SparkCognition is committed to delivering leading-edge AI solutions to improve our clients’ businesses and educating the world on how AI will increasingly shape and enhance our lives in the years to come. But for this to happen, public- and private-sector organizations must join forces to invest broadly and wisely in educational resources that can pay future dividends. Leading by example, SparkCognition CEO Amir Husain and his wife Zaib recently made a significant gift to the University of Texas in support of the school’s groundbreaking Machine Learning Laboratory in Austin, TX.
There is much more work to be done on this front. Let’s take a look at the broader conversation around AI education, particularly as it relates to the race between China and the U.S. for leadership in the AI space.
“The nation needs highly-skilled workers in industry and academia who can contribute to the R&D advances that create the AI of the future. Currently, the United States has an AI talent gap that requires urgent attention.”
This stark assessment from a recent report by the National Artificial Intelligence Initiative Office (NAIIO) lays out in clear terms the challenge faced by the American education system, both public and private. Much ink has been spilled in recent years comparing the approaches to AI education in the U.S. versus those currently taking place in China, our greatest perceived rival in this domain. The short version of this comparison posits that while China is taking an aggressive and centralized approach to implementing AI-centric programs from K-12 through university and graduate school, the U.S., by contrast, is taking a state-by-state approach, as has commonly been done with most other educational challenges throughout our history. There are pros and cons to both approaches, laid out in great detail in a recent report from Georgetown University’s Center for Security and Emerging Technology. There is no shortage of debate concerning the important question of who will likely emerge the victor from this rapidly intensifying competition. The implications of the eventual outcome encompass not only the industrial sector but also our research and development capabilities, national security, and macroeconomic performance.
The challenge
Since its emergence nearly 70 years ago—particularly given the escalating pace of advances in the past 20 years—artificial intelligence has become the foremost educational imperative worldwide, with particular emphasis on competition between the U.S. and China. The goal for both countries is the same: equip the current and future workforce with the skills needed to work as researchers, developers, builders, and maintainers of the AI systems that are currently appearing faster than we can learn about them.
A tale of two educational systems
China
China’s largely centralized system encompasses more than 282M students across 530K educational institutions that span all levels from preschool through graduate school. Most of what takes place in these institutions is controlled by the Ministry of Education, which oversees not only educational agendas but also certifies teachers and administers performance standards and curriculum goals—goals that include the extent to which AI topics are included.
Recently, the Chinese government released several strategic plans that directly address AI education. It is aggressively integrating AI into curricula at the primary level, including courses like Python and AI ethics and safety, while providing access to technology labs. All high schools are, as of 2018, required to provide AI courses. Three hundred forty-five universities were certified at the collegiate level to offer four-year AI engineering degrees as of 2021. In 2020 and 2021, AI was by far the most popular major.
United States
Education in the U.S.—AI and otherwise—is by design largely decentralized. In contrast to China, the U.S. Department of Education provides only high-level guidance on policies and goals. AI has been implemented in numerous schools throughout the country, particularly those with computer science (CS) departments (currently 47% of U.S. public schools). The extent to which AI education has begun making its way into the public school system is a complex interaction between the systems themselves, private entities, and nonprofits like the AI Education Project and the Association for the Advancement of AI. At the collegiate level, most AI education comes in the form of CS majors with AI concentrations, a rapidly growing field. Indeed, as noted in the 2021 AI Index report, the availability of undergraduate and graduate AI courses has grown at a rate of 102.9% and 41.7%, respectively. Doctoral degrees have risen as well and now constitute the largest percentage of Ph.D. degrees awarded.
Implications
Both of the approaches described present numerous advantages and challenges. While China has a nominally more standardized nationwide approach to implementing AI education, it has experienced significant difficulties with teacher qualifications and a stark divide in opportunity between urban and rural students, the latter of which suffer from a considerable lack of resources. The U.S.’s federalized approach suffers from a lack of consistency but benefits from the opportunity to try a wide range of flexible and innovative techniques. This advantage appears to provide greater creativity and the freedom to pursue a broader range of funding and support mechanisms. Also, as with China, widely disparate resource availability limits the consistency of opportunities provided. Furthermore, due to the vibrant private corporate sector, retaining qualified teachers is proving challenging, as many Ph.D. recipients opt for lucrative corporate jobs rather than teaching positions.
Both China and the U.S. take AI education seriously while pursuing their goals in very different ways and with widely differing results. So, which is more likely to win the day, given the common goal of educating as large a portion of the workforce as possible in AI techniques? China’s centralized approach suggests it can deliver a pervasive and consistently educated future workforce if perhaps limited in its capacity for flexibility and innovation. Conversely, the U.S. looks to benefit in the innovation and creativity departments while experiencing an inconsistent range of capabilities from one area of the country to another. While the winning approach is unclear at this point, one thing is indisputable: AI is the technology of the future, and those with the right educational capabilities will emerge as winners in the contests to come.
