AI in Education: Local vs. Global Research

Artificial Intelligence (AI) is transforming education worldwide. But how should research efforts focus - locally or globally? Here's the key takeaway:

• Local research addresses specific community needs, tailoring AI tools to match regional challenges, languages, and educational systems. However, it often struggles with limited resources and scalability.
• Global networks pool resources and expertise to tackle universal challenges, offering scalable solutions. Yet, they can miss the nuances of local contexts.

Quick Facts:

• The AI in education market is projected to grow from $5.18 billion in 2024 to $112.3 billion by 2034.
• In the U.S., 60% of educators use AI tools, while 27% of students regularly use generative AI.
• Local programs excel in relevance to specific needs, while global networks shine in resource sharing and scalability.

The future lies in blending these approaches - leveraging global resources while addressing local realities. Platforms like LongStories.ai highlight this balance, offering globally accessible tools tailored to individual communities. This hybrid strategy could shape the next wave of AI-driven education.

AI in Higher Ed: Global Perspectives & Collaborative Solutions - WISE On Air

1. Local AI Education Research Programs

Local AI education research programs collaborate closely with communities to design AI tools that address specific regional needs. These efforts focus on understanding local challenges and creating solutions that resonate with both learners and educators.

Relevance to Educational Context

These programs emphasize the importance of culturally informed AI solutions. They recognize that successful AI implementation requires more than technical expertise - it demands cultural awareness and active community engagement.

"It is crucial to make learning authentic and contextualize it in the lives and cultures of students so that it becomes meaningful for them. Especially with the task of teaching AI and ethics, … contextualization of the materials and topics used in curriculum help them make sense…" - Eguchi et al., 2021

This approach is especially effective when tackling region-specific challenges. For example, projects like pollution monitoring in Pittsburgh highlight how community-driven research can address local issues while enriching AI education.

Resource Accessibility

One of the biggest hurdles for local programs is limited resources, which can restrict their reach and impact. Professionals involved in these initiatives often cite challenges such as insufficient funding, small research teams, and limited access to advanced technologies.

To navigate these obstacles, local programs rely heavily on community involvement and creative resource management. Many adopt Community Citizen Science (CCS) models, which encourage collaboration among various stakeholders to address pressing local concerns. This approach not only stretches available resources but also strengthens ties between researchers and the community. However, these resource limitations often make it difficult to scale successful projects beyond their initial scope.

Scalability

Scalability remains a significant challenge for local AI education programs. Solutions that work well in one region may not easily adapt to another due to differences in culture, education systems, or technological infrastructure. Additionally, these programs often grapple with limited data and unrealistic assumptions about how their solutions will perform in broader contexts. As a result, while these initiatives excel at addressing localized issues, their broader impact tends to stay geographically confined.

Innovation Potential

Despite these challenges, local programs are a hotbed for innovation. By collaborating directly with teachers, students, and parents, researchers gain valuable insights that lead to highly customized AI solutions. Through co-creation processes, where community members actively participate in designing and testing AI systems, these programs develop creative tools that larger, centralized efforts might overlook.

However, without adequate resources, many promising prototypes remain underdeveloped, limiting their potential impact. Even so, the true strength of local AI education research lies in its ability to create solutions that are deeply relevant to the communities they serve.

2. Global AI Education Research Networks

Global AI education research networks bring together researchers, policymakers, and educators from around the world to advance the use of AI in education. By combining expertise and resources, these networks tackle challenges that cross national borders.

Relevance to Educational Context

While local programs are designed to meet the specific needs of individual communities, global networks have the power to amplify these efforts by sharing resources and knowledge across countries. These networks address diverse educational challenges through coordinated international action. UNESCO plays a key role in these initiatives, helping member states use AI to improve education while promoting values like inclusion and equity.

"UNESCO is committed to supporting Member States to harness the potential of AI technologies for achieving the Education 2030 Agenda, while ensuring that its application in educational contexts is guided by the core principles of inclusion and equity." – UNESCO

In September 2024, WISE joined forces with the Institute of International Education and seven universities spanning Africa, Asia, Europe, North America, and South America. Together, they explored how AI could shape higher education, focusing on strategies to strengthen collaboration between academia and industry. These global efforts are complemented by national initiatives. For instance, China’s national AI strategy promotes "AI + Education" programs to bridge the gap between urban and rural schools. Similarly, the European Union’s Digital Education Action Plan (2021–2027) supports AI and data literacy across its member states.

