AI In Education Market by Software Type (Learning Management System (LMS), Adaptive Learning Platform, Chatbot & Virtual Assistant, Plagiarism Detection Tools), Technology, Academic Application (Predictive Analysis, Gamification) - Global Forecast to 2030
The AI in education market is projected to grow from USD 2.21 billion in 2024 to USD 5.82 billion by 2030, at a compound annual growth rate (CAGR) of 17.5% during the forecast period. The growing reliance on data-driven insights to improve academic outcomes and the rising demand for personalized learning experiences. The emergence of virtual tutors is empowering self-paced learning, while the integration of AR and VR with AI is revolutionizing education by delivering immersive, interactive experiences. Together, these advancements are reshaping traditional learning methods, paving the way for more adaptive and engaging educational environment.
“By institutional application, student enrollment and retention analysis segment will lead the market during the forecast period.”
The increasing emphasis on personalized learning experiences and the need for educational institutions to improve student retention rates are driving this growth. AI technologies can analyze vast amounts of student data to identify trends and predict enrollment patterns, making it easier for institutions to tailor their strategies effectively. AI tools not only enhance student engagement but also help institutions optimize their resources, ultimately leading to improved educational outcomes.
“By region, Asia Pacific to register the highest CAGR market during the forecast period.” The Asia Pacific region is poised to exhibit the highest CAGR in the AI in education market, driven by several key factors that highlight its rapid expansion and adoption of advanced educational technologies. Generative AI tools facilitate tailored educational content, allowing students to engage with materials that match their individual learning styles and paces. For instance, AI-driven platforms can adapt lessons in real time based on student performance, fostering greater engagement and understanding. Furthermore, initiatives in these countries emphasize ethical AI use and teacher training, ensuring that educators are well-equipped to harness AI's potential responsibly. Overall, the effective application of generative AI in education across the Asia-Pacific is paving the way for innovative teaching and learning practices.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI in education market.
The study includes an in-depth competitive analysis of these key players in the AI in education market, including their company profiles, recent developments, and key market strategies.
Research Coverage
This research report categorizes the AI in education market by offering (software type and services), software type (Learning Management Systems (LMS), Chatbots and Virtual Assistants, Adaptive Learning Platforms, Automated Grading and Feedback Systems, Intelligent Tutoring Systems, Content Generation Tools, AI-enhanced Plagiarism Detection, Gamified Learning Platforms, and others), by deployment mode (cloud and on-premises), services (Professional Development Programs, Custom AI Platform Development, Data Analytics Consulting, Admission Services, Instructional services, and others) by technology (generative AI and other AI), by application (academic (Personalized learning and content management, grading and assessment management, Language translation and support, student support and service, Gamification and Engagement, Predictive Analysis, Plagiarism Detection and Academic Integrity) Institutional(Student enrollment and retention analysis, Administrative Process Automation, Alumni engagement and relationship management, Workforce alignment and skills mapping, Resource allocation and financial planning), by End user (academic (students, tutors (teachers & professors), parents & guardians, corporate trainers/ instructors, and Others) institutional (K-12, higher education, Research Firms & NGO, skill development & Corporate Training Centers, Government Education Departments, edtech companies, and others) and by region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the AI in education market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions and services, key strategies, Contracts, partnerships, and agreements. new product & service launches, mergers and acquisitions, and recent developments associated with the AI in education market. Competitive analysis of upcoming startups in the AI in education market ecosystem is covered in this report.
Key Benefits of Buying the Report
The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in education market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
“By institutional application, student enrollment and retention analysis segment will lead the market during the forecast period.”
The increasing emphasis on personalized learning experiences and the need for educational institutions to improve student retention rates are driving this growth. AI technologies can analyze vast amounts of student data to identify trends and predict enrollment patterns, making it easier for institutions to tailor their strategies effectively. AI tools not only enhance student engagement but also help institutions optimize their resources, ultimately leading to improved educational outcomes.
“By region, Asia Pacific to register the highest CAGR market during the forecast period.” The Asia Pacific region is poised to exhibit the highest CAGR in the AI in education market, driven by several key factors that highlight its rapid expansion and adoption of advanced educational technologies. Generative AI tools facilitate tailored educational content, allowing students to engage with materials that match their individual learning styles and paces. For instance, AI-driven platforms can adapt lessons in real time based on student performance, fostering greater engagement and understanding. Furthermore, initiatives in these countries emphasize ethical AI use and teacher training, ensuring that educators are well-equipped to harness AI's potential responsibly. Overall, the effective application of generative AI in education across the Asia-Pacific is paving the way for innovative teaching and learning practices.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI in education market.
- By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
- By Designation: C-Level: 35%, Director Level: 25%, and Others: 40%
- By Region: North America: 40%, Europe: 25%, Asia Pacific: 20%, Middle East & Africa: 10%, and Latin America: 5%.
The study includes an in-depth competitive analysis of these key players in the AI in education market, including their company profiles, recent developments, and key market strategies.
Research Coverage
This research report categorizes the AI in education market by offering (software type and services), software type (Learning Management Systems (LMS), Chatbots and Virtual Assistants, Adaptive Learning Platforms, Automated Grading and Feedback Systems, Intelligent Tutoring Systems, Content Generation Tools, AI-enhanced Plagiarism Detection, Gamified Learning Platforms, and others), by deployment mode (cloud and on-premises), services (Professional Development Programs, Custom AI Platform Development, Data Analytics Consulting, Admission Services, Instructional services, and others) by technology (generative AI and other AI), by application (academic (Personalized learning and content management, grading and assessment management, Language translation and support, student support and service, Gamification and Engagement, Predictive Analysis, Plagiarism Detection and Academic Integrity) Institutional(Student enrollment and retention analysis, Administrative Process Automation, Alumni engagement and relationship management, Workforce alignment and skills mapping, Resource allocation and financial planning), by End user (academic (students, tutors (teachers & professors), parents & guardians, corporate trainers/ instructors, and Others) institutional (K-12, higher education, Research Firms & NGO, skill development & Corporate Training Centers, Government Education Departments, edtech companies, and others) and by region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the AI in education market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions and services, key strategies, Contracts, partnerships, and agreements. new product & service launches, mergers and acquisitions, and recent developments associated with the AI in education market. Competitive analysis of upcoming startups in the AI in education market ecosystem is covered in this report.
Key Benefits of Buying the Report
The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in education market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
- Analysis of key drivers (Increasing demand for personalized learning experiences, rising adoption of e-learning platforms and digital education tools, increasing reliance on data-driven insights to enhance academic outcomes, the rising prevalence of mobile and smart devices enables ubiquitous learning.), restraints (Reluctance among institutions to replace traditional teaching/ learning methods), opportunities (Enhanced customization of curriculum to individual student needs, rise in demand for AI-powered assessment systems and real-time feedback, the advent of virtual tutors for self-paced learning, integration of AR and VR with AI for immersive learning experience), and challenges (Protecting sensitive student data from breaches, disparity in access to AI-enabled educational resources, misuse of AI tools for unethical academic practices, accessibility issues for students with disabilities) influencing the growth of the AI in education market.
- Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in education market
- Market Development: Comprehensive information about lucrative markets – the report analyses the AI in education market across varied regions.
- Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in education market
- Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players Microsoft (US), IBM (US), Google (US), Alibaba Cloud (China), AWS (US), Adobe (US), Pearson (UK), Baidu (China), OpenAI (US), Duolingo (US), Cengage Group (US), Knewton (US), Skillsoft (US), Udacity (US), Stride (US), HPE (US), Carnegie Learning (US), Dreambox Learning (US), Quizlet (US), Grammarly (US), Vimeo (US) among others in AI in education market.
