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1776699

2032 年教育市場人工智慧預測：按組件、部署、交付模式、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球教育人工智慧市場預計在 2025 年達到 73.7 億美元，到 2032 年將達到 442.7 億美元，預測期內的複合年成長率為 29.2%。

人工智慧 (AI) 正在透過改進教學方法、個人化學習體驗和提升管理效率來改變教育。 AI 平台可以根據學生獨特的學習風格和學習進度來調整課程，從而加深學生對概念的理解。智慧輔導系統、即時分析和自動評分使教師能夠精準定位學生的優勢和發展方向。 AI 也透過聊天機器人和虛擬導師等工具實現身臨其境型學習，從而提高學生的參與度。此外，隨著 AI 的發展，它有潛力透過變得更加包容、便利和高效，改善各種學習環境中的教育。

根據印度全民教育計畫的數據，印度目前缺少150萬所學校、2.5億名學生和約100萬名教師。人工智慧正透過個人化學習、行政自動化和數據主導的決策提供解決方案。人工智慧正被用於支援DIKSHA和SWAYAM等平台，最佳化UDISE+和SDMS等資料系統，並改善印度各地的教育可近性和規劃能力。

增加教育數位基礎設施

全球網路普及率的提高、智慧型裝置的普及以及學習管理系統 (LMS) 的日益普及，使得人工智慧與教育的融合成為可能。隨著眾多教育機構向混合式或全線上學習模式轉型，數位工具已成為教育交付的核心。智慧評分系統、預測分析和自動化內容推薦只是人工智慧技術的幾個例子，得益於這些基礎設施的發展，這些技術可以輕鬆應用。此外，政府和私人機構正在大力投資數位教育基礎設施，尤其是在貧困地區，使所有社會經濟背景的人們都能更容易使用人工智慧工具。

實施成本高

高昂的實施成本是人工智慧在教育市場應用的最大障礙之一，尤其對於資源匱乏的教育機構和中低收入國家。部署人工智慧系統需要對基礎設施進行大量投資，包括高速網際網路、尖端電腦硬體、雲端服務和授權軟體平台。此外，將人工智慧工具融入現有的學習管理系統需要更新數位內容格式、聘用專業技術人員並培訓教育工作者。這些經濟障礙限制了公平的取得途徑，使小型學校和農村教育機構難以大規模實施人工智慧解決方案，從而在教育創新中造成了數位落差。

使用人工智慧建立學習和課程模型

高昂的實施成本仍然是人工智慧在教育市場應用的最大障礙之一，尤其是在人工智慧技術日益普及的今天，教育模式亟需從小培養學生對人工智慧的素養、道德和技能。教育機構開始將人工智慧相關內容納入STEM課程，為學生在資料科學、機器人技術和機器學習等領域的職業發展做好準備。課程設計者、人工智慧教育平台和培訓提供者擴大發現機會，可以打造尖端的培訓材料、認證和技能發展計劃。此外，各國政府和國際組織正在進一步推動以人工智慧為中心的教育作為戰略重點，這進一步推動了以智慧數位工具為支撐的課程現代化的需求。

缺乏標準化和規定不明確

人工智慧在教育領域的應用是一個快速發展的領域，但目前尚無廣泛接受的最佳實踐、標準或法律體制。由於教育部門缺乏關於演算法透明度、資料倫理和人工智慧審核的統一準則，教育機構往往難以滿足倫理和法律要求。這種模糊性會阻礙投資，延遲採用，並為濫用和違法行為創造機會。此外，缺乏標準使得難以比較人工智慧工具的有效性和卓越性，這可能導致學習結果不一致。這種模糊性可能導致訴訟風險，並且在監管嚴格的司法管轄區，也可能導致政府暫停對人工智慧計劃的資助。

