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市场调查报告书
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1880525

人工智慧驱动的个人化学习系统市场预测至2032年：按组件、学习类型、存取模式、部署模式、应用、最终用户和地区分類的全球分析

AI-Driven Personalized Learning Systems Market Forecasts to 2032 - Global Analysis By Component (Platform and Services), Learning Type, Access Mode, Deployment Mode, 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 的一项研究，全球人工智慧驱动的个人化学习系统市场预计到 2025 年将达到 71.9 亿美元，到 2032 年将达到 203.2 亿美元，在预测期内的复合年增长率为 16%。

人工智慧驱动的个人化学习系统是一种教育平台，它利用人工智慧技术来优化学习体验，使其与每个学习者的需求、偏好和学习表现相匹配。透过分析学习进度、评估结果和参与度等数据，这些系统能够动态调整学习内容、推荐学习资源并提供即时回馈。其主要功能包括智慧辅导、自适应评估和个人化学习路径，从而优化技能习得和维持。这些系统适用于K-12教育、高等教育、企业培训和终身学习等各种环境中的学习者。透过提高参与度、改善学习成果并实现可扩展的个人化，人工智慧驱动的系统正在将传统教育转变为更有效率、以学习者为中心的学习体验。

个别化学习的需求

学习者期望获得根据自身学习目标和认知特点量身定制的内容、循序渐进的学习进度和回馈。平台利用人工智慧引擎、基于规则的逻辑和行为分析技术，即时调整教学内容。与学习管理系统 (LMS)、行动应用和游戏化模组的集成，能够提升学习者的参与度和留存率。教育机构、雇主和教育科技Start-Ups都在寻求扩充性、全面且以结果为导向的解决方案。这些趋势正在推动人工智慧驱动的个人化学习系统的应用。

资料隐私和安全问题

自适应系统会收集敏感的学习者数据，包括表现生物识别和行为模式，因此需要强大的加密和使用者同意通讯协定。企业在满足《家庭教育权利和隐私法案》(FERPA)、 《一般资料保护规则》(GDPR) 和区域合规要求的同时，还要保持个人化，这面临着许多挑战。缺乏透明度、演算法偏差和第三方存取权限进一步加剧了实施的复杂性。供应商必须投资于符合伦理的人工智慧、隐私仪表板和安全的云端架构，以降低风险。这些限制持续阻碍着合规驱动型学习环境中平台的成熟度。

扩大远距和混合式教育

教育机构和雇主正在拓展其数位化项目，以涵盖分散的学习者并提高灵活性。平台支援模组化内容、动态评估和个人化学习路径，并可在行动和桌面介面上运作。与虚拟教室、认证系统和分析仪表板的整合提高了学习的连续性和有效性。在正规教育、劳动力发展和终身学习领域，对扩充性、高弹性和以学习者为中心的基础设施的需求日益增长。这些趋势正在推动混合式和远距学习、人工智慧驱动的个人化学习系统的发展。

高昂的实施和整合成本

自适应系统需要对内容标籤、后端整合和教师培训进行投资，这减缓了其普及速度。企业难以将传统基础设施与云端原生引擎和互通性标准相容。缺乏内部专业知识和变更管理进一步加剧了扩展性和效能方面的挑战。供应商必须提供模组化定价、部署支援和低程式码接口，以提高可访问性。在预算敏感且抵制变革的教育产业，这些限制持续限制平台绩效。

新冠疫情的影响：

疫情加速了数位化学习的普及，同时也暴露了个人化、互动性和学习者支持方面的不足。封锁措施扰乱了课堂教学，并增加了对支援远距离诊断和自主学习的自适应平台的需求。教育机构部署了人工智慧引擎，以指导不同学习群体的补习、强化和能力提升。公立和私立教育系统在云端迁移、内容数位化和分析方面的投资激增。政策制定者和消费者越来越关注学习损失、公平性和数位化教学方法。这些变化强化了对自适应和弹性学习基础设施的长期投资。

预计在预测期内，影片学习领域将占据最大的市场份额。

在预测期内，由于其便利性、互动性和与自适应引擎的兼容性，影片学习预计将占据最大的市场份额。平台利用互动式影片、分支逻辑和内建评估来实现个人化教学和进度追踪。与行动应用、学习管理系统 (LMS) 和内容库的整合扩大了覆盖范围并增强了学习者的自主控制权。 K-12、高等教育和专业培训领域对视觉身临其境型和自主学习模式的需求日益增长。供应商提供模组化影片堆迭、人工智慧标籤和分析仪表板以辅助实施。这些功能正在巩固影片学习在人工智慧驱动的个人化学习系统中的主导地位。