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS & EXCLUSIONS
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.2.1 TOP-DOWN APPROACH
2.2.2 BOTTOM-UP APPROACH
2.3 MARKET FORECAST
2.4 RESEARCH ASSUMPTIONS
2.5 RISK ASSESSMENT
2.6 STUDY LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES IN AI IN EDUCATION MARKET
4.2 AI IN EDUCATION MARKET: TOP THREE ACADEMIC APPLICATIONS
4.3 NORTH AMERICA: AI IN EDUCATION MARKET, BY DEPLOYMENT MODE AND END USER
4.4 AI IN EDUCATION MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increase in demand for personalized learning experiences
5.2.1.2 Rise in adoption of e-learning platforms and digital education tools
5.2.1.3 Increase in reliance on data-driven insights to enhance academic outcomes
5.2.1.4 Rise in prevalence of mobile and smart devices enables ubiquitous learning
5.2.2 RESTRAINTS
5.2.2.1 Reluctance among institutions to replace traditional teaching/ learning methods
5.2.3 OPPORTUNITIES
5.2.3.1 Enhanced customization of curriculum to individual student needs
5.2.3.2 Rise in demand for AI-powered assessment systems and real-time feedback
5.2.3.3 Advent of virtual tutors for self-paced learning
5.2.3.4 Integration of AR and VR with AI for immersive learning experience
5.2.4 CHALLENGES
5.2.4.1 Protecting sensitive student data from breaches
5.2.4.2 Disparity in access to AI-enabled educational resources
5.2.4.3 Accessibility issues for students with disabilities
5.3 INDUSTRY TRENDS
5.3.1 EVOLUTION OF AI IN EDUCATION MARKET
5.3.2 CASE STUDY ANALYSIS
5.3.2.1 Google helped Ministry of Education of Malaysia to transform digital learning accessibility and efficiency
5.3.2.2 Alibaba Cloud helped GetCourse enhance online education with scalable and cost-effective solutions
5.3.2.3 IVMF overcame learning management challenges with Skillsoft’s custom learning paths and enhanced user experience
5.3.2.4 Stride Learning enhanced Reading Comprehension with stable diffusion on Amazon Bedrock
5.3.2.5 Century Tech and Epsom & Ewell High School transformed learning with AI integration and enhanced engagement
5.3.3 ECOSYSTEM ANALYSIS
5.3.3.1 Learning management system providers
5.3.3.2 Adaptive learning platform providers
5.3.3.3 Chatbots & virtual assistant providers
5.3.3.4 Automated grading & feedback system providers
5.3.3.5 Content generation tools providers
5.3.3.6 AI in education market end users
5.3.4 TECHNOLOGY ANALYSIS
5.3.4.1 Key technologies
5.3.4.1.1 NLP and deep learning
5.3.4.1.2 Computer vision
5.3.4.1.3 Predictive analytics
5.3.4.1.4 Robotic process automation (RPA)
5.3.4.1.5 Reinforcement learning
5.3.4.2 Adjacent technologies
5.3.4.2.1 Cybersecurity
5.3.4.2.2 IoT
5.3.4.2.3 AR/VR
5.3.4.3 Complementary technologies
5.3.4.3.1 Cloud computing
5.3.4.3.2 Edge computing
5.3.4.3.3 Quantum computing
5.3.4.3.4 Big data analytics
5.3.4.3.5 Blockchain
5.3.5 REGULATORY LANDSCAPE
5.3.5.1 Regulatory bodies, government agencies, and other organizations
5.3.5.2 Regulatory framework
5.3.5.2.1 North America
5.3.5.2.1.1 US
5.3.5.2.1.2 Canada
5.3.5.2.2 Europe
5.3.5.2.2.1 Germany
5.3.5.2.2.2 UK
5.3.5.2.3 Asia Pacific
5.3.5.2.3.1 China
5.3.5.2.3.2 Australia
5.3.5.2.3.3 Japan
5.3.5.2.3.4 Singapore
5.3.5.2.4 Middle East & Africa
5.3.5.2.4.1 Saudi Arabia
5.3.5.2.4.2 UAE
5.3.5.2.4.3 Egypt
5.3.5.2.5 Latin America
5.3.5.2.5.1 Brazil
5.3.5.2.5.2 Mexico
5.3.5.2.5.3 Argentina
5.3.6 SUPPLY CHAIN ANALYSIS
5.3.7 PORTER’S FIVE FORCES ANALYSIS
5.3.7.1 Threat of new entrants
5.3.7.2 Threat of substitutes
5.3.7.3 Bargaining power of suppliers
5.3.7.4 Bargaining power of buyers
5.3.7.5 Intensity of competitive rivalry
5.3.8 KEY CONFERENCES AND EVENTS
5.3.9 KEY STAKEHOLDERS AND BUYING CRITERIA
5.3.9.1 Key stakeholders in buying process
5.3.9.2 Buying criteria
5.3.10 PRICING ANALYSIS
5.3.10.1 Indicative pricing analysis, by software type
5.3.10.2 Indicative pricing analysis, by application
5.3.11 PATENT ANALYSIS
5.3.11.1 Methodology
5.3.11.2 Patents filed, by document type
5.3.11.3 Innovations and patent applications
5.3.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES
5.3.13 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
5.3.14 IMPACT OF GENERATIVE AI ON EDUCATION MARKET
5.3.14.1 Top use cases & market potential
5.3.14.2 Key use cases
5.3.14.2.1 Personalized learning experiences
5.3.14.2.2 Content creation automation
5.3.14.2.3 Enhanced feedback mechanisms
5.3.14.2.4 Immersive learning environments
5.3.14.2.5 Administrative efficiency
5.3.14.2.6 Support for diverse learning needs
6 AI IN EDUCATION MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: AI IN EDUCATION MARKET DRIVERS
6.2 SOFTWARE
6.2.1 BY TYPE
6.2.1.1 Learning management systems (LMS)
6.2.1.1.1 AI algorithms to create adaptive learning paths based on learner's performance, preferences, and engagement levels
6.2.1.2 Chatbots & virtual assistants
6.2.1.2.1 Offering study aid and providing safe space for students and helping educators with plans
6.2.1.3 Adaptive learning platforms
6.2.1.3.1 Tailored learning experiences with continuous assessment of learners’ progress
6.2.1.4 Automated grading & feedback systems
6.2.1.4.1 Enhancing efficiency and accuracy of evaluation devoid of biases
6.2.1.5 Intelligent tutoring systems
6.2.1.5.1 Advancing personalized learning focused on integrating multimedia resources and interactive simulations
6.2.1.6 Content generation tools
6.2.1.6.1 Empowering educators with AI-driven content creation for personalized learning
6.2.1.7 AI-enhanced plagiarism detection
6.2.1.7.1 Fostering academic integrity with pattern recognition
6.2.1.8 Gamified learning platforms
6.2.1.8.1 Transforming education with immersive, story-driven platforms
6.2.1.9 Other software types
6.2.2 BY DEPLOYMENT MODE
6.2.2.1 Cloud
6.2.2.1.1 Scaling education with cloud-powered AI: Personalized learning and seamless integration
6.2.2.2 On-premises
6.2.2.2.1 Empowering education on-premises: Secure, customizable, and controlled AI solutions
6.3 SERVICES
6.3.1 PROFESSIONAL DEVELOPMENT PROGRAMS
6.3.1.1 Essential to maintain educator competence and building community
6.3.2 CUSTOM AI DEVELOPMENT PLATFORM
6.3.2.1 Technology adaptability: Crucial in diverse learning landscape
6.3.3 DATA ANALYTICS CONSULTING
6.3.3.1 Optimizing course offerings and improving user experience with tailored content
6.3.4 ADMISSION SERVICES
6.3.4.1 Transforming admissions with AI: Streamlining processes and boosting recruitment
6.3.5 INSTRUCTIONAL SERVICES