COVID-19的影響：

新冠疫情迫使教育產業迅速轉向數位化和遠距學習，顯著加速了人工智慧在教育市場的普及。為了保持教學和評估的連續性，世界各地的教育機構在實體教室關閉期間正在轉向人工智慧主導的平台。個人化學習、自動化管理業務以及在虛擬環境中追蹤學生表現的即時分析，都得益於人工智慧工具。此外，這場危機也凸顯了對包容性、可擴展性和技術支援型教育的需求，促使教育科技公司和政府加大對人工智慧的投資。

機器學習將成為預測期內最大的市場

預計機器學習領域將在預測期內佔據最大的市場佔有率。為了提供個人化的學習體驗，機器學習演算法使平台能夠分析大量的學生數據，包括考試成績、互動模式和學習行為。這些系統能夠預測學生的表現、糾正內容並通知教師進行早期療育。 ML 還支援資源推薦、自動評分和效能分析等後端功能，這使其對於學術和管理使用案例都至關重要。此外，機器學習是教育領域使用最廣泛、最重要的人工智慧技術，因為它具有擴充性、透過數據不斷改進的能力以及與各種教育工具的兼容性。

預計自適應評估和評分部分在預測期內將以最高的複合年成長率成長。

自我調整評估和評分領域預計將在預測期內實現最高成長率。透過提供與評分標準一致的論文、計劃和測驗的Brick回饋，人工智慧主導的自適應評估系統正在徹底改變教師評估學生表現的方式，顯著縮短評分時間。這些系統使用機器學習和自然語言處理來識別誤解模式並提供個人化的改進計劃。隨著教育轉向持續的、基於能力的模式，對智慧、反饋豐富且擴充性的評估工具的需求日益成長，而這個市場是人工智慧驅動的學習生態系統創新和投資的主要驅動力。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率，這得益於其對教育創新的大量投資、頂尖教育科技公司的強勁存在以及先進的數位基礎設施。在積極的政府政策和資金推動下，該地區受益於人工智慧技術在K-12和高等教育以及企業學習環境中的早期應用。美國一些機構和新興企業率先推出了智慧輔導系統、自動評分工具和個人化學習平台等人工智慧應用，推動了市場擴張。此外，北美對數據主導教育、人工智慧課程整合和平等數位存取的重視，鞏固了其在全球教育人工智慧市場的主導地位。

複合年成長率最高的地區：

預計亞太地區在預測期內將呈現最高的複合年成長率，這得益於網際網路使用率的上升、數位轉型步伐的加快以及政府不斷加大對教育體系更新的力度。中國、印度、日本和韓國等國家正積極投資以人工智慧為基礎的教育技術，以解決教師短缺問題、提升學習成果，並擴大服務欠缺地區和農村地區獲得優質教育的機會。蓬勃發展的教育科技Start-Ups生態系統、日益成長的科技型學生數量，以及諸如中國《人工智慧發展藍圖》和印度《2020年國家教育政策》（NEP）等鼓舞人心的國家政策，共同推動了這一領域的快速發展。

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  • 地理細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
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    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 教育領域人工智慧市場（按組成部分）

  • 軟體解決方案
  • 服務

第6章 教育領域人工智慧市場（按部署）

  • 雲端基礎
  • 本地
  • 混合

7. 教育領域人工智慧市場（以交付模式）

  • 行動應用程式
  • 網路為基礎的平台

第8章 教育領域的人工智慧市場（按技術）

  • 深度學習
  • 機器學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 語音辨識
  • 邊緣人工智慧和設備內推理

第9章：人工智慧教育市場（按應用）

  • 智慧輔導系統
  • 虛擬促進者與學習環境
  • 學習分析和推薦引擎
  • 自動化管理和監督
  • 內容傳送系統
  • 自適應評估和評分
  • 其他用途

第 10 章：教育領域的人工智慧市場（按最終用戶分類）

  • K-12教育
  • 高等教育
  • 企業培訓與學習
  • 教育出版商
  • 政府、非政府組織與非正式學習平台
  • 其他最終用戶

第 11 章：按地區分類的教育人工智慧市場

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第13章 公司概況

  • 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.