Context

Given the increasing importance of Artificial Intelligence in education sector, it is important to have a comparative analysis of China and India’s higher education reforms for AI preparedness and research.

Background

• India, an emerging economy, is striving to build itself into a knowledge economy so it can compete in the global market and pursue sustainable socio-economic growth and development.
• In July 2015, the government launched the Skill India Mission in line with Prime Minister Narendra Modi’s vision of India as “the world’s human resource capital.”
• AI has assumed a pivotal role on this front, with the government think tank, NITI Aayog underlining India’s emergence as an “AI Garage” (or “solutions provider”) as a strategy for leadership in AI.
• AI and data could contribute about US$500 billion to India’s GDP by 2025, with AI poised to add a further US$957 billion to the country’s GDP by 2035.
• Similarly, China unveiled its ‘New Generation of Artificial Intelligence Development Plan’ in 2017, which outlines the country’s pathway to becoming the world’s leading power in artificial intelligence (AI) by 2030.
• Broadly, AI entails human-like capabilities of machines or programmes in “perception, cognition, decision making and implementation.”
• Machine learning is a subset of AI, with examples of AI technologies including natural language processing and computer vision.

Analysis

Why the comparison?

China and India lend themselves for comparison due to several reasons.

• First, China and India have the world’s largest and second-largest higher education systems, respectively.
• Second, both are among the largest developing countries in the world, with China the second largest (US$14.1 trillion) and India the fifth (US$2.9 trillion).
• Third, mainland China has the second-largest number of AI companies in the world (1,011 as of June 2018), specialising in voice, vision and natural language processing, and India is swiftly catching up, with the fifth-most number of companies and AI jobs globally.

Comparing AI Development Plans and Higher Education Strategies

The case of India

• NITI Aayog’s discussion paper: In June 2018, NITI Aayog released a discussion paper on the ‘National Strategy for Artificial Intelligence,’ which incorporates education (including higher education) among sectors of focus such as agriculture and healthcare.
  • It underlined the “incremental value” of AI in reforming India’s education sector in terms of quality and access.
  • Further, it identified preparing a new generation to harness the global AI revolution as a focus area for NITI Aayog.
• AI in NEP: India’s National Education Policy (NEP) 2020, released in July 2020, provides that all universities offer doctorate and masters programmes in core areas such as machine learning and in multidisciplinary fields (“AI” + “X”).
  • The NEP also includes provisions for setting up a National Educational Alliance for Technology “to enhance learning, assessment, planning, [and] administration” at schools and higher education institutions.

China’s plans

• 2017 AI Development plan: China’s 2017 AI development plan, which predates India’s own discussion paper, also highlights ‘intelligent education’ as a segment of AI application to provide a learner-centric environment.
  • However, it is distinct in its emphasis on a connection between AI talent and the country’s education system.
  • This shows that China is taking proactive action by moving from a generic AI plan to a detailed action plan focused on post-secondary education.
• Artificial Intelligence Innovation Action Plan for Institutions of Higher Education: In 2018, China launched the Artificial Intelligence Innovation Action Plan for Institutions of Higher Education, which stipulates that by 2030, “colleges and universities will become the main force behind building the world’s main AI innovation centres and will lead the development of a new generation AI talent pool to provide China with the scientific and technological support and guaranteed talent to put it at the forefront of innovation-oriented countries.”

The difference

• The difference is evident in India’s and China’s core purposes as articulated in their policy documents.

India	China
India’s AI plan focuses on “social and inclusive growth,” and its NEP 2020 has a perfunctory reference to the country’s potential leadership role in the emerging fields employing AI and machine learning.	In contrast, China’s AI development plan is fiercely competitive, imbued with a fervour in building the country’s “first-mover advantage.” While China has an official blueprint dedicated to AI for post-secondary institutions, India does not have an exclusive action plan to revamp higher education institutions for AI readiness.
Also, the Indian policy document (2018) provides a broad-brush direction for course upgrades and training in AI.	Whereas China’s 2018 action plan carries AI training-specific targets for 2020.
India’s new education policy is inward-oriented, prioritising “institutional restructuring and consolidation” and a “more holistic education” that is mindful of multi-faceted human capacities.	Clearly, higher education institutions have been accorded top priority in China’s blueprint to win the race to global leadership.