6.3.5.1 Enabling educators to focus more on teaching and less on logistics
6.3.6 OTHER SERVICES
7 AI IN EDUCATION MARKET, BY APPLICATION
7.1 INTRODUCTION
7.1.1 APPLICATION: AI IN EDUCATION MARKET DRIVERS
7.2 ACADEMIC
7.2.1 PERSONALIZED LEARNING & CONTENT MANAGEMENT
7.2.1.1 Customizable lesson plans
7.2.1.1.1 Personalizing education with AI-driven lesson plans
7.2.1.2 Adaptive quizzes and tests
7.2.1.2.1 Tailoring assessments to enhance learning outcomes
7.2.1.3 Skill level analysis
7.2.1.3.1 Grouping students based on skill level for effective learning
7.2.1.4 Other personalized learning & content management applications
7.2.2 GRADING & ASSESSMENT MANAGEMENT
7.2.2.1 Essay & short-answer evaluation
7.2.2.1.1 Evaluating student understanding and critical thinking through essay & short-answer assessments
7.2.2.2 Multiple-choice grading automation
7.2.2.2.1 Streamlining grading and easing administrative burden on educators
7.2.2.3 Rubric-based assessment customization
7.2.2.3.1 Customizing rubrics for tailored and structured student performance evaluation
7.2.2.4 Other grading & assessment management applications
7.2.3 LANGUAGE TRANSLATION & SUPPORT
7.2.3.1 Grammar & vocabulary enhancement
7.2.3.1.1 Improving linguistic quality in AI-driven language translation tools
7.2.3.2 Pronunciation assistance
7.2.3.2.1 Using real-time feedback for more effective results
7.2.3.3 Language translation management
7.2.3.3.1 Streamlined translation with real-time collaboration among translators and project managers
7.2.3.4 Other language translation & support applications
7.2.4 STUDENT SUPPORT & SERVICES
7.2.4.1 Question-answering support
7.2.4.1.1 Proactive approach to improve student engagement
7.2.4.2 Assignment reminders
7.2.4.2.1 Boosting student engagement and accountability with assignment reminders
7.2.4.3 Campus navigation assistance
7.2.4.3.1 Enhancing student experience with interactive maps, GPS-based location services, and chatbots
7.2.4.4 Mental health resources & counseling support
7.2.4.4.1 Bridging gaps: Enhancing mental health support with access to information and reducing stigma
7.2.4.5 Other student support & services applications
7.2.5 GAMIFICATION & ENGAGEMENT
7.2.5.1 Points & reward system management
7.2.5.1.1 Maximizing engagement through points, badges, certificates, or even tangible rewards
7.2.5.2 Interactive challenges & quizzes
7.2.5.2.1 Enhancing learning with game mechanics such as time limits, levels of difficulty, and instant feedback
7.2.5.3 Gamified progress tracking
7.2.5.3.1 Gamified elements to monitor and visualize student achievements
7.2.5.4 Other gamification & engagement applications
7.2.6 PREDICTIVE ANALYSIS
7.2.6.1 Early-warning system management
7.2.6.1.1 Preventing dropouts and fostering success with effective early-warning systems
7.2.6.2 Trend analysis
7.2.6.2.1 Leveraging trend analysis and predictive analytics to improve learning outcomes
7.2.6.3 Predictive grading outcomes
7.2.6.3.1 Optimizing resource allocation with timely interventions using predictive models
7.2.6.4 Other predictive analysis applications
7.2.7 PLAGIARISM DETECTION & ACADEMIC INTEGRITY
7.2.7.1 Paraphrasing & source-checking
7.2.7.1.1 Promoting responsible research practices and originality
7.2.7.2 Academic integrity tracking
7.2.7.2.1 Promoting ethical standards and transparency in academics
7.2.7.3 Other plagiarism detection & academic integrity applications
7.3 INSTITUTIONAL
7.3.1 STUDENT ENROLLMENT & RETENTION ANALYSIS
7.3.1.1 At-risk student identification
7.3.1.1.1 Improving student outcomes with proactive AI interventions for retention
7.3.1.2 Retention strategy optimization
7.3.1.2.1 Analyzing student data to develop effective strategies to combat attrition and offer support
7.3.1.3 Personalized communication
7.3.1.3.1 Analyzing data such as academic performance and personal preferences to boost learning outcomes through notifications and messages
7.3.1.4 Student engagement
7.3.1.4.1 Improving academic performance and cultivating dynamic and inclusive learning environment
7.3.1.5 Other student enrollment & retention analysis applications
7.3.2 ADMINISTRATIVE PROCESS AUTOMATION
7.3.2.1 Automated admission processing
7.3.2.1.1 Streamlining admissions for faster, efficient workflows
7.3.2.2 Scheduling optimization
7.3.2.2.1 Efficient resource allocation and class management
7.3.2.3 Document verification automation
7.3.2.3.1 Need for accurate, error-free admissions
7.3.2.4 Other administrative process automation applications
7.3.3 ALUMNI ENGAGEMENT & RETENTION ANALYSIS
7.3.3.1 Alumni tracking
7.3.3.1.1 Building community using alum databases
7.3.3.2 Engagement metrics
7.3.3.2.1 Optimizing outreach by analyzing alum engagement patterns
7.3.3.3 Event recommendations
7.3.3.3.1 Increasing event participation with personalized event suggestions
7.3.3.4 Other alumni engagement & retention analysis applications
7.3.4 WORKFORCE ALIGNMENT & SKILLS MAPPING
7.3.4.1 Career services
7.3.4.1.1 Leveraging machine learning algorithms to evaluate future career success
7.3.4.2 Internship matchmaking
7.3.4.2.1 Enhancing job market competitiveness that would help students and companies
7.3.4.3 Skill-gap analysis
7.3.4.3.1 Curriculum development and training programs tailored to address specific deficiencies
7.3.4.4 Labor market demand analysis
7.3.4.4.1 Aligning education with market trends to facilitate internships and job placements
7.3.4.5 Other workforce alignment & skills mapping applications
7.3.5 RESOURCE ALLOCATION & FINANCIAL PLANNING
7.3.5.1 Predicting demand analysis
7.3.5.1.1 Utilizing data-driven methodologies to anticipate technological needs
7.3.5.2 Budget forecasting
7.3.5.2.1 Identifying funding gaps and prioritizing strategic investments
7.3.5.3 Financial aid allocation optimization
7.3.5.3.1 Optimizing financial aid allocation to support diverse student populations and improve educational outcomes
7.3.5.4 Other resource allocation & financial planning applications
8 AI IN EDUCATION MARKET, BY TECHNOLOGY
8.1 INTRODUCTION
8.1.1 GENERATIVE AI
8.1.2 OTHER AI
9 AI IN EDUCATION MARKET, BY END USER
9.1 INTRODUCTION
9.1.1 END USER: AI IN EDUCATION MARKET DRIVERS
9.2 BY TYPE
9.2.1 ACADEMIC
9.2.1.1 Students
9.2.1.1.1 Comprehensive, personalized means to identify individual strengths and weaknesses
9.2.1.2 Tutors
9.2.1.2.1 Offering educational, instructional, and administrative assistance
9.2.1.3 Parents & guardians