预计在预测期内，技能发展和认证领域将以最高的复合年增长率成长。

预计在预测期内，技能发展和认证领域将实现最高成长率，因为该平台正拓展至员工技能提升、认证和绩效追踪等领域。学习者透过自适应学习路径获得与工作相关的技能，并努力取得符合业界标准的微证书。该平台支援企业培训和职业培训项目中的能力映射、个人化评估和数位徽章。与人力资源系统、学习管理系统平台和职业服务机构的整合提升了平台的价值和学员留存率。雇主、自由工作者和成人学习者对扩充性、检验且与成果挂钩的学习需求日益增长。这些趋势正在推动以技能为中心、人工智慧驱动的个人化学习系统和服务的发展。

占比最大的地区：

由于教育科技的成熟、机构投资以及监管机构对人工智慧驱动的个人化学习系统的承诺，预计北美地区将在预测期内占据最大的市场份额。各公司正在学校、大学和企业内部培训中部署平台，以提高个人化程度、学生留存率和学习成果。对人工智慧引擎、云端基础设施和数位教学法的投资为创新和扩充性提供了支持。主要供应商、研究机构和政策框架的存在正在推动该生态系统的深化和广泛应用。各公司正在调整其适应性策略，使其与第一类教育补助金（Title I）的要求、劳动力发展和终身学习目标保持一致。这些因素正在推动北美在人工智慧驱动的个人化学习系统的商业化和管治处于主导地位。

年复合成长率最高的地区：

预计亚太地区在预测期内将实现最高的复合年增长率，这主要得益于该地区各国经济对教育的需求不断增长、行动网路普及率不断提高以及数位转型日益融合。印度、中国、印尼和越南等国家正在各个教育阶段（包括K-12、高等教育和职业培训）扩展其平台。政府支持计画正在促进都市区地区的教育科技孵化、数位素养提升和远距学习基础建设。本地供应商正在提供行动优先、多语言且具有文化适应性的解决方案，以满足不同学习者的需求。正规和非正规教育系统对扩充性、全面且个人化的学习基础设施的需求日益增长。

免费客製化服务：

购买此报告的客户可以选择以下免费自订选项之一：

  • 公司概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 主要参与者（最多3家公司）的SWOT分析
  • 区域细分
    • 根据客户要求，提供主要国家的市场规模估算与预测，以及复合年增长率（註：可行性需确认）。
  • 竞争基准化分析
    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 原始研究资料
    • 二手研究资料
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

第四章 波特五力分析

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

5. 全球人工智慧驱动的个人化学习系统市场（按组件划分）

  • 介绍
  • 平台
  • 服务
    • 实施与集成
    • 支援与维护
    • 咨询

6. 全球人工智慧驱动的个人化学习系统市场（按学习类型划分）

  • 介绍
  • 基于视讯的学习
  • 以文本为基础的学习
  • 基于音讯的学习
  • 混合/多模态学习
  • 其他学习类型

7. 全球人工智慧驱动的个人化学习系统市场（按访问模式划分）

  • 介绍
  • 桌面
  • 药片
  • 智慧型手机
  • VR/AR设备
  • 其他接取方式

8. 全球人工智慧驱动的个人化学习系统市场（按部署模式划分）

  • 介绍
  • 云端基础的
  • 本地部署

9. 全球人工智慧驱动的个人化学习系统市场（按应用划分）

  • 介绍
  • 技能发展与认证
  • 基于课程的学习
  • 企业培训与合规
  • 考试准备与评估
  • 其他用途

第十章 由全球人工智慧驱动的个人化学习系统市场（依最终用户划分）

  • 介绍
  • 高等教育机构
  • 公司
  • 政府和国防部
  • 职业技术培训中心
  • 其他最终用户

第十一章 由全球人工智慧驱动的个人化学习系统市场（按地区划分）

  • 介绍
  • 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
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲国家
  • 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
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十三章：企业概况

  • 360Learning
  • Adaptemy
  • CogBooks
  • Disprz
  • edyoucated
  • OttoLearn
  • Paradiso Solutions
  • Pearson plc
  • Realizeit
  • Smart Sparrow
  • DreamBox Learning Inc.
  • Knewton Inc.
  • McGraw Hill LLC
  • Area9 Lyceum ApS
  • Squirrel AI Learning Inc.
Product Code: SMRC32502

