Advanced economies push AI policy because productivity gains are visible and immediate
Poorer countries lag as low returns and weak capacity dampen urgency
Education policy can still slow the widening AI divide

Since the emergence of accessible large language models, a clear pattern has developed in the economic dynamics of artificial intelligence. A small group of developed nations now control the critical resources, expertise, and financial rewards associated with AI, while much of the world remains excluded. This disparity has significant consequences. Businesses in leading economies are integrating AI into their operations and products, fundamentally altering value creation. This trend is projected to accelerate productivity gains, increase wages for AI-proficient workers, and drive further market consolidation. Without targeted public policy and educational reform, these effects will likely intensify. The education sector faces a pivotal choice: whether to treat AI as a specialized technical field limited to research and development, or to redesign curricula, training, and institutional incentives to enable broader regions to convert AI access into concrete economic benefits. The resolution of this issue will determine whether the global AI divide becomes a primary driver of inequality in the next decade.

Global AI Divide: The Concentration of Access, Talent, and Capital

A defining feature of AI is its uneven geographic distribution. The development of models, computing infrastructure, and investment capital is concentrated in a small number of countries and cities. This concentration is significant because AI is a general-purpose technology that transforms business structures, task execution, and the valuation of skills. Countries with established technology sectors benefit from efficient pathways that move innovations from research to market. Their universities produce skilled graduates, startups secure funding, and established firms implement AI systems to enhance productivity. In contrast, many lower-income countries encounter substantial barriers, including limited research facilities, insufficient cloud infrastructure, and minimal venture capital. In regions lacking AI capacity, the technology does not redistribute tasks but instead exacerbates the divide between firms that can adopt AI and those that cannot.

Statistical evidence demonstrates that institutions in a select group of countries have developed the leading AI models and attracted the majority of private investment in recent years. This disparity perpetuates a self-reinforcing cycle: success draws additional capital, talent gravitates toward these hubs, and local firms benefit from early AI adoption. Conversely, economies with limited capital and restricted AI research and development experience a negative feedback loop. Lacking local models, computing resources, or skilled labor, they depend on external tools that are often unsuited to their languages, regulatory environments, or economic contexts. This reliance leads to lower immediate returns and persistent challenges in tailoring AI solutions to local development goals.

Education Systems: The Decisive Factor in Narrowing or Widening the Gap

Education is essential to any viable strategy for bridging the global AI divide. It serves as the primary means by which economies transform technological capabilities into increased productivity. However, different approaches to education produce different results. Traditional methods treat AI as a specialized field best left to computer science departments. This perspective overlooks a crucial point. AI is transforming routine tasks in healthcare, agriculture, logistics, and public administration. Its impact is greatest when it is combined with knowledge of these specific areas, rather than when it exists in isolation. To produce economic benefits, education and training initiatives must simultaneously broaden basic digital skills across the workforce, cultivate practical AI knowledge within key industries, and establish pathways for technical experts to convert prototypes into operational systems. Without this comprehensive approach, investments in equipment or short courses will have minimal impact.

There are practical limitations to consider. Many lower-income regions still lack consistent, affordable internet connectivity. While about 90% of people in high-income countries have internet access, this figure is less than a third in the poorest countries. This disparity is a basic reality that education policy must address. Online laboratories or cloud-based curricula cannot be scaled effectively if a large portion of the population lacks reliable access. Furthermore, even where internet access is available, there is often a lack of local support systems, such as instructors with current practical experience, industry partnerships, and accessible datasets. This deficiency makes it more challenging for educational institutions and training centers to offer practical modules that lead to measurable improvements in productivity. Therefore, policymakers should avoid treating digital skills as a universal solution. Instead, they should create detailed, sector-specific programs linked to measurable outcomes.

Practical Policy: Aligning Curriculum with Economic Returns, Not Just Popular Trends

The key policy consideration is whether an intervention will increase the anticipated return on investment for implementing AI in local businesses and public services. If the answer is affirmative, the investment is worthwhile. If not, it will likely waste valuable resources. This principle directs us toward targeted, results-oriented education policies. First, identify industries where AI can produce immediate gains in productivity. For many developing economies, this includes agriculture, logistics, health diagnostics, and the administrative tasks of small and medium-sized enterprises. Second, develop short, modular credentials in collaboration with employers and industry experts, focusing on changes to business processes, data-collection standards, and the implementation of small-scale projects. Third, invest in individuals who can serve as translators. These hybrid practitioners possess knowledge of both the specific industry, such as crop management, and the practical aspects of deploying AI models. These investments offer greater returns than general computer science degrees when resources are limited.

Procurement is another critical yet frequently overlooked factor. Governments, as major purchasers of goods and services, can drive demand for systems that are interoperable, locally adaptable, and auditable. Such demand supports the growth of domestic capabilities. Educational institutions can align curricula with these procurement requirements, ensuring graduates are equipped to meet government standards. This alignment creates direct pathways from training to implementation, reducing the likelihood that skilled individuals migrate to foreign firms or utilize tools unsuited to local needs. This strategy mitigates risk for students and harmonizes the objectives of educational institutions, businesses, and government agencies.

