NLP in Education Global Market Report 2023: Growing Adoption of NLP to Assess Student Performance Presents Opportunities

DUBLIN, May 31, 2023 /PRNewswire/ -- The "NLP in Education Market by Offering (Solutions and Services), Model Type (Rule-based, Statistical, and Hybrid), Application (Sentiment Analysis & Data Extraction, Intelligent Tutoring & Language Learning), End User and Region - Global Forecast to 2028" report has been added to  ResearchAndMarkets.com's offering.

The global NLP in education market size is projected to grow from USD 99 million in 2023 to USD 228 million by 2028, at a Compound Annual Growth Rate (CAGR) of 18.1%.

With the advent of predictive analytics and machine language, organizations can amplify their outcomes with predictive models based on customer data, such as purchasing patterns, support feedback, and social media sentiment. Enterprises across the globe are deploying NLP in education solutions with predictive analytic capabilities for multiple use cases, such as sentiment analysis, email spam detection, and text mining.

Image-based NLP solutions segment to grow at highest CAGR during forecast period

Based on solutions, the NLP in education market is further subsegmented into text-based NLP solutions, audio-based, image-based, and video-based. Among these subsegments, the image-based NLP solutions sub-segment is expected to grow at the highest CAGR during the forecast period. This growth is attributed to the rise in demand for optical character recognition (OCR) tools, image captioning tools, and image recognition tools in the education industry. Image-based NLP solutions have the potential to significantly enhance the learning experience for students and improve the efficiency and effectiveness of education delivery.

Hybrid NLP model to grow at highest CAGR during forecast period.

Based on model type, the NLP in education market is classified into Rule-based, Statistical, and Hybrid. Among these, the hybrid NLP model is expected to grow at the highest growth rate during 2023-2028. The use of a hybrid NLP model can help improve the quality of education, increase student engagement, and reduce teachers' workload, leading to a more efficient and effective education system. Major advantages of the hybrid NLP model are improved student engagement, personalized learning, automated grading and feedback, language translation, and improved accessibility.

North America to record highest market share

North America is home to advanced economies like the US and Canada. These nations were among the first to use Al. The United States and Canada are ranked fourth and sixth globally for government Al preparation in Oxford's 2019 Government Artificial Intelligence Readiness Index, making North America a very competitive Al region. Al has been designated as a national policy priority in both nations. Companies in this region have used AI, ML, and deep learning technology as part of ongoing business processes to remain ahead of the competition, particularly in the US. Al-based technologies in customer support, whether used or not, provide firms that primarily provide call center services with a considerable cash gain.

Market Dynamics

Drivers

• Rising Demand for Efficient Language Learning
• Increasing Adoption of Predictive Analytics to Provide Targeted Support to Students at Risks
• Evolution of AI and Deep Learning

Restraints

• Limited Availability of Educational Data Impacts Efficiency of NLP Models
• Risks Associated with Data Breaches and Sensitive Information Leakage

Opportunities

• Growing Adoption of NLP to Assess Student Performance
• Soaring Demand for Personalized Learning
• Significant R&D Activities by Market Vendors

Challenges

• Developing Customized NLP Solutions
• Regulatory and Privacy Concerns Over Data Security

The report provides insights on the following pointers:

• Analysis of key drivers (rising demand for efficient language learning, greater urge for predictive analytics to reduce risks and identify growth opportunities, and evolution of AI), restraints (limited data availability and risks associated with data breaches and sensitive information leakage), opportunities (increase in adoption of learning analytics and surge in the development of big data technology for actionable business intelligence), and challenges (increasing requirement for customization and regulatory and privacy concerns over data security) influencing the growth of the NLP in education market.
• Product Development/Innovation: Detailed insights on coming technologies, R&D activities, and product & service launches in the NLP in education market.
• Market Development: Comprehensive information about lucrative markets - the report analyses the NLP in education market across varied regions.
• Market Diversification: Exhaustive information about products & services, untapped geographies, recent developments, and investments in the NLP in education market.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights

5 Market Overview and Industry Trends

6 NLP in Education Market, by Offering
6.1 Introduction
6.2 Solutions
6.2.1 Text-based NLP Solution
6.2.2 Audio-based NLP Solution
6.2.3 Image-based NLP Solution
6.2.4 Video-based NLP Solution
6.3 Services
6.3.1 Professional Services
6.3.2 Managed Services

7 NLP in Education Market, by Model Type
7.1 Introduction
7.2 Statistical NLP
7.2.1 Suitable Approach to Generate Results with Large Datasets
7.3 Hybrid NLP
7.3.1 Ideal in Absence of Proper Datasets
7.4 Rule-based NLP
7.4.1 Widely Used for Tokenization

