Survey and Analysis of the Current Development Status of the AI Education Industry
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What is the current state of the AI education industry? AI education refers to a multi-level artificial intelligence education system for the general public, including the introduction of AI-related courses in primary and secondary schools. Many regions have begun exploring AI education in compulsory education, but face challenges such as lack of smart equipment support and top-level design involving local education administrations and research departments. Collaborative efforts from government, industry, academia, and research are expected to drive AI education toward a more systematic and scientific direction.
AI education stocks gained momentum during trading, with Jiafa Education rising nearly 10%, Chuanzhi Education and Century Tianhong up over 6%, followed by Guoxin Culture, Kede Education, and Rongxin Culture. Recently, NetEase Youdao's "Ziyue," the first vertical large-scale model in China's education sector, passed relevant regulatory filings. This model and its applications will be open to the public, with new products set to launch soon. The model will be integrated into a wider range of smart hardware and apps, providing efficient learning experiences for learners of all ages.
"AI + Education" refers to the deep integration and development of artificial intelligence and education, leveraging AI applications in educational settings to promote equity, improve quality, and achieve personalized learning. Specifically, "AI + Education" encompasses technologies, models, and practices of AI innovation in education, categorized into "computational intelligence + education," "perceptual intelligence + education," and "cognitive intelligence + education." AI in education is evolving from "storage and computation" to "listening, speaking, and recognition," ultimately aiming for "understanding and thinking."
From the perspective of teaching activities, current educational scenarios can be divided into teaching, learning, management, and assessment. "Teaching" and "management" primarily involve educators. Teaching includes tasks like research, lesson preparation, instruction, Q&A, exam creation, and grading, with a core need to reduce workload and enable precise teaching. Management involves administrative tasks such as staff recruitment, supervision, admissions, scheduling, and campus development, requiring efficiency and scientific decision-making. "Learning" and "assessment" focus on students. Learning includes activities like previewing, attending lectures, reading, homework, revision, exams, and internships, with a core need for adaptive and personalized learning. Assessment involves large-scale standardized testing, where automation is needed to handle the labor-intensive grading process while ensuring accuracy.
Survey and Analysis of the Current Development Status of the AI Education Industry
From 2000 to 2020, global AI journal publications increased by approximately 3.5 times. Since 2017, China has led in AI journal publications, accounting for 18.0% in 2020, followed by the U.S. (12.3%) and the EU (8.6%). According to Microsoft Academic Graph 2020 data, China (20.7%) surpassed the U.S. (19.8%) in AI journal citations for the first time.
As of June 30, 2021, there were 60 AI education companies globally with funding exceeding $100 million, primarily in China, the U.S., and India. European AI education firms received relatively smaller funding, while Chinese companies attracted more capital. India has seen the rise of several large AI education firms in recent years.
U.S. and Indian AI education companies focus more on competency assessment, with Indian firms also emphasizing personal career planning. Chinese companies prioritize technological applications in teaching, with less focus on assessment. Applications are shifting from replacing repetitive teacher tasks to proactively addressing learners' needs, emphasizing precision and personalization. Scenarios like personalized teaching, smart classrooms, adaptive learning, and intelligent career planning are becoming more widespread.
In China, 46% of AI education companies are in Series C, D, or E funding rounds, with 23% in Series F or later. Nearly 70% of Chinese AI education firms have undergone at least three funding rounds, indicating a mature industry. However, 31% are in Series A or B, reflecting ongoing competition and entrepreneurial interest.
China has 29 AI education companies with funding exceeding $100 million, led by TAL Education Group at $3.8 billion. Chinese AI education firms generally secure higher funding than their global counterparts, showcasing strong investor interest and government support for educational reform and informatization.
Over 90% of Chinese AI education companies were founded in the past decade, driven by policy shifts. In 2014, information technology was included in college entrance exams, and in 2015, the "Guidelines for Deepening Education Informatization During the 13th Five-Year Plan" were issued. Subsequent policies, such as the "Education Informatization 13th Five-Year Plan" and "Education Informatization 2.0 Action Plan," spurred industry growth. However, recent market saturation and stricter regulations have slowed the pace of new entrants.
The State Council's "New Generation AI Development Plan" emphasizes using AI to reform education models and teaching methods. The Ministry of Education's "AI Innovation Action Plan for Higher Education" and pilot programs for AI-assisted teacher development further support this. Joint initiatives by eight departments, including the Cyberspace Administration of China, have identified 19 smart education governance bases. The Ministry of Science and Technology and five other departments included smart education in the first batch of AI demonstration scenarios, promoting replicable and scalable experiences. "AI + Education" continues to spark innovation, injecting strong momentum into educational transformation.
Future Trends in the AI Education Industry
Looking ahead, collaboration is needed to build a high-quality, human-centered AI education ecosystem. Key challenges include enhancing human-machine collaboration and dialogue. Technology should serve education, supporting teaching while preserving the teacher's role in imparting values. Meanwhile, educators must understand and utilize AI to improve information application skills, enabling better AI-assisted teaching.