Resource Accessibility

Global networks help overcome resource limitations by sharing infrastructure and funding. They provide access to major cloud platforms like Amazon AWS, Google Cloud Platform, and Microsoft Azure, as well as NSF-supported high-performance computing centers. Initiatives such as the National Research Platform (NRP) and CENIC AI Resource (CENIC AIR) exemplify how shared resources can be made accessible to a broader audience.

"To facilitate this transformation there is a need for a national-level 'AI Resource' that is open-access, scalable, and grown through the contributions of its user community."

A strong example of resource sharing comes from the California State University system. Campuses like CSU Northridge, CSU San Bernardino, San Diego State University, San Jose State University, and Cal Poly Humboldt utilize CENIC AIR for research and collaboration. This platform not only provides essential tools but also fosters mentorship and networking through events and conferences, connecting underfunded campuses with new opportunities. Shared resources like these lay the groundwork for scalable implementations.

Scalability

Global networks are instrumental in scaling AI initiatives across regions and educational systems. The AI-in-education market is expected to grow to $32.27 billion by 2030, with a compound annual growth rate of 31.2% starting in 2025. North America currently leads the market, holding a 38.0% share in 2024.

Real-world examples illustrate this scalability. In 2019, India’s CBSE, its largest national school board, introduced an AI curriculum for Grades 8–10 in collaboration with industry partners. By 2020, AI became an official elective subject for secondary students, complete with a dedicated syllabus and textbook. The pilot phase alone reached 5,000 students and 1,000 teachers. Similarly, by 2024, over 500 Chinese universities had launched AI majors or degree programs. Initiatives like the "bring your own resources" (BYOR) program under CENIC AIR allow institutions to pool their resources, creating a scalable model for growth.

Innovation Potential

Global networks also serve as hubs for innovation, connecting experts from different parts of the world. For instance, the United Nations University Global AI Network launched the AI for SDGs Global Youth AI Future Innovation Competition 2025 during the World Artificial Intelligence Conference in Shanghai. This competition challenges young innovators to create AI solutions for environmental issues and to support underdeveloped regions.

"This is the first time we've developed a multi-partner research consortium of this nature, enabling us to explore AI across different world regions and perspectives." – Stavros Yiannouka, CEO of WISE

Such initiatives help reduce global educational disparities. AI has the potential to contribute to achieving 79% of the Sustainable Development Goals, with its impact expected to reach over 8.5 billion people by 2030.

"Artificial intelligence presents a rare opportunity to bridge longstanding gaps in higher education outcomes across Africa. It will enable personalized learning experiences, augment teaching capabilities, and democratize access to quality education." – Joseph Adjei, Associate Professor of Information Systems, Ashesi University, Ghana

In addition, partnerships like Internet2’s collaborations across the US, Asia-Pacific, and Oceania enable advanced research in fields ranging from high-energy physics to biomedical engineering. Through secure, high-speed connections, innovations from one region can be quickly shared and adapted worldwide.

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Pros and Cons

When it comes to AI education research, local and global programs each bring their own strengths and challenges to the table. As the AI education sector continues to grow, understanding these differences is crucial for making smart decisions about implementation.

Aspect	Local AI Research Programs	Global AI Research Networks
Relevance to Context	High, tailored to local needs	Moderate, may lack localization
Resource Accessibility	Limited by regional funding	Greater access through partnerships
Scalability	Limited to local infrastructure	High scalability across regions
Innovation Potential	Highly focused on specific challenges	Driven by diverse global insights

Let’s break down these comparisons further.

Local programs shine when it comes to addressing specific needs tied to language, cultural differences, economic gaps, and regional tech infrastructure. By focusing on these factors, they help ensure that AI systems don’t unintentionally reinforce biases or make decisions that conflict with community values. For example, an AI tutoring system designed for American students might struggle to accommodate the cultural references or unique learning styles found in rural areas or among minority groups.

On the other hand, global networks excel in resource accessibility and scalability. As Erik Larson, Chief Technology Officer at Terra Dotta, puts it:

"International education pros need more support, and AI has shown to be a valuable way to address resource challenges."

However, a global approach isn’t without its downsides. A one-size-fits-all model often overlooks key local needs, and issues like ethics, equitable access, and privacy may not be uniformly addressed across all regions.

