead to litigation risks and halt government funding for AI projects in areas with strict regulations.

Covid-19 Impact:

The COVID-19 pandemic forced a quick transition to digital and remote learning, which greatly accelerated the adoption of AI in the education market. In order to maintain continuity in instruction and evaluation, educational institutions around the world have resorted to AI-driven platforms as physical classrooms have been shut down. Personalized learning, administrative task automation, and real-time analytics to track student performance in virtual environments were all made possible by AI tools. Moreover, the crisis also brought attention to the need for inclusive, scalable, and tech-enabled education, which led to investments in AI by edtech companies and governments.

The machine learning segment is expected to be the largest during the forecast period

The machine learning segment is expected to account for the largest market share during the forecast period. In order to provide individualized learning experiences, platforms can analyze enormous volumes of student data, including test scores, interaction patterns, and learning behaviors, owing to machine learning algorithms. These systems have the ability to predict student performance, modify content, and notify teachers of early intervention. ML is essential for both academic and administrative use cases since it also supports backend features like resource recommendation, automated grading, and performance analytics. Additionally, machine learning is the most widely used and significant AI technology in the education sector because of its scalability, ability to continuously improve through data, and compatibility with a variety of educational tools.

The adaptive assessment and grading segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the adaptive assessment and grading segment is predicted to witness the highest growth rate. By providing real-time, rubric-aligned feedback on essays, projects, and quizzes, AI-driven adaptive assessment systems are revolutionizing how teachers evaluate student performance and significantly cutting down on grading time. In addition to efficiently scoring student responses-often in a matter of seconds-these systems use machine learning and natural language processing to identify patterns of misunderstanding and offer individualized remediation plans. The need for intelligent, feedback-rich, and scalable assessment tools is growing as education moves toward continuous, competency-based models, making this market a major force behind innovation and investment in AI-powered learning ecosystems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by its substantial investments in educational innovation, robust presence of top edtech companies, and sophisticated digital infrastructure. With the help of proactive government policies and funding initiatives, the region gains from the early adoption of AI technologies in K-12, higher education, and corporate learning environments. Market expansion has been accelerated by U.S.-based organizations and startups that have led the way in AI applications such as intelligent tutoring systems, automated grading tools, and personalized learning platforms. Furthermore, North America's emphasis on data-driven education, AI curriculum integration, and equitable digital access has strengthened its leading position in the global AI in education market.

Region with highest CAGR:

Over the forecast period, the Asia-Pacific region is anticipated to exhibit the highest CAGR, driven by the increased use of the internet, the quickening pace of digital transformation, and growing government efforts to update educational systems. AI-based educational technologies are being actively invested in by nations like China, India, Japan, and South Korea in an effort to solve the teacher shortage, enhance learning outcomes, and expand access to high-quality education in underserved and rural areas. Adoption is accelerating due to a flourishing edtech startup ecosystem, a growing number of tech-savvy students, and encouraging national policies like China's AI development roadmap and India's National Education Policy (NEP) 2020.

Key players in the market

Some of the key players in AI in Education Market include Amazon Web Services, Inc., Salesforce Inc, Carnegie Learning, Inc., Google LLC, Microsoft Corporation, Intel Corporation, Siemens AG, NVIDIA Corporation, Cisco Systems, Oracle Corporation, DreamBox Learning, Inc., Cognizant, IBM Corporation, Fishtree Inc and Blackboard Inc

Key Developments:

In May 2025, Amazon Web Services, Inc. and SAP announced the launch of a new AI Co-Innovation Program to help partners build generative artificial intelligence applications and agents that help customers rapidly solve real-time business challenges. The AI Co-Innovation Program represents the two companies shared vision to help partners define, build, and deploy generative AI applications tailored to their ERP workloads.

In March 2025, Google LLC announced it has signed a definitive agreement to acquire Wiz, Inc., a leading cloud security platform headquartered in New York, for $32 billion, subject to closing adjustments, in an all-cash transaction. Once closed, Wiz will join Google Cloud. This acquisition represents an investment by Google Cloud to accelerate two large and growing trends in the AI era: improved cloud security and the ability to use multiple clouds.

In February 2025, Salesforce and Google Cloud have expanded a partnership that will bring Google's Gemini models to Agentforce, integrate Salesforce Service Cloud tightly with Google Customer Engagement Suite and enable handoffs between the companies' AI agents. The deal also gives Salesforce, which historically has run on AWS, another option for its workloads. Salesforce Agentforce, Data Cloud and Customer 360 applications will run on Google Cloud and be available through Google Cloud Marketplace.