9.2.1.3.1 Leveraging AI for real-time insights and personalized support in education
9.2.1.4 Corporate trainers/instructors
9.2.1.4.1 Streamlining training operations and boosting employee productivity and training outcomes
9.2.1.5 Other academic end users
9.2.2 INSTITUTIONAL
9.2.2.1 K–12
9.2.2.1.1 Integrating AI in K–12 education to foster AI literacy among students
9.2.2.2 Higher education
9.2.2.2.1 Adopting AI in higher education to boost operational efficiency and student success
9.2.2.3 Research firms & NGO
9.2.2.3.1 Advocating for responsible AI Use in education to ensure ethical and inclusive learning environments
9.2.2.4 Skill development & corporate training centers
9.2.2.4.1 Leveraging AI in to enhance workforce readiness
9.2.2.5 Government education departments
9.2.2.5.1 Adopting AI to improve learning outcomes and efficiency across varying educational standards
9.2.2.6 EdTech companies
9.2.2.6.1 Making learning more efficient and accessible for diverse populations
9.2.2.7 Other institutional end users
10 AI IN EDUCATION MARKET, BY REGION
10.1 INTRODUCTION
10.2 NORTH AMERICA
10.2.1 NORTH AMERICA: AI IN EDUCATION MARKET DRIVERS
10.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
10.2.3 US
10.2.3.1 Revolutionizing US education with AI innovations and responsible frameworks
10.2.4 CANADA
10.2.4.1 Pioneering inclusive AI education pathways with AIPP Initiative
10.3 EUROPE
10.3.1 EUROPE: AI IN EDUCATION MARKET DRIVERS
10.3.2 EUROPE: MACROECONOMIC OUTLOOK
10.3.3 UK
10.3.3.1 Government investment and collaborations with schools and universities
10.3.4 GERMANY
10.3.4.1 Industry-academia collaboration with strategies such as National AI Strategy
10.3.5 FRANCE
10.3.5.1 Regulatory efforts and funding for research and establishment of Interdisciplinary Institutes of Artificial Intelligence
10.3.6 ITALY
10.3.6.1 AI pilot program in schools across four regions
10.3.7 SPAIN
10.3.7.1 Notable firms focusing on democratizing access to knowledge through AI-driven digital content platforms
10.3.8 REST OF EUROPE
10.4 ASIA PACIFIC
10.4.1 ASIA PACIFIC: AI IN EDUCATION MARKET DRIVERS
10.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
10.4.3 CHINA
10.4.3.1 Leading AI education transformation through public-private collaboration
10.4.4 JAPAN
10.4.4.1 Advancing AI literacy with Nationwide Educational Strategy and Teacher Training
10.4.5 INDIA
10.4.5.1 Accelerating AI education and innovation through government initiatives and industry collaborations
10.4.6 SOUTH KOREA
10.4.6.1 Strategies such as Digital Infrastructure Improvement Plan for integrating AI into public education
10.4.7 AUSTRALIA & NEW ZEALAND
10.4.7.1 Government focus on curriculum and building frameworks for AI in schools
10.4.8 ASEAN COUNTRIES
10.4.8.1 Promoting secure digital services and technology ecosystems with ASEAN Digital Masterplan 2025
10.4.9 REST OF ASIA PACIFIC
10.5 MIDDLE EAST & AFRICA
10.5.1 MIDDLE EAST & AFRICA: AI IN EDUCATION MARKET DRIVERS
10.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
10.5.3 MIDDLE EAST
10.5.3.1 KSA
10.5.3.1.1 Saudi Arabia to lead with advanced research and personalized learning systems
10.5.3.2 UAE
10.5.3.2.1 Advancing AI with University Curricula and Nationwide Teacher Training Initiatives
10.5.3.3 Bahrain
10.5.3.3.1 Enhancing education with AI-powered personalized learning at the British School of Bahrain
10.5.3.4 Kuwait
10.5.3.4.1 Kuwait University integrating AI to enhance education and ensure exam integrity
10.5.3.5 Rest of the Middle East
10.5.4 AFRICA
10.6 LATIN AMERICA
10.6.1 LATIN AMERICA: AI IN EDUCATION MARKET DRIVERS
10.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
10.6.3 BRAZIL
10.6.3.1 Innovative EdTech solutions and strategic investments
10.6.4 MEXICO
10.6.4.1 Modernizing education with AI-driven content creation and enhanced student engagement
10.6.5 ARGENTINA
10.6.5.1 Platforms for personalized learning and teaching support and projects such as ColabIA
10.6.6 REST OF LATIN AMERICA
11 COMPETITIVE LANDSCAPE
11.1 OVERVIEW
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2020–2024
11.3 REVENUE ANALYSIS, 2019–2023
11.4 MARKET SHARE ANALYSIS, 2023
11.4.1 MARKET SHARE ANALYSIS OF KEY PLAYERS
11.4.2 MARKET RANKING ANALYSIS
11.5 PRODUCT COMPARATIVE ANALYSIS
11.5.1 PRODUCT COMPARATIVE ANALYSIS, BY LEARNING MANAGEMENT SYSTEM (LMS)
11.5.1.1 Canvas LMS (Canvas)
11.5.1.2 AI-powered LMS (360Learning)
11.5.2 PRODUCT COMPARATIVE ANALYSIS OF INTELLIGENT TUTORING SYSTEMS
11.5.2.1 Carnegie Learning
11.5.2.2 Knewton Alta
11.5.2.3 Smart Sparrow
11.5.3 PRODUCT COMPARATIVE ANALYSIS OF CHATBOTS & PERSONAL ASSISTANTS
11.5.3.1 IBM Watson Tutor (IBM)
11.5.3.2 AdmitHub (Mainstay)
11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
11.7.1 COMPANY EVALUATION MATRIX: KEY PLAYERS (ACADEMIC), 2023
11.7.2 STARS
11.7.3 EMERGING LEADERS
11.7.4 PERVASIVE PLAYERS
11.7.5 PARTICIPANTS
11.7.6 COMPANY EVALUATION MATRIX: KEY PLAYERS (INSTITUTIONAL), 2023
11.7.7 STARS
11.7.8 EMERGING LEADERS
11.7.9 PERVASIVE PLAYERS
11.7.10 PARTICIPANTS
11.7.11 COMPANY FOOTPRINT: KEY PLAYERS
11.7.11.1 Company footprint
11.7.11.2 Software type footprint
11.7.11.3 Application footprint
11.7.11.4 End user footprint
11.7.11.5 Region footprint
11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
11.8.1 COMPANY EVALUATION MATRIX: STARTUPS/SMES (ACADEMIC), 2023
11.8.2 PROGRESSIVE COMPANIES
11.8.3 RESPONSIVE COMPANIES
11.8.4 DYNAMIC COMPANIES
11.8.5 STARTING BLOCKS
11.8.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES (INSTITUTIONAL), 2023
11.8.7 PROGRESSIVE COMPANIES
11.8.8 RESPONSIVE COMPANIES
11.8.9 DYNAMIC COMPANIES
11.8.10 STARTING BLOCKS
11.8.11 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
11.8.11.1 Detailed list of key startups/SMEs
11.8.11.2 Competitive benchmarking of key startups/SMEs
11.9 COMPETITIVE SCENARIO
11.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
11.9.2 DEALS
12 COMPANY PROFILES
12.1 INTRODUCTION
12.2 KEY PLAYERS
12.2.1 MICROSOFT
12.2.1.1 Business overview
12.2.1.2 Products/Solutions/Services offered
12.2.1.3 Recent developments
12.2.1.3.1 Product launches and enhancements
12.2.1.3.2 Deals
12.2.1.4 MnM view
12.2.1.4.1 Key strengths
12.2.1.4.2 Strategic choices
12.2.1.4.3 Weaknesses and competitive threats
12.2.2 IBM
12.2.2.1 Business overview
12.2.2.2 Products/Solutions/Services offered
12.2.2.3 Recent developments
12.2.2.3.1 Product launches and enhancements
12.2.2.3.2 Deals
12.2.2.4 MnM view
12.2.2.4.1 Key strengths
12.2.2.4.2 Strategic choices
