The US can compete with China in AI education — here’s how

The artificial intelligence (AI) “strategic competition” with China is more intense than ever. To many, the stakes have never been higher — who leads in AI will lead globally.  

At first glance, China appears to be well-positioned to take the lead when it comes to AI talent. China is actively integrating AI into every level of its education system, while the United States has yet to embrace AI education as a strategic priority. This will not do. To maintain its competitive edge, the United States must adopt AI education and workforce policies that are targeted and coordinated. Such policies must also increase AI-specific federal investment and encourage industry partnerships.  

Upon first glance, the state of U.S. AI education appears to be on a positive trajectory. Recent years have seen a proliferation of AI education materials outside the classroom: a rise in online AI education programs at all levels, including K-12 summer camps, “boot camps,” and a range of certificates and industry-academia partnerships. Nearly 300 different organizations now offer AI or computer science summer camps to K-12 students. Other K-12 learning opportunities include after-school programs, competitions and scholarships, including explicit outreach to underrepresented groups in computer science education to address race and gender disparities.    

However, the reach and effectiveness of these piecemeal efforts tell a different story. There are no standardization or quality benchmarks for the maze of online offerings or data on reach. Moreover, outside of a handful of schools, very little AI education is happening in the classroom. Integrating any new education into classrooms is notoriously slow and difficult, and AI education will be no exception. If anything, it faces an even steeper uphill battle as schools across the country are in a constant struggle over competing priorities.  

Meanwhile, China’s rollout and scale of AI education dramatically eclipse U.S. initiatives. While it is too early to assess the effectiveness and quality of China’s AI education programs, our research at Georgetown University’s Center for Security and Emerging Technology (CSET) reveals that China’s Ministry of Education is rapidly implementing AI curricula across all education levels and has even mandated high schools to teach AI coursework since 2018. In Beijing, as well as Zhejiang and Shandong provinces, education authorities have integrated Python into the notoriously difficult Gaokao college entrance exam. 

At the postsecondary level, China’s progress appears even more impressive. In 2019, the Ministry of Education standardized an undergraduate AI major, which today is offered at 345 universities and has been the most popular new major in China. Additionally, our tally indicates at least 34 universities have AI institutes that often train both undergraduate and graduate students, and research areas such as natural language processing, robotics, medical imaging, smart green technology and unmanned systems. The U.S. has a world-class university system, but AI majors in large part remain a specialization of computer science.   

The U.S. education system is not designed to operate like China’s. Nor should it be. There are inherent advantages in a system that allows for a greater degree of educational autonomy. This gives breathing room for experimentation, creativity and innovation among U.S. educational institutions and opens doors for collaboration with the local community, private sector, philanthropic organizations and other relevant stakeholders.  

But for experimental AI education initiatives to be successful, they must be evaluated and scaled inclusively throughout the education system. In this context, the decentralized nature of the U.S. education systems can pose a challenge –– curricula, teacher training and qualifications and learning standards are all fragmented by different state approaches. 

For instance, computer science coursework is currently available at 51 percent of U.S. high schools but unlike in China, is not required in most cases. Initiatives are cropping up in various schools around the country, but a lack of coordination delivering comprehensive awareness, cross-state collaboration and shared assessment metrics hinder these nascent programs from having a nationwide, widespread impact on AI education.  

Implementing competitive AI education across the United States is no easy task — there are no shortcuts and no single solution. There are, however, two elements that education leaders and policymakers should prioritize: coordination and investment. 

 For coordination at the federal level, one path forward is through the White House’s National Artificial Intelligence Initiative Office for Education and Training, which can help coordinate AI education, training and workforce development policy across the country. At the same time, community and state-level engagement to implement, evaluate and scale AI education initiatives are likely to be just as important as federal efforts.  

For example, the Rhode Island Department of Elementary and Secondary Education is leveraging partnerships with private universities and nonprofits to strengthen its K-12 computer science initiative. Results are starting to show promise: There has been a 17-fold increase in advanced placement computer science exams taken since 2016; however, this still represents a small fraction of the overall student body.   

Adequate and diversified investment in AI education is also essential. Federal funding can help close accessibility gaps between states. To that end, Congress can appropriate funding for states to provide public K-12 students with AI experiential learning opportunities and K-12 educators with the required training and support. State and local governments can also fund teacher training initiatives to encourage more educators to become certified in computer science or offer ongoing professional development. Concurrently, funding from nonprofit and private sectors can complement federal, state-level and local investments.   

Ultimately, successful AI education implementation and adoption will be a national endeavor requiring participation from federal, state and local governments, as well as nonprofits, academia and industry. Coordination within the education ecosystem will help to spur ideas and initiatives.  

For those touting U.S. innovation as a competitive strength vis-à-vis China, it should be nothing less.   

Kayla Goode is a research analyst at Georgetown University’s Center for Security and Emerging Technology (CSET), where she works on the CyberAI Project. 

Dahlia Peterson is a research analyst at Georgetown University’s Center for Security and Emerging Technology (CSET). Follow her on Twitter @dahlialpeterson.