Leveraging AI for Sustainable Development in Developing Regions 

Introduction 

From the first spark of human curiosity, technological advancements have crafted tools to extend human capabilities. Most innovations promised better productivity and progress, eventually giving birth to AI. AI represents a significant shift as it moves beyond programmed tasks into adaptive learning and decision-making. Machines now do not just serve; they learn and adapt. AI is the fusion of human knowledge and machine learning, and processing information is not the only option anymore, mimicking human thinking and learning from experience are add-ons.  

Developing countries often depend on exports of a limited range of commodities, making them vulnerable to economic fluctuations. Diversification through AI offers a pathway to new forms of stability and growth.¹ The potential of AI can help advance fields such as the economy, education, and healthcare in the developing regions. However, skepticism prevails in areas where socio-economic challenges come before technological development. New strategies should integrate AI into national development plans by evaluating policies, infrastructures, and workforce readiness to effectively harness AI’s capabilities. 

The sound decision to call for strategic AI adaptation was central to the discussions at the iNNOV8 event held in Paris.² The event featured a panel discussion with experts on the second day of AI Action Summit Week at École Normale Supérieure (ENS). The panel of experts examined how developing regions can use AI to drive sustainable development. Additionally, they addressed some key themes including bridging the AI knowledge gap, adapting labor markets to AI-driven transformations, leveraging media to shape AI literacy, and establishing ethical and governance frameworks. A key takeaway was that AI is not merely a technological advancement but a socio-economic force that, if efficiently utilized, can empower developing regions in the face of existing disparities. By examining these aspects, this commentary aims to provide a deeper understanding of the challenges and opportunities that lie ahead in integrating AI into these domains.

New strategies should integrate AI into national development plans by evaluating policies, infrastructures, and workforce readiness to effectively harness AI’s capabilities.

AI Literacy and The Evolution of Education 

AI is set to revolutionize industries, but progress remains imbalanced due to gaps in AI literacy between developed and developing nations. AI literacy includes the skills and critical understandings necessary to engage with AI technologies. Given AI's far-reaching influence, its integration into education is essential.  

Developing nations face significant challenges in implementing AI literacy programs, such as curricula gaps, and the lack of resources and trained educators.³ With the existence of these disadvantages and many more in other domains, various AI initiatives have emerged, particularly in the Gulf Cooperation Council (GCC) countries, as illustrated in Figure 1. Most of these AI initiatives are in the early stages, with ongoing obstacles such as inadequate infrastructure and funding limitations preventing substantial outcomes. 

Prior to any investments, developing nations should prioritize incorporating AI into the education system to counteract AI illiteracy.⁴ A critical consideration is that 55% of the Global South’s population⁵ is under the age of 25. The Global South, encompassing Africa, Asia, the Middle East, and Latin America, is home to 7.2 billion people, and is expected to contribute to most of the global population growth in the coming decades. This demographic change has created unexpected demand for education while also presenting a unique opportunity to introduce transformational models, like AI. AI can ensure that students, regardless of location or socioeconomic status, have access to education.  

AI systems are prompting educators to rethink traditional teaching methods, moving away from rigid essay structures and toward fostering conceptual understanding and problem-solving skills. Instead of depending solely on memorization, education should help students recognize the real-life applications of what they learn. This way, students are more likely to engage deeply, reducing the reliance on using AI merely as a shortcut. A tool that is advancing AI-powered education is Khanmigo⁶, an AI-driven tutor and teaching assistant. It guides users toward independent problem-solving. Such tools can be particularly beneficial in developing nations, offering accessible learning support.  

Additionally, educators can use AI to create lesson plans and interactive learning experiences to improve learning conveyance. In China, AI brainwave-sensing headbands are introduced to education.⁷ These devices track students’ real-time concentration levels, sending collected data to teachers in the form of insight reports. This tool has improved classroom discipline and academic performance according to teachers’ notes, despite accuracy concerns. Moreover, these innovations also raise questions about issues such as privacy.

AI is not merely a technological advancement but a socio-economic force.

There is a weak correlation between homework and academic achievement⁸, and its removal could alleviate stress while fostering deeper intellectual curiosity. For decades, the practice of assigning homework was criticized for its inefficiency and lack of engagement. Now, Al allows students to complete homework easily, and without genuinely engaging with the material, eliminating the purpose for which homework was initially designed to serve. Therefore, removing tasking homework can become a plausible way to prioritize other meaningful learning experiences offered by AI. 

The rise of AI has also reignited debates about the effectiveness of traditional assessment methods. A growing consensus suggests that the current examination system disfavors critical thinking and problem-solving skills, while these skills are indispensable in an AI-driven future. However, the fear of AI’s impact on academic integrity may favor traditional methods. This contentious matter is just the tip of the iceberg. Instead of resisting AI, the focus should be on its thoughtful integration. Embracing this shift will help nurture a generation capable of thriving in an AI-enhanced world, as AI is bound to dominate other areas as well.  