Addressing Common Concerns

Concern 1: Investment is excessively concentrated, rendering it impossible for lower-income countries to catch up. In reality, although capital and computing resources have become highly concentrated, several smaller economies have demonstrated significant progress in the past five years through coordinated public-private initiatives. Targeted investments in data infrastructure, cloud computing, and sector-specific training have enabled these countries to support local firms in developing customized solutions, rather than relying solely on foreign technologies. The objective is not immediate parity, but the cultivation of essential capabilities that yield long-term benefits.

Concern 2: AI will only benefit those with advanced education, increasing inequality. This is a valid concern, but it is not inevitable. Training that focuses on practical tasks, such as using AI-powered decision support for nurses or agricultural specialists, can increase the productivity and wages of mid-skilled workers. The best approach combines brief vocational modules with employer commitments, rather than assuming that only university-level programs are beneficial. The focus should be on those with moderate skills, not just the top performers. When training is linked to measurable productivity improvements in companies or public services, the benefits are more widely distributed.

Concern 3: Local curricula will be ineffective if global AI models are proprietary and controlled by multinational corporations. This is true, but local capability does not require complete ownership of AI models. It requires the ability to adapt and integrate tools, assess their outputs, and govern their responsible use. Public-private partnerships, cloud computing credits, and licensing arrangements can provide access while local institutions develop the skills and governance structures needed to manage and customize systems. The immediate policy priority is to transform access into local value creation. Procurement practices, standards, and training programs are the tools to achieve this.

A Targeted Call to Action

The global AI divide is not inevitable; it is shaped by choices regarding investment, education, and technology acquisition. Education policy can serve as a catalyst, transforming AI from a force for concentration into an instrument for inclusive development. Achieving this requires targeted reforms: prioritizing sectors with demonstrable gains, developing stackable credentials aligned with employer needs, integrating training with infrastructure support, and funding roles that convert prototypes into practical solutions. These measures can mitigate the risk of the AI divide becoming a permanent source of inequality. Inaction will only allow the gap to widen. Immediate, decisive action is required.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

International Telecommunication Union. (2024). Facts and figures 2024: Internet use. ITU.
Stanford Institute for Human-Centered Artificial Intelligence (HAI). (2025). The 2025 AI Index Report. Stanford HAI.
Visual Capitalist. (2025). Visualizing global AI investment by country. Visual Capitalist.
World Economic Forum. (2023). The ‘AI divide’ between the Global North and the Global South. WEF.
Zürich Innovation / Dealroom. (2024). AI Europe report 2024.

Schools are banning AI while workplaces are adopting it, creating a growing skills gap
AI literacy must be taught through teachers and curriculum, not enforced through restrictions on students
The real policy failure is institutional resistance to change, not student misuse of technology

By the close of 2024, a trend emerged: 40% of education systems worldwide had enacted regulations that either limit or completely ban the use of phones in schools. Surprisingly, about 25% of teens in the U.S. admitted to using an AI chatbot to help with school assignments, and more than half said they were using AI-based search engines or chat apps. It is hard to deny the disconnect: as young people are using AI outside of school, schools are banning devices. In the workforce, AI is becoming increasingly widespread. If school systems treat AI and phones the same way they did personal computers and video games in the past, they risk turning out students who can't make effective use of these technologies in most workplaces. Instead of just banning things, there should be investment in teaching about AI in schools. Students and teachers alike will then be able to tell when AI is helpful and when it is giving wrong information. Employers will get workers who can check, verify, and improve what AI produces, so they aren't tricked by it.

Why teaching AI skills in school is a must

There is a reason for all the schools' actions. It makes sense to have bans because of distractions, unfairness, and privacy issues. UNESCO has found that 79 educational systems across the globe have adopted rules on phones, indicating that people are concerned about safety. At the same time, more and more young people are using AI for school between 2023 and 2025. In the United States, about 26% of teens admitted to using an AI chatbot for school in early 2025. Young people outside of school are testing different search engines, ways to produce search results, picture and video tools, along with chat assistants. Such out-of-school influence shapes what they expect and what they are good at. When businesses and teams make plans to use AI on a large scale, studies show that companies are going to implement AI models and use them for daily tasks. Students who have not been taught to double-check what AI gives them will most likely be less able to detect mistakes or get work done.

The timing of this situation is an important consideration. The biggest danger from AI in education is not students finding ways to cheat. It is about students gaining wrong knowledge. When students copy a machine-made answer that is wrong, they pick up wrong facts and start the bad habit of always believing in things that are not checked. We know that AI language models commonly make up information. Studies have indicated that AI tools can fabricate references and provide incorrect information. The good thing is that it can be fixed! Classes should teach how to check; they can learn to use several sources, ask questions, and ensure things align with other resources. These skills can all be taught. Companies need those skills, too. Treating phones and chats as bad is not a sufficient plan. Avoiding the difficult work of giving teachers resources and designing classes means that students won't learn to get the most out of AI and spot its problems.