8 NLP in Education Market, by End-user
8.1 Introduction
8.2 Academic User
8.2.1 Higher Education
8.2.1.1 NLP Solutions Used to Identify Areas That Need Improvement
8.2.2 K12
8.2.2.1 Governments Worldwide Encouraging Adoption of NLP Solutions to Improve Quality of Education at K12 Level
8.3 Edtech Provider
8.3.1 Learning Management System Providers
8.3.1.1 Improve Learning Services Through NLP-based Offerings
8.3.2 Virtual Classroom Providers
8.3.2.1 Use NLP to Offer Customized Learning Materials
8.3.3 E-Learning Platform Providers
8.3.3.1 Create and Curate Content in Multiple Languages with NLP
8.3.4 Learning Content Providers
8.3.4.1 Use NLP for Content Analysis and Personalization
8.3.5 Mobile Learning Providers
8.3.5.1 Use NLP to Develop More Engaging, Personalized, and Accessible Mobile Learning Platforms
8.3.6 Skill Development Platform Providers
8.3.6.1 Improve Efficiency and Effectiveness of Learning Platforms with NLP

9 NLP in Education Market, by Application
9.1 Introduction
9.2 Sentiment Analysis & Data Extraction
9.2.1 Student Behavior and Feedback Analysis
9.2.1.1 Need to Determine Overall Emotional Tone of Content
9.2.2 Social Media Analysis
9.2.2.1 Need to Leverage Social Media Data to Enhance Educational Experience for Students
9.2.3 Course Material Analysis
9.2.3.1 Helps Identify Areas of Potential Confusion or Difficulty for Students
9.2.4 Admissions Support & Analysis
9.2.4.1 Assist in Identifying Promising Candidates
9.2.5 Student Performance Analysis
9.2.5.1 Data Extraction Helps Analyze Student Performance Data
9.2.6 Research Analysis
9.2.6.1 Used to Analyze Research Articles, Papers, and Publications
9.3 Content Management & Automatic Summarization
9.3.1 Textbook Summarization
9.3.1.1 Helps to Identify Key Concepts and Summarize Content of Textbooks or Study Materials
9.3.2 Lecture Summarization
9.3.2.1 Helps Educators Identify Areas of Weakness and Improve Teaching Methods
9.3.3 Research Paper Summarization
9.3.3.1 Helps Extract Relevant Information from Lengthy Research Papers
9.3.4 Content/Essay Summarization
9.3.4.1 Helps Teachers Assess Content of Multiple Essays
9.3.5 Text Mining
9.3.5.1 Helps Mine Relevant Information from Large Sets of Educational Content
9.3.6 Question Answering
9.3.6.1 Aids Students to Get Answers to Questions in Real Time
9.3.7 Machine Translation
9.3.7.1 Makes Educational Content Accessible to Students Worldwide
9.4 Intelligent Tutoring & Language Learning
9.4.1 Adaptive Learning
9.4.1.1 Helps Analyze Learners' Performance on Tasks
9.4.2 Dialogue-based Tutoring
9.4.2.1 Supports Natural Language Interactions Between Learners and Systems
9.4.3 Intelligent Writing Assistance
9.4.3.1 Helps Learners Improve Writing Skills
9.4.4 Language Modeling
9.4.4.1 Assists Teachers in Creating Customized Learning Materials
9.4.5 Language Assessment
9.4.5.1 Used to Evaluate Language Proficiency Levels of Learners
9.4.6 Personalized Learning
9.4.6.1 Analyzes Student Data to Identify Strengths and Weaknesses
9.5 Risk & Threat Detection
9.5.1 Student Safety
9.5.1.1 Helps Identify Potential Threats to Student Safety
9.5.2 Cybersecurity
9.5.2.1 Used to Detect Potential Cyber Threats in Educational Institutions
9.5.3 Campus Security
9.5.3.1 Helps Detect Unusual Behavior or Suspicious Activities on Campuses
9.5.4 Admissions Fraud Detection
9.5.4.1 Helps Detect Fraud in Admission Process
9.6 Corporate Training
9.6.1 Language Training for International Business
9.6.1.1 Creates Customized Language Training Programs for International Business Professionals
9.6.2 Soft Skills Training
9.6.2.1 Helps Students Improve Communication, Writing, and Presentation Skills
9.6.3 Compliance Training
9.6.3.1 Compliance Training Made More Engaging and Effective with NLP
9.6.4 Customer Service Training
9.6.4.1 NLP-based Training Programs Help Employees Improve Communication Skills
9.7 Other Applications

10 Market, by Region

11 Competitive Landscape

12 Company Profiles

13 Adjacent/Related Markets

14 Appendix

Companies Mentioned

• Abbyy
• Amazon Web Services (Aws)
• Attivio
• Automated Insights
• Avaamo
• Aylien
• Baidu
• Carnegie Learning
• Chivox
• Cognii
• Conversica
• Gecko
• Google
• Gupshup
• Haptik
• IBM
• Inbenta
• Ivy.AI
• Microsoft
• Nice
• Primer.AI
• Revolve.AI
• Sas Institute
• Sensforth.AI
• Welocalize
• Width.AI
• Yellow.AI

For more information about this report visit https://www.researchandmarkets.com/r/c6zchs

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