AI as an Academic Discipline

• India
• Various Indian universities have begun offering undergraduate degrees in AI or computer science and engineering with a specialisation in AI and machine learning.
  • The Indian Institute of Technology (IIT) in Hyderabad was India’s first educational institution to offer a “full-fledged” four-year degree in AI in the 2019-20 academic year.
  • IIT Delhi has also set up a School of Artificial Intelligence to offer PhD courses starting January 2021, with postgraduate degree courses in the pipeline.
  • Further, the IITs have also partnered with Massive Open Online Courses (MOOC) platforms to offer courses on AI.
    • For instance, IIT Roorkee and Coursera offer six-month certificate programmes in AI, machine learning and data science through diverse instructional methods such as video lectures, hands-on learning opportunities, and team projects.
• China
• Meanwhile, in March 2019, China’s Ministry of Education approved the introduction of an AI major in 35 universities, including the Beijing University of Aeronautics and Astronautics, Shanghai Jiao Tong University, and Zhejiang University.
• As of May 2019, 479 universities in China, accounting for nearly 40 percent of the country’s universities, were offering big data-related majors.

It is premature, however, to compare the size of AI degrees programmes in both countries since it is not yet clear whether application approval has resulted in student intake in Chinese institutions.

Automation Readiness Index

• The Economist Intelligence Unit’s Automation Readiness Index ranks 25 countries for their preparedness for “intelligent automation” based on their innovation environment and labour market and education policies.
• India: India is placed at 18, with its policy environment readiness for intelligent automation rated as ’emerging’.
  • It ranks marginally better in the labour market and innovation environment categories, at 16th and 17th place, respectively.  
  • However, in the education policy category, India ranks 22nd of 25 countries.
• China: At the same time, China is the 12th most automation-ready country on the index.
  • In the education policy category, it ranks higher than India at 14th place. This difference is attributable to the “21st century skills [such as critical thinking and creativity] and knowledge” component of the education category, where India ranks 22nd and China ranks 11th.
  • Further, China’s position is better in compulsory education and early childhood policies, while India’s score is better than that of China in post-compulsory education (for instance, in science, technology, engineering and mathematics, or STEM fields).
• South Korea is at the top spot in the education category due to measures such as soft skills advancement, fostering science and technology talent, and promoting lifelong education.

Talent Retention

• Of the international AI talent pool, the US ranks first with 28,536 AI talents, followed by China with 18,232, and India with 17,384 AI talents; the numbers are based on researchers’ issued patents and/or published English papers.
• However, when it comes to the top AI talent based on H-index, the developed world has the highest share.
• Globally, universities account for 72 percent of international AI talents,]and China is home to several universities that have a high proportion of international AI talents, with “Tsinghua University having the greatest number of international AI talents” (822) and “Shanghai Jiao Tong University in second place with 590.”
• India’s Vellore Institute of Technology is in third place.
• However, no Chinese or Indian university made it to the top ten list “by the number of top international AI talent.”

Issues and Challenges (Indian Context)

While AI has great potential in the education space, India is a vast county, and the challenges that are thrown are unique and cannot be compared with other nations

• Lack of reliable high data: While the government has taken steps and initiative to collect data across higher education institutions, there is still a huge gap that exists between the ‘actual’ data and the data that is pulled by information systems.
• Lack of data at the district state and regional level: Many India educational still don’t have internal data that is available to the grassroots level. This is another challenge for implementing AI and ML, which can bring great insights and can help the teaching learning process.
• The digital divide: In-spite of mobile penetration at one end of the spectrum, there are schools that still lack basic facilities. This is again a challenge to implement AI and ML across all educational institutions in India.

Wrapping up

Higher-education reforms are underway in India to foster AI talent, for example, by widening the incorporation of AI as an academic discipline.  Meanwhile, China has been assailed by foreign observers for blunting its citizens’ intellect through political indoctrination and “ideational regimentation. Any comparison of India and China in AI boils down to China’s lead in quantitative metrics. India has many milestones to achieve if it is to catch up with China. A clear action plan for talent formation (especially given India’s so-called demographic dividend potential) and research output must be outlined. Future research may investigate faculty growth, enrolment in MOOCs, and AI startups to understand and compare trends in both countries.