12.2.2.4.3 Weaknesses and competitive threats
12.2.3 GOOGLE
12.2.3.1 Business overview
12.2.3.2 Products/Solutions/Services offered
12.2.3.3 Recent developments
12.2.3.3.1 Product launches and enhancements
12.2.3.3.2 Deals
12.2.3.4 MnM view
12.2.3.4.1 Key strengths
12.2.3.4.2 Strategic choices
12.2.3.4.3 Weaknesses and competitive threats
12.2.4 ALIBABA CLOUD
12.2.4.1 Business overview
12.2.4.2 Products/Solutions/Services offered
12.2.4.3 Recent developments
12.2.4.3.1 Product launches and enhancements
12.2.4.3.2 Deals
12.2.4.4 MnM view
12.2.4.4.1 Key strengths
12.2.4.4.2 Strategic choices
12.2.4.4.3 Weaknesses and competitive threats
12.2.5 AWS
12.2.5.1 Business overview
12.2.5.2 Products/Solutions/Services offered
12.2.5.3 Recent developments
12.2.5.3.1 Product launches and enhancements
12.2.5.4 MnM view
12.2.5.4.1 Key strengths
12.2.5.4.2 Strategic choices
12.2.5.4.3 Weaknesses and competitive threats
12.2.6 ADOBE
12.2.6.1 Business overview
12.2.6.2 Products/Solutions/Services offered
12.2.6.3 Recent developments
12.2.6.3.1 Deals
12.2.7 PEARSON
12.2.7.1 Business overview
12.2.7.2 Products/Solutions/Services offered
12.2.7.3 Recent developments
12.2.7.3.1 Product launches and enhancements
12.2.7.3.2 Deals
12.2.8 BAIDU
12.2.8.1 Business overview
12.2.8.2 Products/Solutions/Services offered
12.2.8.3 Recent developments
12.2.8.3.1 Product launches and enhancements
12.2.9 OPENAI
12.2.9.1 Business overview
12.2.9.2 Products/Solutions/Services offered
12.2.9.3 Recent developments
12.2.9.3.1 Product launches and enhancements
12.2.9.3.2 Deals
12.2.10 DUOLINGO
12.2.10.1 Business overview
12.2.10.2 Products/Solutions/Services offered
12.2.10.3 Recent developments
12.2.10.3.1 Product launches and enhancements
12.2.10.3.2 Deals
12.2.11 CENGAGE GROUP
12.2.12 KNEWTON
12.2.13 SKILLSOFT
12.2.14 UDACITY
12.2.15 STRIDE
12.2.16 HPE
12.2.17 DREAMBOX LEARNING
12.2.18 QUIZLET
12.2.19 GRAMMARLY
12.2.20 VIMEO
12.3 STARTUPS/SMES
12.3.1 RIIID
12.3.2 COGNII
12.3.3 ELSA SPEAK
12.3.4 MEMRISE
12.3.5 ALEF EDUCATION
12.3.6 QUERIUM
12.3.7 AMIRA LEARNING
12.3.8 KNOWRE
12.3.9 CENTURY TECH
12.3.10 THINKSTER MATH
12.3.11 QUIZIZZ
12.3.12 KHAN ACADEMY
12.3.13 SANA LABS
12.3.14 TEACHMINT X
12.3.15 360LEARNING
12.3.16 MAINSTAY
12.3.17 BLIPPAR
12.3.18 BLUE CANOE LEARNING
12.3.19 QUIZLET
12.3.20 OTTER.AI
12.3.21 QUILLBOT
12.3.22 NOLEJ
12.3.23 SPEECHIFY
13 ADJACENT AND RELATED MARKETS
13.1 INTRODUCTION
13.2 ARTIFICIAL INTELLIGENCE (AI) MARKET
13.2.1 MARKET DEFINITION
13.2.2 MARKET OVERVIEW
13.2.2.1 Artificial intelligence market, by offering
13.2.2.2 Artificial intelligence market, by business function
13.2.2.3 Artificial intelligence market, by technology
13.2.2.4 Artificial intelligence market, by vertical
13.2.2.5 Artificial intelligence market, by region
13.3 LEARNING MANAGEMENT SYSTEM MARKET
13.3.1 MARKET DEFINITION
13.3.2 MARKET OVERVIEW
13.3.2.1 LMS market, by offering
13.3.2.2 LMS market, by delivery mode
13.3.2.3 LMS market, by organization size
13.3.2.4 LMS market, by deployment type
13.3.2.5 LMS market, by application
13.3.2.6 LMS market, by user type
13.3.2.7 LMS market, by region
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 CUSTOMIZATION OPTIONS
14.4 RELATED REPORTS
14.5 AUTHOR DETAILS
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS & EXCLUSIONS
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.2.1 TOP-DOWN APPROACH
2.2.2 BOTTOM-UP APPROACH
2.3 MARKET FORECAST
2.4 RESEARCH ASSUMPTIONS
2.5 RISK ASSESSMENT
2.6 STUDY LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES IN AI IN EDUCATION MARKET
4.2 AI IN EDUCATION MARKET: TOP THREE ACADEMIC APPLICATIONS
4.3 NORTH AMERICA: AI IN EDUCATION MARKET, BY DEPLOYMENT MODE AND END USER
4.4 AI IN EDUCATION MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increase in demand for personalized learning experiences
5.2.1.2 Rise in adoption of e-learning platforms and digital education tools
5.2.1.3 Increase in reliance on data-driven insights to enhance academic outcomes
5.2.1.4 Rise in prevalence of mobile and smart devices enables ubiquitous learning
5.2.2 RESTRAINTS
5.2.2.1 Reluctance among institutions to replace traditional teaching/ learning methods
5.2.3 OPPORTUNITIES
5.2.3.1 Enhanced customization of curriculum to individual student needs
5.2.3.2 Rise in demand for AI-powered assessment systems and real-time feedback
5.2.3.3 Advent of virtual tutors for self-paced learning
5.2.3.4 Integration of AR and VR with AI for immersive learning experience
5.2.4 CHALLENGES
5.2.4.1 Protecting sensitive student data from breaches
5.2.4.2 Disparity in access to AI-enabled educational resources
5.2.4.3 Accessibility issues for students with disabilities
5.3 INDUSTRY TRENDS
5.3.1 EVOLUTION OF AI IN EDUCATION MARKET
5.3.2 CASE STUDY ANALYSIS
5.3.2.1 Google helped Ministry of Education of Malaysia to transform digital learning accessibility and efficiency
5.3.2.2 Alibaba Cloud helped GetCourse enhance online education with scalable and cost-effective solutions
5.3.2.3 IVMF overcame learning management challenges with Skillsoft’s custom learning paths and enhanced user experience
5.3.2.4 Stride Learning enhanced Reading Comprehension with stable diffusion on Amazon Bedrock
5.3.2.5 Century Tech and Epsom & Ewell High School transformed learning with AI integration and enhanced engagement
5.3.3 ECOSYSTEM ANALYSIS
5.3.3.1 Learning management system providers
5.3.3.2 Adaptive learning platform providers
5.3.3.3 Chatbots & virtual assistant providers
5.3.3.4 Automated grading & feedback system providers
5.3.3.5 Content generation tools providers
5.3.3.6 AI in education market end users
5.3.4 TECHNOLOGY ANALYSIS
5.3.4.1 Key technologies
5.3.4.1.1 NLP and deep learning
5.3.4.1.2 Computer vision
5.3.4.1.3 Predictive analytics
5.3.4.1.4 Robotic process automation (RPA)
5.3.4.1.5 Reinforcement learning
5.3.4.2 Adjacent technologies
5.3.4.2.1 Cybersecurity
5.3.4.2.2 IoT
5.3.4.2.3 AR/VR
5.3.4.3 Complementary technologies
5.3.4.3.1 Cloud computing
5.3.4.3.2 Edge computing
5.3.4.3.3 Quantum computing
5.3.4.3.4 Big data analytics
5.3.4.3.5 Blockchain
5.3.5 REGULATORY LANDSCAPE
5.3.5.1 Regulatory bodies, government agencies, and other organizations
5.3.5.2 Regulatory framework
5.3.5.2.1 North America
5.3.5.2.1.1 US
5.3.5.2.1.2 Canada
5.3.5.2.2 Europe
5.3.5.2.2.1 Germany
5.3.5.2.2.2 UK
5.3.5.2.3 Asia Pacific
5.3.5.2.3.1 China
5.3.5.2.3.2 Australia
5.3.5.2.3.3 Japan
5.3.5.2.3.4 Singapore
5.3.5.2.4 Middle East & Africa