With teaching AI skills in school, teachers have to take charge and not get left in the dust

The challenge is teacher readiness and not student interest. Research shows that more teachers were using AI in 2023 and 2025. These days, quite a few teachers use AI to assist with plans, alter classwork, and come up with class material. However, training for teachers has been uneven. When teachers use safe AI systems managed by the school, they report saving more time and being better prepared. Otherwise, teachers are fearful and place bans. Consequently, the work is being pushed away from school leaders and onto students.

If leaders want to prepare students for their careers, they have to help adults out as well. Investing in in-service time, having checked training that gets folks to double check is important, and placing secure tools will keep student data safe as folks practice. Not to mention, teachers have to learn how to create prompts that make students need to think deeply, instead of just having a computer write a report, assign work in such a way that the manner someone does things is just as important as the end result, and know how to properly analyze AI detection that is similar. It is important to keep in mind that AI detection is not fully correct at all times. Studies have indicated that AI detection tools are not always reliable. Just punishing folks without a teacher's input will lead to wrongful accusations, break trust, and lead to behavior that makes the work harder rather than the learning better.

Teachers who recognize what AI cannot do have the upper hand. They can show students how to come up with the right questions, match what a machine says with valid resources, test different approaches, and document methods in a log. Businesses really value these thinking skills. Schedules should have time to practice, contemplate, and revise assignments, in helping them to grow. Leaders should do their part as well. Principals and supervisors have to develop practical policies that enable AI to be useful in the classroom, with clearly stated norms, rather than just imposing bans that take the issue out of the school. This is not about letting everything go; rather, it is about teaching AI skills in a practical manner.

When teaching AI skills in schools, you have to improve safety, course design, and how students are evaluated

If schools decide that AI skills are extremely important, how they evaluate students ' needs needs to change. Classic essays make way for people to misuse AI improperly. Rather, tasks have to focus on process and proof: drafts with notes, oral defenses, lab records, code walkthroughs, and research papers with places to add resources and steps taken to come to a conclusion. Point systems must include a review of resources, steps for verification, and explanations for why an AI recommendation was accepted or rejected. When exams must be held under controlled conditions, classwork should include AI-assisted exercises so students can recognize the difference between good content and solid information.

When creating a shift like the one above, fairness should be looked at directly. Not every student is able to get the same devices or AI at home. Platforms offered by schools that care about privacy will make it so that every student has access to things. However, districts have to spend money on them. Teacher learning should include inclusive ways of teaching that ensure people are getting help from AI and that teachers' decision-making abilities are not replaced for students who need extra assistance. With that said, policy must forecast and fix issues. AI detection tools are able to show material that could be used incorrectly, but they aren't always correct. It would be better to have students state they are using AI with assignments, teachers making work so that students have to explain, and holding brief check-ups and casual follow-ups. That mix will reduce people trying to take the easy way out and make learning environments that show how to check and create judgment.

To conclude, governance has immense importance. To begin with, districts should put in place short-term rules that will allow controlled classroom use. On the other hand, they should require training for teachers ahead of time. They should mandate that sellers have privacy guarantees for schools and would rather use tools that have administrative controls. If bans seem like the right thing given the current political climate, leaders can pair them with timelines and pilot programs: briefly pause to collect data, then fund teacher growth and larger classroom pilots. That is a plan that protects students and prepares them for the future.

The point is that while rules do lower risk right away, they won't help folks grow the skill sets needed in the workplace. Instead of only having bans, schools must teach and verify. Schools need to shift from policing devices to truly growing judgment. Achieving this success requires investing in teachers' skill development, designing tests that focus on process rather than just end results, and securing district-level agreements that ensure fair access to verified tools. We can do more than end cheating if students are taught to test statements, check references, and repair mistakes made by AI. In other words, we must produce people who can contribute positively to society and become workers who help improve technology. Without change, the workforce risks misusing AI or failing to work effectively with it. Let's invest in adult abilities and classroom design, so AI skills are taught in schools. Schools must act now: treat AI as a core subject to teach, not just something to keep out, and ensure graduates can confidently confirm and use the resources common in today's workplaces.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

Chelli, M., et al. (2024). Hallucination rates and reference accuracy of ChatGPT and Bard for systematic reviews. Journal of Medical Internet Research.
Gallup (2025). Three in 10 teachers use AI weekly, saving weeks per year. Gallup Education Research Brief.
McKinsey & Company (2024). The state of AI in early 2024: generative AI adoption and value. McKinsey Global Survey Report.
Pew Research Center (2025). About a quarter of U.S. teens have used ChatGPT for schoolwork; usage doubled since 2023. Pew Research Center Short Read.
UNESCO Global Education Monitoring (GEM) team (2025). To ban or not to ban? Monitoring countries’ regulations on smartphone use in school. UNESCO GEM Reports.
Turnitin/AI-detection literature and mixed-method reviews consulted for detection reliability and accuracy discussions (summarized from peer-reviewed evaluations of AI detection tools; representative review: Elkhatat, A.M., et al., 2023).