Since its popularization, Artificial Intelligence has been having deep impacts in the cultures where it has been made accessible (Ge et al, 2024). Furthermore, it has been altering fundamentally both the education and work lives of people across the globe. This essay discusses some of the implications of widespread adoption of AI to perform educational and work tasks, focusing on providing a critical perspective about the challenges and opportunities this tool can provide.
AI means different things for different cultures
A research performed by Ge et al. (2024) proposes that different sociocultural contexts will have equally different foundational assumptions about individuals and their relationship with AI. The researchers mention East Asian and Indigenous communities as more prone to have a positive view of AI because these cultures celebrate a sense of interconnection with all living beings and things. In contrast, some European and United States depictions of AI evokes terror and fear that machines will control humans. What this points out is that cultural differences reflect on how people understand themselves and their relationships with AI. In other words, culture defines how AI is adopted and why.
On this note, it’s important to mention that most of the current AI models have been developed from WEIRD (Western, Educated, Industrialized, Rich and Democratic) countries. A research performed by Linxen et al apud Ge et al. (2024) found that, between 1990 and 2006, less than 1% of prominent HCI publications came from outside WEIRD countries. One important conclusion that can be drawn from such a result is that what is considered Human Centered Design today is largely biased to represent mostly Global North countries, leaving Global South and many groups underrepresented in Human Centered and AI models.
In turn, Human Centered Design, has been gaining increasing popularity as a result of a shift in the technology culture towards a data-centrism. As a concept, Human Centered Design seems straightforward: a creative approach to problem-solving that starts with the people you’re designing for and ends with new solutions that are tailor-made to suit their needs. However, this concept is a deceptively simple idea, because people within a community and across the world have different values, beliefs and behaviors.
In other words, culture should not be treated as only a factor of usability or interface design in AI development, but as a determining element on how AI is developed throughout all stages of Design and implementation, not an afterthought. We argue that a successful AI should have a more rigorous consideration of cultural variations surrounding people’s expectations and behavior towards AI (Ge et al.,2024).
AI is a double edged sword in a BANI world
BANI World is an acronym developed to attempt to describe today’s global scenarios: Brittle, Anxious, Non Linear and Incomprehensible. However, this “BANIness” affects Global North and Global South differently.
This is so because the global distribution of wealth is uneven in both these areas, as well as its capitalist economy stages. This can be perceived in the types of problems each region faces: while Global North’s challenges with AI seem to be connected with education outdatedness and welfare state de-structuring, Global South is facing an increase in Digital Colonialism and other contradictions.
Berg (2024) analysed in detail how European welfare, that helped working class access education in the past, is now in ruins. As a result, “not only are there fewer guarantees, but the conditions attached to access to the welfare state create further insecurity”. This author also describes a state of ‘permanent austerity’ in modern Western Europe as post-Fordist, which results in societies and economies
“characterized by flexible specialization, volatile global markets, a destabilization of the relation between labour and capital, the end of the family wage, the rise of service sectors, the fragmentation of cities and the dispersal of governance to other organizations”.
Against this backdrop, studies from Boffo and Fedeli (2018) and Santilini et al. (2025) have found that in Europe, universities are struggling to keep up with the fast pace of global changes and the need to maintain curriculum relevance in a fast-evolving technological landscape. This challenge seems to be reflected in all tiers of the workforce: people leaving university are not fully prepared for today’s BANI world and people already working seem to struggle to maintain employability by training relevant sets of skill.
In contrast, Global South challenges with AI can be linked to what Lippold & Faustino (2023) describe as Digital Colonialism. In their book, the authors elaborate on the concept of primitive data accumulation and data colonialism, which is described as
“(…) a set of practices, techniques and politics through which ‘social media platforms create, in a sociotechnical way, mechanisms to extract profit from the digitalized experience of the subjects’, based on a violent and despotic logic that resembles the old ‘primitive accumulation'”.
On the same note, in developing countries, access to quality education is already a significant challenge. Issues like digital literacy and biased data that might produce discriminatory outcomes are part of contradictions that are rising amidst the rapid growth in AI uses, which can further social and economic disparities.
Potential uses of AI in education
As mentioned, the introduction and scaling of AI tools in today’s fragmented world poses many challenges. However, some of the promising uses of AI in education are personalized learning, assistive technology and community collaboration.
A powerful characteristic of AI tools is pattern analysis and prediction. A well trained AI can be geared towards analyzing individual learning patterns and preferences to create content to individual needs (Guan et al, 2025).
In this sense, AI can be of value because they can provide assistive technologies for students with different learning styles or learning disabilities. Also, AI can offer real-time translation services for non-native speakers, which could help bridge some educational gaps between different people and promote equity in education (Bewersdorff et al, 2025).
Another important potential use is fast learning and community collaboration. AI usage in learning platforms can facilitate access to a large set of information and support knowledge dissemination, which can be helpful both in learning setups and working environments (Santilini et al, 2025).
What do we want for AI?
When discussing the implementation and limitations of AI in education, it is fundamental to have a more rigorous consideration of cultural variations. Research has shown that there are notable differences between countries and communities about their expectations of AI, which can be linked to culture. This is visible in cultural products of different countries and regions, such as movies like “The Terminator” in the United States and “Sayonara” in Japan (Ge et al.,2024). In addition to cultural differences, there are also geopolitical, economic and labor differences that have to be taken into account when discussing AI in education.
Notably, global labor division, distribution of wealth and fragmentation of public services as a result of a “permanent austerity” state are factors that need to be considered when evaluating the challenges and opportunities for AI use in Global North and Global South. How these elements translate locally vary significantly as well. Currently Global North’s main challenge seems to be linked to education outdatedness and welfare state destructuring, while Global South is facing an increase in discrepancies that already existed, such as digital literacy, access to quality education and discriminatory AI models.
On the other hand, some of the promising uses of AI in education are personalized learning, assistive technology and community collaboration. Today’s BANI world asks both students and workers for more adaptability, which means that lifelong learning and continuous professional development are even more important than ever to ensure future employability in both Global North and South. In this context, AI’s capabilities to identify patterns and predict behaviors can help to create continuous learning paths that are also adapted to each individual’s need.
Another interesting opportunity for AI in education is multiple collaborative and cooperative discussion and production of AI platforms, safe communication strategies and education enhancement opportunities across the globe.
In conclusion, AI represents a critical juncture for education and labor, presenting both challenges and opportunities for the Global North and South. The advent of AI has brought about significant transformations in various sectors, and education is no exception. Even if the characteristics of the challenges associated with AI development and adoption vary significantly from culture to culture, frameworks that foster collaboration and cooperation to create the creation and training of AI seem to be a relevant path for future AI uses in education.
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