5.3.5.2.4.1 Saudi Arabia
5.3.5.2.4.2 UAE
5.3.5.2.4.3 Egypt
5.3.5.2.5 Latin America
5.3.5.2.5.1 Brazil
5.3.5.2.5.2 Mexico
5.3.5.2.5.3 Argentina
5.3.6 SUPPLY CHAIN ANALYSIS
5.3.7 PORTER’S FIVE FORCES ANALYSIS
5.3.7.1 Threat of new entrants
5.3.7.2 Threat of substitutes
5.3.7.3 Bargaining power of suppliers
5.3.7.4 Bargaining power of buyers
5.3.7.5 Intensity of competitive rivalry
5.3.8 KEY CONFERENCES AND EVENTS
5.3.9 KEY STAKEHOLDERS AND BUYING CRITERIA
5.3.9.1 Key stakeholders in buying process
5.3.9.2 Buying criteria
5.3.10 PRICING ANALYSIS
5.3.10.1 Indicative pricing analysis, by software type
5.3.10.2 Indicative pricing analysis, by application
5.3.11 PATENT ANALYSIS
5.3.11.1 Methodology
5.3.11.2 Patents filed, by document type
5.3.11.3 Innovations and patent applications
5.3.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES
5.3.13 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
5.3.14 IMPACT OF GENERATIVE AI ON EDUCATION MARKET
5.3.14.1 Top use cases & market potential
5.3.14.2 Key use cases
5.3.14.2.1 Personalized learning experiences
5.3.14.2.2 Content creation automation
5.3.14.2.3 Enhanced feedback mechanisms
5.3.14.2.4 Immersive learning environments
5.3.14.2.5 Administrative efficiency
5.3.14.2.6 Support for diverse learning needs
6 AI IN EDUCATION MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: AI IN EDUCATION MARKET DRIVERS
6.2 SOFTWARE
6.2.1 BY TYPE
6.2.1.1 Learning management systems (LMS)
6.2.1.1.1 AI algorithms to create adaptive learning paths based on learner's performance, preferences, and engagement levels
6.2.1.2 Chatbots & virtual assistants
6.2.1.2.1 Offering study aid and providing safe space for students and helping educators with plans
6.2.1.3 Adaptive learning platforms
6.2.1.3.1 Tailored learning experiences with continuous assessment of learners’ progress
6.2.1.4 Automated grading & feedback systems
6.2.1.4.1 Enhancing efficiency and accuracy of evaluation devoid of biases
6.2.1.5 Intelligent tutoring systems
6.2.1.5.1 Advancing personalized learning focused on integrating multimedia resources and interactive simulations
6.2.1.6 Content generation tools
6.2.1.6.1 Empowering educators with AI-driven content creation for personalized learning
6.2.1.7 AI-enhanced plagiarism detection
6.2.1.7.1 Fostering academic integrity with pattern recognition
6.2.1.8 Gamified learning platforms
6.2.1.8.1 Transforming education with immersive, story-driven platforms
6.2.1.9 Other software types
6.2.2 BY DEPLOYMENT MODE
6.2.2.1 Cloud
6.2.2.1.1 Scaling education with cloud-powered AI: Personalized learning and seamless integration
6.2.2.2 On-premises
6.2.2.2.1 Empowering education on-premises: Secure, customizable, and controlled AI solutions
6.3 SERVICES
6.3.1 PROFESSIONAL DEVELOPMENT PROGRAMS
6.3.1.1 Essential to maintain educator competence and building community
6.3.2 CUSTOM AI DEVELOPMENT PLATFORM
6.3.2.1 Technology adaptability: Crucial in diverse learning landscape
6.3.3 DATA ANALYTICS CONSULTING
6.3.3.1 Optimizing course offerings and improving user experience with tailored content
6.3.4 ADMISSION SERVICES
6.3.4.1 Transforming admissions with AI: Streamlining processes and boosting recruitment
6.3.5 INSTRUCTIONAL SERVICES
6.3.5.1 Enabling educators to focus more on teaching and less on logistics
6.3.6 OTHER SERVICES
7 AI IN EDUCATION MARKET, BY APPLICATION
7.1 INTRODUCTION
7.1.1 APPLICATION: AI IN EDUCATION MARKET DRIVERS
7.2 ACADEMIC
7.2.1 PERSONALIZED LEARNING & CONTENT MANAGEMENT
7.2.1.1 Customizable lesson plans
7.2.1.1.1 Personalizing education with AI-driven lesson plans
7.2.1.2 Adaptive quizzes and tests
7.2.1.2.1 Tailoring assessments to enhance learning outcomes
7.2.1.3 Skill level analysis
7.2.1.3.1 Grouping students based on skill level for effective learning
7.2.1.4 Other personalized learning & content management applications
7.2.2 GRADING & ASSESSMENT MANAGEMENT
7.2.2.1 Essay & short-answer evaluation
7.2.2.1.1 Evaluating student understanding and critical thinking through essay & short-answer assessments
7.2.2.2 Multiple-choice grading automation
7.2.2.2.1 Streamlining grading and easing administrative burden on educators
7.2.2.3 Rubric-based assessment customization
7.2.2.3.1 Customizing rubrics for tailored and structured student performance evaluation
7.2.2.4 Other grading & assessment management applications
7.2.3 LANGUAGE TRANSLATION & SUPPORT
7.2.3.1 Grammar & vocabulary enhancement
7.2.3.1.1 Improving linguistic quality in AI-driven language translation tools
7.2.3.2 Pronunciation assistance
7.2.3.2.1 Using real-time feedback for more effective results
7.2.3.3 Language translation management
7.2.3.3.1 Streamlined translation with real-time collaboration among translators and project managers
7.2.3.4 Other language translation & support applications
7.2.4 STUDENT SUPPORT & SERVICES
7.2.4.1 Question-answering support
7.2.4.1.1 Proactive approach to improve student engagement
7.2.4.2 Assignment reminders
7.2.4.2.1 Boosting student engagement and accountability with assignment reminders
7.2.4.3 Campus navigation assistance
7.2.4.3.1 Enhancing student experience with interactive maps, GPS-based location services, and chatbots
7.2.4.4 Mental health resources & counseling support
7.2.4.4.1 Bridging gaps: Enhancing mental health support with access to information and reducing stigma
7.2.4.5 Other student support & services applications
7.2.5 GAMIFICATION & ENGAGEMENT
7.2.5.1 Points & reward system management
7.2.5.1.1 Maximizing engagement through points, badges, certificates, or even tangible rewards
7.2.5.2 Interactive challenges & quizzes
7.2.5.2.1 Enhancing learning with game mechanics such as time limits, levels of difficulty, and instant feedback
7.2.5.3 Gamified progress tracking
7.2.5.3.1 Gamified elements to monitor and visualize student achievements
7.2.5.4 Other gamification & engagement applications
7.2.6 PREDICTIVE ANALYSIS
7.2.6.1 Early-warning system management
7.2.6.1.1 Preventing dropouts and fostering success with effective early-warning systems
7.2.6.2 Trend analysis
7.2.6.2.1 Leveraging trend analysis and predictive analytics to improve learning outcomes
7.2.6.3 Predictive grading outcomes
7.2.6.3.1 Optimizing resource allocation with timely interventions using predictive models
7.2.6.4 Other predictive analysis applications
7.2.7 PLAGIARISM DETECTION & ACADEMIC INTEGRITY
7.2.7.1 Paraphrasing & source-checking
7.2.7.1.1 Promoting responsible research practices and originality