Unlocking AI in education for developing regions requires plans that account for ethical considerations and resource limitations. Instead of perpetuating a system where education remains a privilege for only a few, the digitalization of learning by AI can serve as an equalizer, promising exemplary education. Even then, the challenge lies in the fact that not all students will have access to AI-powered learning, especially in some of the developing countries that are lacking fundamental infrastructure such as electricity and the internet. Without AI, the distinction between good and bad education is straightforward. With AI, more layers are introduced to the classification, such as the extent of human involvement in the learning process and the challenge of balancing automation with the development of human thinking. If AI-enabled education remains regional or exclusive to certain societies, it may lead to educational discontinuity in places where AI cannot be adopted. Furthermore, if AI is integrated globally as a standard, the educational disparity between developed and developing regions will only widen, making education more inequitable than before the existence of AI. This could result in a generation ill-equipped to thrive outside local context. If actions are not taken to solve core issues, the inequality in access to services will continue to grow. 

The New World of Work in the Era of AI 

In preparation for the future of work, AI’s influence on the workforce cannot be overlooked, as the arrival of AI is causing elemental changes. More than 10% of workers⁹ today hold job titles that did not exist back in 2000, signaling the decline of certain traditional roles. This dynamic shift is vividly captured in the projections shown in Figure 2, showing both the fastest growing and rapidly declining occupations between 2025 and 2030. Despite the rise of demand for AI literacy in the workplace, only one in every 500 job postings¹⁰ requires AI knowledge and literacy.

A recent analysis¹¹ explored the relationship between AI exposure and income levels in different regions. Workers in low-income countries experience significantly lower exposure to AI compared to those in high-income ones, according to the findings. This variation is partly attributed to the structural differences in labor markets. In developing economies, more jobs rely on manual labor and interpersonal interactions, which are less susceptible to AI automation. Additionally, limited access to electricity and the internet in low-income regions is the rationale behind further restriction of AI adoption. The analysis is illustrated in Figure 3.  

An element used to measure the AI exposure index in the analysis is automation, which is one of the most notable AI-used processes that is altering the workforce through automating repetitive tasks. AI technologies and robots are handling human performed tasks, such as data entry, customer services, and other manual duties. With the automation of routine tasks, concerns about workforce displacement grow. Predictions indicate that AI could eliminate up to 85 million jobs worldwide in 2025. In compensation for this, AI is expected to create 97 million new roles¹² during the same period, mostly in data analysis, cybersecurity, and software engineering fields.  

In an AI-powered economy, there is a growing preference for individuals that acquire high competency levels in skills such as data analysis and programming that align with the demands of emerging industries. Furthermore, employers should invest in continuous learning and skill development to maintain a reliable workforce. Preparing employees for the future requires staying attuned to the latest technological advancements and fostering a culture of upskilling and reskilling. One relevant example is the integration of AI into human resources platforms such as Lattice¹³, which leverages AI to improve feedback and performance reviews.  

An additional layer complicating the landscape is the diverse range of attitudes toward AI within the workforce. While senior executives may be highly motivated to integrate AI into organizational workflows, a large portion of workers remain hesitant. Based on Slack’s Workforce Lab, five distinct AI personas¹⁴ can be identified in a workplace:  

These differing perspectives are a token of the broader challenges in building trust in AI systems. As organizations navigate these challenges, the thriving organizations will not necessarily be those with the most advanced technologies, but those who rethink the nature of work and adapt their organizational models to the opportunities AI presents. For these companies, it will not only be scale that matters, but speed and innovation.¹⁵ The next wave of innovation will feature AI agents capable of performing tasks autonomously. In developing nations, AI agents are already demonstrating their potential. For instance, Alibaba’s AI agent, Accio¹⁶, is helping e-commerce businesses boost sales. Half of the top 20 countries using Accio are enterprises located in developing countries. The private sector stands to gain substantially from integrating AI into the workplace in developing countries. After providing staff training and using AI for critical domains of the work environment, ensuring AI’s responsible use is the next priority.

Striking the right balance between rigidity of regulation and safeguarding privacy concerns will determine whether AI fosters development or deepens disparities in these regions.

Governing AI and the Ethics of Innovation

The ethical dimensions of AI are constantly discussed within technology governance frameworks. For developing countries, these challenges are more complex, as they are struggling with the risks of premature deployment while handling existing development priorities. AI governance, by definition, is the establishment of legal frameworks, regulations, and standards to ensure responsible use of AI worldwide. In developing countries, the potential for misuse of AI has increased in the absence of data protection regulations, leaving these societies vulnerable to exploitation. Globally, AI legislation is in its early development, leaving developing nations with more time to construct AI policies before harmful practices are established. Developing countries should secure their presence in the global conversations on AI¹⁷, to ensure that international policies include the concerns of diverse economies and communities. AI risks are beyond simple technical malfunctions, as issues such as bias, copyright violation, and misinformation (AI hallucination) have already demonstrated their harmful impacts. These risks penetrate multiple regulatory spheres, often exceeding the scope of existing legal measures, as illustrated in Figure 5.