When it comes to innovation, local programs tend to focus deeply on solving specific regional problems, while global networks draw on a wide range of perspectives and ideas. This diversity can lead to solutions that work across multiple contexts, potentially impacting a broader audience.

Bill Gates captures the potential of combining these approaches perfectly:

"One of the things that excites me the most about this type of technology is the possibility of localizing it to every student, no matter where they live."

This highlights the idea that the most effective AI education strategies might blend global resources with local customization. The choice between these approaches often depends on priorities and resources. For regions with strong local expertise and funding, targeted local programs may be the way to go. Meanwhile, areas with fewer resources might benefit more from global networks.

Platforms like LongStories.ai illustrate how AI can bridge this gap. By offering personalized, culturally adaptable content, they show how global accessibility can align with local needs. This balance of global and local strengths could pave the way for more impactful AI-driven education solutions.

Conclusion

Looking at both local and global efforts, the path forward seems clear: combining their best aspects. Research in artificial intelligence (AI) shows that hybrid models - those blending global resources with local expertise - are shaping the future.

For instance, education-focused AI is projected to grow into a $6 billion market by 2025. A staggering 83% of higher education leaders believe generative AI will significantly transform education within three to five years, and 65% feel its advantages outweigh its challenges.

Real-world examples underline this approach. The Togo Data Lab, a partnership between Togo's Ministry of Digital Economy and Transformation and the University of California, Berkeley, demonstrates how global knowledge can empower local initiatives. Similarly, the African Women in AI and Tech program - organized by UNESCO, the International Artificial Intelligence Center of Morocco, and the OCP Foundation - shows the impact of global-local collaboration in addressing specific regional needs.

The emergence of smaller, specialized AI systems also reflects the need for solutions that balance global advancements with local relevance. Take Coursera, for example: generative AI allowed them to scale course translations from 200 per year to over 2,000 in just a single quarter. This dramatic increase highlights how global technology can be tailored to serve diverse audiences effectively.

To make such solutions sustainable, organizations must focus on nurturing local talent, building internal expertise, and engaging communities. This ensures that AI systems remain grounded in local realities while drawing on global insights.

Bill Gates summed up the current moment well when he said:

"This is an exciting and confusing time, and if you haven't figured out how to make the best use of AI yet, you are not alone".

Platforms like LongStories.ai are a great example of this hybrid approach. They offer globally accessible tools that adapt to local contexts, proving that the most effective AI solutions think globally but act locally.

The future of AI in education will thrive on this blend of international collaboration and local innovation, creating tools that are both scalable and meaningful.

FAQs

How can local AI education programs address challenges like limited resources and scalability?

Local AI education programs can address resource constraints and scalability issues by leveraging cloud computing and employing AI techniques such as model pruning and quantization. These methods help streamline operations by lowering computational demands, making sophisticated AI tools more accessible to a broader audience.

Moreover, incorporating AI into current educational systems and utilizing scalable frameworks for interactive, hands-on learning can significantly broaden access without straining available resources. These approaches enable local programs to provide meaningful AI education while keeping costs under control.

What challenges can arise from relying only on global AI education research networks?

Relying solely on global AI education research networks can bring about some notable challenges. These networks often fail to account for the local cultural, social, and ethical contexts that shape individual communities. As a result, the solutions they propose might not align well with the unique needs of specific regions, leading to outcomes that feel disconnected or less effective for students and educators.

Another concern is that an overreliance on global frameworks can stifle critical thinking and independent problem-solving. When students and educators lean too heavily on AI tools, they risk missing out on the chance to develop key skills that are crucial for long-term success - not just in education, but in life. The real key lies in striking a balance: blending global insights with local perspectives to create educational approaches that are both relevant and impactful.

How can combining global resources with local needs improve AI-driven education?

A hybrid approach brings together global AI developments and local educational goals to craft a well-rounded and effective learning environment. By customizing AI tools to meet the unique needs of specific communities, while drawing on international expertise, this method ensures education remains both inclusive and tailored.

Blending AI-powered insights with local understanding enables educators to design solutions that scale effectively while respecting cultural differences. This strategy addresses diverse student needs, encourages new ideas, and connects cutting-edge technology with practical, localized applications - making education more meaningful and accessible for all.