Components Covered:

  • Software Solutions
  • Services

Deployments Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

Delivery Modes Covered:

  • Mobile Applications
  • Web-Based Platforms

Technologies Covered:

  • Deep Learning
  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Speech Recognition
  • Edge AI and On-device Inference

Applications Covered:

  • Intelligent Tutoring Systems
  • Virtual Facilitators and Learning Environments
  • Learning Analytics and Recommendation Engines
  • Automated Administration and Proctoring
  • Content Delivery Systems
  • Adaptive Assessment and Grading
  • Other Applications

End Users Covered:

  • K-12 Education
  • Higher Education
  • Corporate Training & Learning
  • Educational Publishers
  • Government, NGOs & Informal Learning Platforms
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Education Market, By Component

  • 5.1 Introduction
  • 5.2 Software Solutions
  • 5.3 Services

6 Global AI in Education Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 On-Premises
  • 6.4 Hybrid

7 Global AI in Education Market, By Delivery Mode

  • 7.1 Introduction
  • 7.2 Mobile Applications
  • 7.3 Web-Based Platforms

8 Global AI in Education Market, By Technology

  • 8.1 Introduction
  • 8.2 Deep Learning
  • 8.3 Machine Learning
  • 8.4 Natural Language Processing (NLP)
  • 8.5 Computer Vision
  • 8.6 Speech Recognition
  • 8.7 Edge AI and On-device Inference

9 Global AI in Education Market, By Application

  • 9.1 Introduction
  • 9.2 Intelligent Tutoring Systems
  • 9.3 Virtual Facilitators and Learning Environments
  • 9.4 Learning Analytics and Recommendation Engines
  • 9.5 Automated Administration and Proctoring
  • 9.6 Content Delivery Systems
  • 9.7 Adaptive Assessment and Grading
  • 9.8 Other Applications

10 Global AI in Education Market, By End User

  • 10.1 Introduction
  • 10.2 K-12 Education
  • 10.3 Higher Education
  • 10.4 Corporate Training & Learning
  • 10.5 Educational Publishers
  • 10.6 Government, NGOs & Informal Learning Platforms
  • 10.7 Other End Users

11 Global AI in Education Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Amazon Web Services, Inc.
  • 13.2 Salesforce Inc
  • 13.3 Carnegie Learning, Inc.
  • 13.4 Google LLC
  • 13.5 Microsoft Corporation
  • 13.6 Intel Corporation
  • 13.7 Siemens AG
  • 13.8 NVIDIA Corporation
  • 13.9 Cisco Systems
  • 13.10 Oracle Corporation
  • 13.11 DreamBox Learning, Inc.
  • 13.12 Cognizant
  • 13.13 IBM Corporation
  • 13.14 Fishtree Inc.
  • 13.15 Blackboard Inc.

List of Tables

  • Table 1 Global AI in Education Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Education Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI in Education Market Outlook, By Software Solutions (2024-2032) ($MN)
  • Table 4 Global AI in Education Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI in Education Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 6 Global AI in Education Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 7 Global AI in Education Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 8 Global AI in Education Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 9 Global AI in Education Market Outlook, By Delivery Mode (2024-2032) ($MN)
  • Table 10 Global AI in Education Market Outlook, By Mobile Applications (2024-2032) ($MN)
  • Table 11 Global AI in Education Market Outlook, By Web-Based Platforms (2024-2032) ($MN)
  • Table 12 Global AI in Education Market Outlook, By Technology (2024-2032) ($MN)
  • Table 13 Global AI in Education Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 14 Global AI in Education Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 15 Global AI in Education Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 16 Global AI in Education Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 17 Global AI in Education Market Outlook, By Speech Recognition (2024-2032) ($MN)
  • Table 18 Global AI in Education Market Outlook, By Edge AI and On-device Inference (2024-2032) ($MN)
  • Table 19 Global AI in Education Market Outlook, By Application (2024-2032) ($MN)
  • Table 20 Global AI in Education Market Outlook, By Intelligent Tutoring Systems (2024-2032) ($MN)
  • Table 21 Global AI in Education Market Outlook, By Virtual Facilitators and Learning Environments (2024-2032) ($MN)
  • Table 22 Global AI in Education Market Outlook, By Learning Analytics and Recommendation Engines (2024-2032) ($MN)
  • Table 23 Global AI in Education Market Outlook, By Automated Administration and Proctoring (2024-2032) ($MN)
  • Table 24 Global AI in Education Market Outlook, By Content Delivery Systems (2024-2032) ($MN)
  • Table 25 Global AI in Education Market Outlook, By Adaptive Assessment and Grading (2024-2032) ($MN)
  • Table 26 Global AI in Education Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 27 Global AI in Education Market Outlook, By End User (2024-2032) ($MN)
  • Table 28 Global AI in Education Market Outlook, By K-12 Education (2024-2032) ($MN)
  • Table 29 Global AI in Education Market Outlook, By Higher Education (2024-2032) ($MN)
  • Table 30 Global AI in Education Market Outlook, By Corporate Training & Learning (2024-2032) ($MN)
  • Table 31 Global AI in Education Market Outlook, By Educational Publishers (2024-2032) ($MN)
  • Table 32 Global AI in Education Market Outlook, By Government, NGOs & Informal Learning Platforms (2024-2032) ($MN)
  • Table 33 Global AI in Education Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.