AI does not exist in isolation; as a result, it is inherently shaped by the social, political, and cultural environments in which it is deployed. Consequently, AI takes on distinct forms across different regions, influenced by historical narratives, economic priorities, and societal values. A diverging perspective can be observed in how Western and Eastern societies conceptualize AI-powered entities¹⁸, while Hollywood frequently depicts robots as existential threats, Asian cultural portrayals lean towards coexistence and harmony.  

AI’s fast evolution makes legal frameworks incapable of providing a comprehensive solution. By nature, legal systems operate at a slower rate than technological advancements, causing a delay in reaction time and leading to what experts call the pacing problem. This was evident in the legislative process of the European Union AI Act¹⁹, where early drafts struggled to anticipate the risks posed by emerging models such as ChatGPT. In response to this obstacle, a risk-based approach was proposed. The method tailors oversight to the potential impact of AI systems where insignificant risk applications face minimal restrictions, whereas substantial risk technologies are subjected to strict compliance measures as seen in Figure 6. The EU AI Act, Brazil’s AI bill, and Canada’s AI and Data Act all integrate elements of this framework.

For developing nations, policymakers should carefully evaluate the interplay between soft law, regulatory sandboxes, and hard law²¹ as illustrated in Figure 7. 

The debate over whether a flexible or strict stance better serves developing economies is unresolved. While flexible regulations allow room for innovation, they might be unable to prevent several AI-driven harms. Conversely, legal measures may risk suspending or restricting technological progress. Striking the right balance will determine whether AI fosters development or deepens disparities in these regions. 

The Role of AI in Media  

Media producers use AI to expand their creative horizons and optimize audience engagements. Since AI can process vast datasets, audience preferences and viewing patterns²² are examples of insights that can be obtained to improve content creation. Moreover, AI is crucial in combating misinformation. The internet is overflowing with fabricated stories, making it difficult to distinguish fact from fiction. A concerning development is that AI itself has recently become a source of misinformation in the media, through the creation of deceptive digital contents that distort reality and manipulate public perception.  

Yet, to unmask such deceptions, advanced AI systems can analyze sources and information to identify inconsistencies in news reporting. A notable example is Google’s 2017 Search Algorithm update²³, designed to restrict the spread of misinformation and hate speech. Similarly, researchers at the University of Michigan pioneered an AI model capable of detecting fake news with a 76% accuracy rate. Such advancements are a part of a broader trend, as highlighted in Figure 8 which shows the growing influence of AI in the media and entertainment market over the next decade. 

AI’s growing influence in creative industries has sparked concerns about the erosion of human artistic expressions. Arguments exist regarding the risks of homogenizing culture and art²⁴ through AI automation, potentially weakening human creativity. On the other hand, another perspective reveals AI as an enabler rather than a replacer, transforming creativity by providing tools that expand access. Media professionals dedicate their energy to artistic exploration made possible by AI, which eliminates repetitive tasks through automation and creates time for creative endeavors. Rather than displacing human ingenuity, AI can serve as a driver of new forms of creative collaboration. Automation in AI also extends to AI-generated images, which can copy and even steal from artists. This ongoing issue has led to court cases, though proving stylistic copying remains complex. In some cases, court rulings have favored the copier rather than the original artist²⁵, raising concerns about property rights in the age of AI. 

The media platforms flip the narrative, shaping how AI is publicly seen and understood. Nevertheless, media portrayals of AI remain inconsistent and misleading. The media plays its role through translating complex technological concepts of AI into accessible language. But lurking in the shadows is the media outlets’ inability to resist sensationalism. Too often, AI is labeled as a precursor to widespread unemployment, and these oversimplified depictions cause fears and unrealistic expectations.²⁶   

To combat this, a more responsible approach to AI reporting is essential, one that is rooted in accuracy and transparency. Mass media should prioritize factual narratives while integrating expert insights when elaborating on AI’s capabilities and limitations. To evoke an example, NewsGPT’s²⁷ initiative is an AI-driven journalism platform. Its generated contents use advanced AI systems that analyze vast amounts of data from various sources. Moreover, its feedback mechanism for readers and experts demonstrates a commitment to refining AI’s role in news delivery. 

Conclusion 

While the widespread adoption of AI in developing nations may seem like a distant ambition, it should not be dismissed as an unattainable goal. AI literacy, generated through accurate media representation and AI-driven education, forms the foundation of economic vitality and institutional resilience. As AI reshapes industries and the job landscape, organizations and individuals should use adaptive strategies, while governments implement risk-based governance to steer technological evolution. Rather than treating AI as a standalone priority, developing nations should embrace it as a transformative force, accelerating progress while breaking down barriers that have long constrained their economic limitations. 

  

 

 

 

 

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