According to Stratistics MRC, the Global AI-Driven Personalized Learning Systems Market is accounted for $7.19 billion in 2025 and is expected to reach $20.32 billion by 2032 growing at a CAGR of 16% during the forecast period. AI-Driven Personalized Learning Systems are educational platforms that leverage artificial intelligence to tailor learning experiences to individual learners' needs, preferences, and performance. By analyzing data such as learning pace, assessment results, and engagement patterns, these systems dynamically adapt content, recommend resources, and provide real-time feedback. Features often include intelligent tutoring, adaptive assessments, and personalized learning pathways that optimize skill acquisition and retention. They support diverse learners across K-12, higher education, corporate training, and lifelong learning environments. By enhancing engagement, improving outcomes, and enabling scalable personalization, AI-driven systems are transforming traditional education into more efficient, learner-centric experiences.

Market Dynamics:

Driver:

Demand for personalized learning

Learners seek tailored content pacing and feedback based on performance goals and cognitive profiles. Platforms use AI engines rule-based logic and behavioral analytics to adapt instruction in real time. Integration with LMS systems mobile apps and gamified modules enhances engagement and retention. Demand for scalable inclusive and outcome-driven solutions is rising across institutions employers and edtech startups. These dynamics are propelling deployment across AI-driven personalized learning systems.

Restraint:

Data privacy & security concerns

Adaptive systems collect sensitive learner data including performance biometrics and behavioral patterns which require robust encryption and consent protocols. Enterprises face challenges in meeting FERPA GDPR and regional compliance mandates while maintaining personalization. Lack of transparency algorithmic bias and third-party access further complicate adoption. Vendors must invest in ethical AI privacy dashboards and secure cloud architecture to reduce risk. These constraints continue to hinder platform maturity across compliance-sensitive learning environments.

Opportunity:

Growth of remote & hybrid education

Institutions and employers are scaling digital programs to reach distributed learners and improve flexibility. Platforms support modular content dynamic assessments and personalized pathways across mobile and desktop interfaces. Integration with virtual classrooms credentialing systems and analytics dashboards enhances continuity and impact. Demand for scalable resilient and learner-centric infrastructure is rising across formal education workforce development and lifelong learning. These trends are fostering growth across hybrid and remote-enabled AI-driven personalized learning systems.

Threat:

High implementation & integration costs

Adaptive systems require investment in content tagging backend integration and faculty training which delays deployment. Enterprises face challenges in aligning legacy infrastructure with cloud-native engines and interoperability standards. Lack of internal expertise and change management further complicates scaling and performance. Vendors must offer modular pricing onboarding support and low-code interfaces to improve accessibility. These limitations continue to restrict platform performance across budget-sensitive and transformation-resistant education segments.

Covid-19 Impact:

The pandemic accelerated digital learning adoption while exposing gaps in personalization engagement and learner support. Lockdowns disrupted classroom instruction and increased demand for adaptive platforms that support remote diagnostics and individualized pacing. Institutions deployed AI-powered engines to guide remediation enrichment and mastery across diverse learner cohorts. Investment in cloud migration content digitization and analytics surged across public and private education systems. Public awareness of learning loss equity and digital pedagogy increased across policy and consumer circles. These shifts are reinforcing long-term investment in adaptive and resilient learning infrastructure.

The video-based learning segment is expected to be the largest during the forecast period

The video-based learning segment is expected to account for the largest market share during the forecast period due to its accessibility engagement and compatibility with adaptive engines. Platforms use interactive videos branching logic and embedded assessments to personalize instruction and track progress. Integration with mobile apps LMS systems and content libraries enhances reach and learner control. Demand for visual immersive and self-paced formats is rising across K-12 higher education and professional training. Vendors offer modular video stacks AI tagging and analytics dashboards to support deployment. These capabilities are boosting segment dominance across video-enabled AI-driven personalized learning systems.