7.2.7.2 Academic integrity tracking
7.2.7.2.1 Promoting ethical standards and transparency in academics
7.2.7.3 Other plagiarism detection & academic integrity applications
7.3 INSTITUTIONAL
7.3.1 STUDENT ENROLLMENT & RETENTION ANALYSIS
7.3.1.1 At-risk student identification
7.3.1.1.1 Improving student outcomes with proactive AI interventions for retention
7.3.1.2 Retention strategy optimization
7.3.1.2.1 Analyzing student data to develop effective strategies to combat attrition and offer support
7.3.1.3 Personalized communication
7.3.1.3.1 Analyzing data such as academic performance and personal preferences to boost learning outcomes through notifications and messages
7.3.1.4 Student engagement
7.3.1.4.1 Improving academic performance and cultivating dynamic and inclusive learning environment
7.3.1.5 Other student enrollment & retention analysis applications
7.3.2 ADMINISTRATIVE PROCESS AUTOMATION
7.3.2.1 Automated admission processing
7.3.2.1.1 Streamlining admissions for faster, efficient workflows
7.3.2.2 Scheduling optimization
7.3.2.2.1 Efficient resource allocation and class management
7.3.2.3 Document verification automation
7.3.2.3.1 Need for accurate, error-free admissions
7.3.2.4 Other administrative process automation applications
7.3.3 ALUMNI ENGAGEMENT & RETENTION ANALYSIS
7.3.3.1 Alumni tracking
7.3.3.1.1 Building community using alum databases
7.3.3.2 Engagement metrics
7.3.3.2.1 Optimizing outreach by analyzing alum engagement patterns
7.3.3.3 Event recommendations
7.3.3.3.1 Increasing event participation with personalized event suggestions
7.3.3.4 Other alumni engagement & retention analysis applications
7.3.4 WORKFORCE ALIGNMENT & SKILLS MAPPING
7.3.4.1 Career services
7.3.4.1.1 Leveraging machine learning algorithms to evaluate future career success
7.3.4.2 Internship matchmaking
7.3.4.2.1 Enhancing job market competitiveness that would help students and companies
7.3.4.3 Skill-gap analysis
7.3.4.3.1 Curriculum development and training programs tailored to address specific deficiencies
7.3.4.4 Labor market demand analysis
7.3.4.4.1 Aligning education with market trends to facilitate internships and job placements
7.3.4.5 Other workforce alignment & skills mapping applications
7.3.5 RESOURCE ALLOCATION & FINANCIAL PLANNING
7.3.5.1 Predicting demand analysis
7.3.5.1.1 Utilizing data-driven methodologies to anticipate technological needs
7.3.5.2 Budget forecasting
7.3.5.2.1 Identifying funding gaps and prioritizing strategic investments
7.3.5.3 Financial aid allocation optimization
7.3.5.3.1 Optimizing financial aid allocation to support diverse student populations and improve educational outcomes
7.3.5.4 Other resource allocation & financial planning applications
8 AI IN EDUCATION MARKET, BY TECHNOLOGY
8.1 INTRODUCTION
8.1.1 GENERATIVE AI
8.1.2 OTHER AI
9 AI IN EDUCATION MARKET, BY END USER
9.1 INTRODUCTION
9.1.1 END USER: AI IN EDUCATION MARKET DRIVERS
9.2 BY TYPE
9.2.1 ACADEMIC
9.2.1.1 Students
9.2.1.1.1 Comprehensive, personalized means to identify individual strengths and weaknesses
9.2.1.2 Tutors
9.2.1.2.1 Offering educational, instructional, and administrative assistance
9.2.1.3 Parents & guardians
9.2.1.3.1 Leveraging AI for real-time insights and personalized support in education
9.2.1.4 Corporate trainers/instructors
9.2.1.4.1 Streamlining training operations and boosting employee productivity and training outcomes
9.2.1.5 Other academic end users
9.2.2 INSTITUTIONAL
9.2.2.1 K–12
9.2.2.1.1 Integrating AI in K–12 education to foster AI literacy among students
9.2.2.2 Higher education
9.2.2.2.1 Adopting AI in higher education to boost operational efficiency and student success
9.2.2.3 Research firms & NGO
9.2.2.3.1 Advocating for responsible AI Use in education to ensure ethical and inclusive learning environments
9.2.2.4 Skill development & corporate training centers
9.2.2.4.1 Leveraging AI in to enhance workforce readiness
9.2.2.5 Government education departments
9.2.2.5.1 Adopting AI to improve learning outcomes and efficiency across varying educational standards
9.2.2.6 EdTech companies
9.2.2.6.1 Making learning more efficient and accessible for diverse populations
9.2.2.7 Other institutional end users
10 AI IN EDUCATION MARKET, BY REGION
10.1 INTRODUCTION
10.2 NORTH AMERICA
10.2.1 NORTH AMERICA: AI IN EDUCATION MARKET DRIVERS
10.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
10.2.3 US
10.2.3.1 Revolutionizing US education with AI innovations and responsible frameworks
10.2.4 CANADA
10.2.4.1 Pioneering inclusive AI education pathways with AIPP Initiative
10.3 EUROPE
10.3.1 EUROPE: AI IN EDUCATION MARKET DRIVERS
10.3.2 EUROPE: MACROECONOMIC OUTLOOK
10.3.3 UK
10.3.3.1 Government investment and collaborations with schools and universities
10.3.4 GERMANY
10.3.4.1 Industry-academia collaboration with strategies such as National AI Strategy
10.3.5 FRANCE
10.3.5.1 Regulatory efforts and funding for research and establishment of Interdisciplinary Institutes of Artificial Intelligence
10.3.6 ITALY
10.3.6.1 AI pilot program in schools across four regions
10.3.7 SPAIN
10.3.7.1 Notable firms focusing on democratizing access to knowledge through AI-driven digital content platforms
10.3.8 REST OF EUROPE
10.4 ASIA PACIFIC
10.4.1 ASIA PACIFIC: AI IN EDUCATION MARKET DRIVERS
10.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
10.4.3 CHINA
10.4.3.1 Leading AI education transformation through public-private collaboration
10.4.4 JAPAN
10.4.4.1 Advancing AI literacy with Nationwide Educational Strategy and Teacher Training
10.4.5 INDIA
10.4.5.1 Accelerating AI education and innovation through government initiatives and industry collaborations
10.4.6 SOUTH KOREA
10.4.6.1 Strategies such as Digital Infrastructure Improvement Plan for integrating AI into public education
10.4.7 AUSTRALIA & NEW ZEALAND
10.4.7.1 Government focus on curriculum and building frameworks for AI in schools
10.4.8 ASEAN COUNTRIES
10.4.8.1 Promoting secure digital services and technology ecosystems with ASEAN Digital Masterplan 2025
10.4.9 REST OF ASIA PACIFIC
10.5 MIDDLE EAST & AFRICA
10.5.1 MIDDLE EAST & AFRICA: AI IN EDUCATION MARKET DRIVERS
10.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
10.5.3 MIDDLE EAST
10.5.3.1 KSA
10.5.3.1.1 Saudi Arabia to lead with advanced research and personalized learning systems
10.5.3.2 UAE
10.5.3.2.1 Advancing AI with University Curricula and Nationwide Teacher Training Initiatives
