The United States can compete with China in teaching AI – here’s how
The “strategic competition” of artificial intelligence (AI) with China is more intense than ever. For many, the stakes have never been higher – who runs the AI will run the world.
At first glance, China appears to be well positioned to take the lead in AI talent. China is actively integrating AI into all levels of its education system, while the United States has yet to make AI a strategic priority. That won’t be enough. To maintain its competitive edge, the United States must adopt targeted and coordinated AI education and workforce policies. Such policies should also increase federal investments specific to AI and encourage partnerships with industry.
At first glance, the state of AI education in the United States appears to be on a positive trajectory. Recent years have seen a proliferation of AI educational materials outside of the classroom: an increase in online AI education programs at all levels, including kindergarten summer camps through grade 12, “boot camps” and a range of certificates and industry-university partnerships. Nearly 300 different organizations now offer AI or computer science summer camps for K-12 students. Other K-12 learning opportunities include after-school programs, competitions, and scholarships, including explicitly reaching out to underrepresented groups in computer science education to address racial disparities. and sexual.
However, the scope and effectiveness of these piecemeal efforts tell a different story. There are no standardization or quality standards for the maze of online offerings or reach data. Additionally, outside of a handful of schools, very little AI education takes place in the classroom. Bringing any new education into classrooms is notoriously slow and difficult, and AI education will be no exception. Rather, he faces an even tougher battle as schools across the country are in a constant struggle over competing priorities.
Meanwhile, the deployment and scale of AI education in China drastically overshadows US initiatives. Although it is too early to assess the effectiveness and quality of AI education programs in China, our research at the Center for Security and Emerging Technologies (CSET) at Georgetown University reveals that the China’s Ministry of Education is rapidly implementing AI curricula at all levels of education and has even mandated secondary schools to teach AI courses since 2018. In Beijing, as well as in the provinces of Zhejiang and Shandong, education authorities have integrated Python into the notoriously difficult Gaokao middle school entrance exam.
At the post-secondary level, China’s progress looks even more impressive. In 2019, the Ministry of Education standardized an undergraduate AI major, which is now offered at 345 universities and has been the the most popular new major in China. Additionally, our tally indicates that at least 34 universities have AI institutes that often train undergraduate and graduate students, and research areas such as natural language processing, robotics, imaging medical, smart green technology and unmanned systems. The United States has a world-class university system, but AI majors remain largely a computer science major.
The American education system is not designed to work like China’s. Nor should it be. There are inherent advantages to a system that allows for greater educational autonomy. It ignites experimentation, creativity, and innovation among American 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 succeed, they need to be evaluated and scaled inclusively across the education system. In this context, the decentralized nature of US education systems can pose a challenge –– curricula, teacher training and qualifications, and learning standards are all fragmented by different state approaches.
For example, computer science courses are currently available in 51% of US high schools, but unlike China, they are not mandatory in most cases. Initiatives are springing up in various schools across the country, but a lack of coordination providing comprehensive outreach, cross-state collaboration, and shared assessment measures prevents these fledgling programs from having a national and widespread impact on education at large. the AI.
Implementing competitive AI training in the United States is no easy task – there are no shortcuts or one-size-fits-all solution. There are, however, two elements that education managers and policy makers should prioritize: coordination and investment.
For coordination at the federal level, one route is through the White House National Artificial Intelligence Initiative Office for Education and Training, which can help coordinate education, training and AI workforce development policy across the country. At the same time, commitment at the community and state level to implement, evaluate, and scale AI education initiatives will likely be just as important as federal efforts.
For example, the Rhode Island Department of Elementary and Secondary Education relies on partnerships with private universities and nonprofit organizations to strengthen its K-12 computer science initiative. The results are starting to look promising: there has been a 17-fold increase in advanced computer science exams taken since 2016; however, this still represents a small fraction of the entire student body.
Adequate and diverse investments in AI education are also essential. Federal funding can help close accessibility gaps between states. To that end, Congress can appropriate funds to states to provide experiential AI learning opportunities for K-12 public students and for K-12 educators the necessary training and support. State and local governments can also fund teacher training initiatives to encourage more educators to pursue computer science certification or provide continuing professional development. At the same time, funding from the nonprofit and private sectors can complement federal, state, and local investments.
Ultimately, the successful implementation and adoption of AI education will be a national endeavor requiring the participation of federal, state, and local governments, as well as nonprofits, universities, and industry. Coordination within the education ecosystem will help stimulate ideas and initiatives.
For those who tout American innovation as a competitive force vis-à-vis China, it should be nothing less.
Kayla Good is a research analyst at the Center for Security and Emerging Technologies (CSET) at Georgetown University, where she works on the CyberAI project.
Dahlia Peterson is a research analyst at the Center for Security and Emerging Technologies (CSET) at Georgetown University. Follow her on Twitter @dahlialpeterson.