The skill development & certification segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the skill development & certification segment is predicted to witness the highest growth rate as platforms expand across workforce reskilling credentialing and performance tracking. Learners pursue adaptive pathways to acquire job-relevant skills and earn microcredentials aligned with industry standards. Platforms support competency mapping personalized assessments and digital badges across enterprise and vocational programs. Integration with HR systems LMS platforms and career services enhances value and continuity. Demand for scalable verified and outcome-linked learning is rising across employers freelancers and adult learners. These dynamics are accelerating growth across skill-focused AI-driven personalized learning systems and services.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its edtech maturity institutional investment and regulatory engagement across AI-driven personalized learning systems. Enterprises deploy platforms across schools universities and corporate training to improve personalization retention and outcomes. Investment in AI engines cloud infrastructure and digital pedagogy supports innovation and scalability. Presence of leading vendors research institutions and policy frameworks drives ecosystem depth and adoption. Firms align adaptive strategies with Title I mandates workforce development and lifelong learning goals. These factors are propelling North America's leadership in AI-driven personalized learning systems commercialization and governance.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as education demand mobile penetration and digital transformation converge across regional economies. Countries like India China Indonesia and Vietnam scale platforms across K-12 higher education and vocational training. Government-backed programs support edtech incubation digital literacy and remote learning infrastructure across urban and rural zones. Local providers offer mobile-first multilingual and culturally adapted solutions tailored to diverse learner profiles. Demand for scalable inclusive and personalized learning infrastructure is rising across formal and informal education systems.

Key players in the market

Some of the key players in AI-Driven Personalized Learning Systems Market include 360Learning, Adaptemy, CogBooks, Disprz, edyoucated, OttoLearn, Paradiso Solutions, Pearson plc, Realizeit, Smart Sparrow, DreamBox Learning Inc., Knewton Inc., McGraw Hill LLC, Area9 Lyceum ApS and Squirrel AI Learning Inc.

Key Developments:

In April 2025, Adaptemy launched an upgraded Curriculum Mapping Engine, enabling granular alignment between student performance and national learning outcomes. The tool offers automatic content suggestions, real-time feedback loops, and teacher dashboards for differentiated instruction.

In October 2023, 360Learning acquired eLamp, a French AI-powered skills management platform, to strengthen its AI-driven personalized learning systems capabilities. The acquisition enabled 360Learning to map skill gaps more precisely and deliver personalized upskilling paths using AI.

Components Covered:

  • Platform
  • Services

Learning Types Covered:

  • Video-Based Learning
  • Text-Based Learning
  • Voice-Based Learning
  • Hybrid/Multimodal Learning
  • Other Learning Types

Access Modes Covered:

  • Desktop
  • Tablets
  • Smartphones
  • VR/AR Devices
  • Other Access Modes

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises

Applications Covered:

  • Skill Development & Certification
  • Curriculum-Based Learning
  • Corporate Training & Compliance
  • Test Preparation & Assessment
  • Other Applications

End Users Covered:

  • Higher Education Institutions
  • Corporate Enterprises
  • Government & Defense
  • Vocational & Technical Training Centers
  • 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 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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-driven Personalized Learning Systems Market, By Component

  • 5.1 Introduction
  • 5.2 Platform
  • 5.3 Services
    • 5.3.1 Implementation & Integration
    • 5.3.2 Support & Maintenance
    • 5.3.3 Consulting

6 Global AI-driven Personalized Learning Systems Market, By Learning Type

  • 6.1 Introduction
  • 6.2 Video-Based Learning
  • 6.3 Text-Based Learning
  • 6.4 Voice-Based Learning
  • 6.5 Hybrid/Multimodal Learning
  • 6.6 Other Learning Types

7 Global AI-driven Personalized Learning Systems Market, By Access Mode

  • 7.1 Introduction
  • 7.2 Desktop
  • 7.3 Tablets
  • 7.4 Smartphones
  • 7.5 VR/AR Devices
  • 7.6 Other Access Modes

8 Global AI-driven Personalized Learning Systems Market, By Deployment Mode

  • 8.1 Introduction
  • 8.2 Cloud-Based
  • 8.3 On-Premises

9 Global AI-driven Personalized Learning Systems Market, By Application

  • 9.1 Introduction
  • 9.2 Skill Development & Certification
  • 9.3 Curriculum-Based Learning
  • 9.4 Corporate Training & Compliance
  • 9.5 Test Preparation & Assessment
  • 9.6 Other Applications

10 Global AI-driven Personalized Learning Systems Market, By End User

  • 10.1 Introduction
  • 10.2 igher Education Institutions
  • 10.3 Corporate Enterprises
  • 10.4 Government & Defense
  • 10.5 Vocational & Technical Training Centers
  • 10.6 Other End Users

11 Global AI-driven Personalized Learning Systems 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 360Learning
  • 13.2 Adaptemy
  • 13.3 CogBooks
  • 13.4 Disprz
  • 13.5 edyoucated
  • 13.6 OttoLearn
  • 13.7 Paradiso Solutions
  • 13.8 Pearson plc
  • 13.9 Realizeit
  • 13.10 Smart Sparrow
  • 13.11 DreamBox Learning Inc.
  • 13.12 Knewton Inc.
  • 13.13 McGraw Hill LLC
  • 13.14 Area9 Lyceum ApS
  • 13.15 Squirrel AI Learning Inc.