10.5.3.3 Bahrain
10.5.3.3.1 Enhancing education with AI-powered personalized learning at the British School of Bahrain
10.5.3.4 Kuwait
10.5.3.4.1 Kuwait University integrating AI to enhance education and ensure exam integrity
10.5.3.5 Rest of the Middle East
10.5.4 AFRICA
10.6 LATIN AMERICA
10.6.1 LATIN AMERICA: AI IN EDUCATION MARKET DRIVERS
10.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
10.6.3 BRAZIL
10.6.3.1 Innovative EdTech solutions and strategic investments
10.6.4 MEXICO
10.6.4.1 Modernizing education with AI-driven content creation and enhanced student engagement
10.6.5 ARGENTINA
10.6.5.1 Platforms for personalized learning and teaching support and projects such as ColabIA
10.6.6 REST OF LATIN AMERICA
11 COMPETITIVE LANDSCAPE
11.1 OVERVIEW
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2020–2024
11.3 REVENUE ANALYSIS, 2019–2023
11.4 MARKET SHARE ANALYSIS, 2023
11.4.1 MARKET SHARE ANALYSIS OF KEY PLAYERS
11.4.2 MARKET RANKING ANALYSIS
11.5 PRODUCT COMPARATIVE ANALYSIS
11.5.1 PRODUCT COMPARATIVE ANALYSIS, BY LEARNING MANAGEMENT SYSTEM (LMS)
11.5.1.1 Canvas LMS (Canvas)
11.5.1.2 AI-powered LMS (360Learning)
11.5.2 PRODUCT COMPARATIVE ANALYSIS OF INTELLIGENT TUTORING SYSTEMS
11.5.2.1 Carnegie Learning
11.5.2.2 Knewton Alta
11.5.2.3 Smart Sparrow
11.5.3 PRODUCT COMPARATIVE ANALYSIS OF CHATBOTS & PERSONAL ASSISTANTS
11.5.3.1 IBM Watson Tutor (IBM)
11.5.3.2 AdmitHub (Mainstay)
11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
11.7.1 COMPANY EVALUATION MATRIX: KEY PLAYERS (ACADEMIC), 2023
11.7.2 STARS
11.7.3 EMERGING LEADERS
11.7.4 PERVASIVE PLAYERS
11.7.5 PARTICIPANTS
11.7.6 COMPANY EVALUATION MATRIX: KEY PLAYERS (INSTITUTIONAL), 2023
11.7.7 STARS
11.7.8 EMERGING LEADERS
11.7.9 PERVASIVE PLAYERS
11.7.10 PARTICIPANTS
11.7.11 COMPANY FOOTPRINT: KEY PLAYERS
11.7.11.1 Company footprint
11.7.11.2 Software type footprint
11.7.11.3 Application footprint
11.7.11.4 End user footprint
11.7.11.5 Region footprint
11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
11.8.1 COMPANY EVALUATION MATRIX: STARTUPS/SMES (ACADEMIC), 2023
11.8.2 PROGRESSIVE COMPANIES
11.8.3 RESPONSIVE COMPANIES
11.8.4 DYNAMIC COMPANIES
11.8.5 STARTING BLOCKS
11.8.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES (INSTITUTIONAL), 2023
11.8.7 PROGRESSIVE COMPANIES
11.8.8 RESPONSIVE COMPANIES
11.8.9 DYNAMIC COMPANIES
11.8.10 STARTING BLOCKS
11.8.11 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
11.8.11.1 Detailed list of key startups/SMEs
11.8.11.2 Competitive benchmarking of key startups/SMEs
11.9 COMPETITIVE SCENARIO
11.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
11.9.2 DEALS
12 COMPANY PROFILES
12.1 INTRODUCTION
12.2 KEY PLAYERS
12.2.1 MICROSOFT
12.2.1.1 Business overview
12.2.1.2 Products/Solutions/Services offered
12.2.1.3 Recent developments
12.2.1.3.1 Product launches and enhancements
12.2.1.3.2 Deals
12.2.1.4 MnM view
12.2.1.4.1 Key strengths
12.2.1.4.2 Strategic choices
12.2.1.4.3 Weaknesses and competitive threats
12.2.2 IBM
12.2.2.1 Business overview
12.2.2.2 Products/Solutions/Services offered
12.2.2.3 Recent developments
12.2.2.3.1 Product launches and enhancements
12.2.2.3.2 Deals
12.2.2.4 MnM view
12.2.2.4.1 Key strengths
12.2.2.4.2 Strategic choices
12.2.2.4.3 Weaknesses and competitive threats
12.2.3 GOOGLE
12.2.3.1 Business overview
12.2.3.2 Products/Solutions/Services offered
12.2.3.3 Recent developments
12.2.3.3.1 Product launches and enhancements
12.2.3.3.2 Deals
12.2.3.4 MnM view
12.2.3.4.1 Key strengths
12.2.3.4.2 Strategic choices
12.2.3.4.3 Weaknesses and competitive threats
12.2.4 ALIBABA CLOUD
12.2.4.1 Business overview
12.2.4.2 Products/Solutions/Services offered
12.2.4.3 Recent developments
12.2.4.3.1 Product launches and enhancements
12.2.4.3.2 Deals
12.2.4.4 MnM view
12.2.4.4.1 Key strengths
12.2.4.4.2 Strategic choices
12.2.4.4.3 Weaknesses and competitive threats
12.2.5 AWS
12.2.5.1 Business overview
12.2.5.2 Products/Solutions/Services offered
12.2.5.3 Recent developments
12.2.5.3.1 Product launches and enhancements
12.2.5.4 MnM view
12.2.5.4.1 Key strengths
12.2.5.4.2 Strategic choices
12.2.5.4.3 Weaknesses and competitive threats
12.2.6 ADOBE
12.2.6.1 Business overview
12.2.6.2 Products/Solutions/Services offered
12.2.6.3 Recent developments
12.2.6.3.1 Deals
12.2.7 PEARSON
12.2.7.1 Business overview
12.2.7.2 Products/Solutions/Services offered
12.2.7.3 Recent developments
12.2.7.3.1 Product launches and enhancements
12.2.7.3.2 Deals
12.2.8 BAIDU
12.2.8.1 Business overview
12.2.8.2 Products/Solutions/Services offered
12.2.8.3 Recent developments
12.2.8.3.1 Product launches and enhancements
12.2.9 OPENAI
12.2.9.1 Business overview
12.2.9.2 Products/Solutions/Services offered
12.2.9.3 Recent developments
12.2.9.3.1 Product launches and enhancements
12.2.9.3.2 Deals
12.2.10 DUOLINGO
12.2.10.1 Business overview
12.2.10.2 Products/Solutions/Services offered
12.2.10.3 Recent developments
12.2.10.3.1 Product launches and enhancements
12.2.10.3.2 Deals
12.2.11 CENGAGE GROUP
12.2.12 KNEWTON
12.2.13 SKILLSOFT
12.2.14 UDACITY
12.2.15 STRIDE
12.2.16 HPE
12.2.17 DREAMBOX LEARNING
12.2.18 QUIZLET
12.2.19 GRAMMARLY
12.2.20 VIMEO
12.3 STARTUPS/SMES
12.3.1 RIIID
12.3.2 COGNII
12.3.3 ELSA SPEAK
12.3.4 MEMRISE
12.3.5 ALEF EDUCATION
12.3.6 QUERIUM
12.3.7 AMIRA LEARNING
12.3.8 KNOWRE
12.3.9 CENTURY TECH
12.3.10 THINKSTER MATH
12.3.11 QUIZIZZ
12.3.12 KHAN ACADEMY
12.3.13 SANA LABS
12.3.14 TEACHMINT X
12.3.15 360LEARNING
12.3.16 MAINSTAY
12.3.17 BLIPPAR
12.3.18 BLUE CANOE LEARNING
12.3.19 QUIZLET
12.3.20 OTTER.AI
12.3.21 QUILLBOT
12.3.22 NOLEJ
12.3.23 SPEECHIFY
13 ADJACENT AND RELATED MARKETS
13.1 INTRODUCTION
13.2 ARTIFICIAL INTELLIGENCE (AI) MARKET
13.2.1 MARKET DEFINITION
13.2.2 MARKET OVERVIEW
13.2.2.1 Artificial intelligence market, by offering
13.2.2.2 Artificial intelligence market, by business function
13.2.2.3 Artificial intelligence market, by technology
13.2.2.4 Artificial intelligence market, by vertical
13.2.2.5 Artificial intelligence market, by region
13.3 LEARNING MANAGEMENT SYSTEM MARKET
13.3.1 MARKET DEFINITION
13.3.2 MARKET OVERVIEW
13.3.2.1 LMS market, by offering
13.3.2.2 LMS market, by delivery mode
13.3.2.3 LMS market, by organization size
13.3.2.4 LMS market, by deployment type
13.3.2.5 LMS market, by application
13.3.2.6 LMS market, by user type
13.3.2.7 LMS market, by region
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 CUSTOMIZATION OPTIONS
14.4 RELATED REPORTS
14.5 AUTHOR DETAILS