List of Tables

  • Table 1 Global AI-driven Personalized Learning Systems Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-driven Personalized Learning Systems Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-driven Personalized Learning Systems Market Outlook, By Platform (2024-2032) ($MN)
  • Table 4 Global AI-driven Personalized Learning Systems Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI-driven Personalized Learning Systems Market Outlook, By Implementation & Integration (2024-2032) ($MN)
  • Table 6 Global AI-driven Personalized Learning Systems Market Outlook, By Support & Maintenance (2024-2032) ($MN)
  • Table 7 Global AI-driven Personalized Learning Systems Market Outlook, By Consulting (2024-2032) ($MN)
  • Table 8 Global AI-driven Personalized Learning Systems Market Outlook, By Learning Type (2024-2032) ($MN)
  • Table 9 Global AI-driven Personalized Learning Systems Market Outlook, By Video-Based Learning (2024-2032) ($MN)
  • Table 10 Global AI-driven Personalized Learning Systems Market Outlook, By Text-Based Learning (2024-2032) ($MN)
  • Table 11 Global AI-driven Personalized Learning Systems Market Outlook, By Voice-Based Learning (2024-2032) ($MN)
  • Table 12 Global AI-driven Personalized Learning Systems Market Outlook, By Hybrid/Multimodal Learning (2024-2032) ($MN)
  • Table 13 Global AI-driven Personalized Learning Systems Market Outlook, By Other Learning Types (2024-2032) ($MN)
  • Table 14 Global AI-driven Personalized Learning Systems Market Outlook, By Access Mode (2024-2032) ($MN)
  • Table 15 Global AI-driven Personalized Learning Systems Market Outlook, By Desktop (2024-2032) ($MN)
  • Table 16 Global AI-driven Personalized Learning Systems Market Outlook, By Tablets (2024-2032) ($MN)
  • Table 17 Global AI-driven Personalized Learning Systems Market Outlook, By Smartphones (2024-2032) ($MN)
  • Table 18 Global AI-driven Personalized Learning Systems Market Outlook, By VR/AR Devices (2024-2032) ($MN)
  • Table 19 Global AI-driven Personalized Learning Systems Market Outlook, By Other Access Modes (2024-2032) ($MN)
  • Table 20 Global AI-driven Personalized Learning Systems Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 21 Global AI-driven Personalized Learning Systems Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 22 Global AI-driven Personalized Learning Systems Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 23 Global AI-driven Personalized Learning Systems Market Outlook, By Application (2024-2032) ($MN)
  • Table 24 Global AI-driven Personalized Learning Systems Market Outlook, By Skill Development & Certification (2024-2032) ($MN)
  • Table 25 Global AI-driven Personalized Learning Systems Market Outlook, By Curriculum-Based Learning (2024-2032) ($MN)
  • Table 26 Global AI-driven Personalized Learning Systems Market Outlook, By Corporate Training & Compliance (2024-2032) ($MN)
  • Table 27 Global AI-driven Personalized Learning Systems Market Outlook, By Test Preparation & Assessment (2024-2032) ($MN)
  • Table 28 Global AI-driven Personalized Learning Systems Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 29 Global AI-driven Personalized Learning Systems Market Outlook, By End User (2024-2032) ($MN)
  • Table 30 Global AI-driven Personalized Learning Systems Market Outlook, By Higher Education Institutions (2024-2032) ($MN)
  • Table 31 Global AI-driven Personalized Learning Systems Market Outlook, By Corporate Enterprises (2024-2032) ($MN)
  • Table 32 Global AI-driven Personalized Learning Systems Market Outlook, By Government & Defense (2024-2032) ($MN)
  • Table 33 Global AI-driven Personalized Learning Systems Market Outlook, By Vocational & Technical Training Centers (2024-2032) ($MN)
  • Table 34 Global AI-driven Personalized Learning Systems Market Outlook, By Other End Users (2024-2032) ($MN)

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