Accelerating AI-Driven Education Infrastructure in Africa

“Until connectivity, devices, power, and language are addressed, AI will remain a promise rather than a transformative force in African classrooms.”

Jeph Acheampong, Founder and Director of Blossom Academy

Across Africa, education systems are being asked to prepare students for economies increasingly shaped by artificial intelligence while still addressing long-standing gaps in access, quality, and relevance. Governments, schools, and workforce institutions are exploring how AI tools might support teaching, expand opportunity, and connect learning more directly to labor market needs.

Yet the question confronting many systems is not whether AI has potential, but whether the foundational conditions exist for that potential to translate into classroom practice.

AI does not enter education in a vacuum. Its use depends on reliable connectivity, consistent device access, stable power, and institutional capacity to support teachers and administrators over time. Where these elements are uneven or absent, AI risks becoming another layer of reform that operates alongside core educational functions rather than strengthening them. In this sense, Africa’s push toward AI-enabled education highlights a broader reality: technological adoption exposes the strengths and weaknesses of the systems into which it is introduced.

The challenge is compounded by scale. Many African countries face rapidly growing student populations and strong demand for digital and AI-related skills, even as public education systems operate under tight fiscal and administrative constraints. Efforts to introduce AI must therefore contend with both urgency and limitation. Infrastructure investments alone are insufficient without governance structures, teacher support, and coherent implementation strategies that enable new tools to be used consistently and effectively.

As debates about AI in education increasingly focus on platforms and applications, experiences across African education systems point to a more fundamental issue. The success of AI depends on whether systems are designed to absorb innovation, align it with curriculum and pedagogy, and sustain its use beyond short-term initiatives. Understanding how infrastructure, human capacity, and institutional coordination interact is essential to assessing whether AI strengthens learning or adds strain.

To explore what building those conditions requires in practice, we spoke with Jeph B. Acheampong, founder of Blossom Academy, a workforce development accelerator operating across multiple African countries. His work focuses on equipping young people with data and AI skills while navigating the infrastructural, institutional, and policy realities that shape how education and workforce systems adopt new technologies at scale.

Jeph B. Acheampong is on a mission to close Africa’s opportunity gap through education and technology. As founder and director of Blossom Academy, an award-winning workforce development accelerator, he equips learners with data and AI skills to increase their incomes while empowering growing companies to embed data-driven practices.

Under his leadership, Blossom Academy has expanded from Ghana to Nigeria and Rwanda, achieving an 85 percent career placement rate for nearly 1,000 graduates. The organization’s pioneering model led to Jeph’s appointment by the Government of Ghana to contribute to the development of the Ghana National AI Strategy (2023–2033). Its impact has garnered global recognition, earning honors such as the Expo 2020 Dubai Global Innovator Award and Ghana EdTech of the Year, alongside support from the UN World Food Programme, German Development Agency (GIZ), and Mastercard Foundation, among others. Blossom’s work has also been featured by CNN, the United Nations, and on the Nasdaq MarketSite in Times Square.

Acheampong’s career is defined by a commitment to widening access and creating pathways for growth. He holds a master’s degree from Harvard University, where he was a finalist in the President’s Innovation Challenge, and a bachelor’s degree from New York University, where he received the President’s Service Award.

Before founding Blossom Academy, he was a founding member of Esusu Financial, a U.S.-based billion-dollar company advancing financial equity, and worked as a Data Solutions Consultant at Experian on Wall Street. He has been recognized as a Princeton in Africa Fellow, an Acumen Fellow, and a Harvard Kennedy School Social Innovation Fellow.

For AI to move from policy ambition to classroom practice, African education systems must first address foundational gaps that shape daily learning conditions. Acheampong is direct about where the process must begin. “For AI adoption to translate into real classroom impact in Africa, education systems must first close the foundational digital divide,” Acheampong says. Without this baseline, more advanced applications remain out of reach for most schools.

Connectivity is the first constraint. Reliable and affordable internet access determines whether students and teachers can use digital resources consistently rather than sporadically. Acheampong emphasizes that progress depends on “public–private partnerships that prioritize last-mile broadband access so learners can use digital resources and AI tools without interruption.” In many regions, connectivity remains uneven across urban and rural areas, creating sharp differences in what schools can realistically implement.

Access to devices presents a related challenge, but Acheampong cautions against simplistic solutions. Rather than promoting one-to-one laptop distribution, he argues for shared infrastructure that supports learning and experimentation. “Device access must also be normalized, not through a laptop-per-child approach, but through refurbished computer labs that give students the space to tinker, collaborate, and build confidence with technology,” he explains. These shared environments, he suggests, allow schools to maximize limited resources while fostering collective learning rather than isolated use.

Acheampong underscores the formative role that such spaces can play by highlighting the power of early exposure. “Bill Gates often credits an early computer lab as the spark that shaped his trajectory, and African learners deserve that same catalytic exposure,” he says. The point is not to replicate individual success stories, but to recognize how access to functional technology environments can influence students’ sense of possibility and agency.

Infrastructure gaps extend beyond connectivity and devices to the basic question of power: “Closing the continent’s $108 billion infrastructure gap also requires confronting the persistent challenge of power,” Acheampong notes. Unreliable electricity and extreme heat disrupt learning schedules and limit the viability of digital tools. As a result, he argues that “investments in renewable, school-based energy solutions” are essential to ensuring that AI-enabled learning can function consistently rather than intermittently.

Language presents a further, often overlooked barrier. Acheampong stresses that AI tools must be usable in the languages students and teachers speak with confidence. This, he argues, “demands greater investment in NLP for African languages and locally relevant content.” Without such investment, AI risks reinforcing exclusion by privileging learners who are already fluent in global languages and sidelining others.

Taken together, these gaps shape the boundary between aspiration and practice. As Acheampong puts it, “until connectivity, devices, power, and language are addressed, AI will remain a promise rather than a transformative force in African classrooms.” The implication is clear: foundational infrastructure is not a preliminary step to be checked off, but the terrain on which all subsequent AI-enabled education efforts depend.

Infrastructure may enable access to AI tools, but it does not ensure their use. Many initiatives fail at the point where policy meets practice, particularly when teachers and school leaders are not equipped to integrate new technologies into daily routines.

Acheampong emphasizes that lasting impact depends on the people inside the system. “Governments often focus on learners when designing AI strategies, but sustained impact depends on the teachers who bring these tools to life,” he says.

One approach gaining traction is the train-the-trainer model, which aims to spread AI literacy beyond a small number of pilot schools. Acheampong views this model as a useful starting point, particularly in resource-limited systems. “A train-the-trainer model can help democratize AI literacy across schools, ensuring that skills do not remain concentrated in a few pilot sites,” he explains. When implemented at scale, such models can create internal expertise, reducing reliance on external support.

Yet training alone does not address the conditions under which teachers work. Acheampong points to structural challenges that undermine engagement. “No model will succeed if teachers lack the motivation or stability to fully engage,” he says. In many public-school systems, low wages and limited incentives erode morale, making it difficult for educators to invest time and energy in new initiatives. Sustainable AI adoption, he argues, requires acknowledging teachers’ contributions and “providing compensation that supports their basic needs.”

Beyond the classroom, community dynamics shape whether AI tools are used consistently. Acheampong notes that “many pilot programs fail because they are designed in isolation from the people they aim to serve.” To counter this, he emphasizes the importance of needs assessments and co-design with parents, teachers, local leaders, and youth. When communities are involved in shaping solutions, AI tools are more likely to be used creatively and maintained beyond the initial pilot phase.

Leadership capacity also plays a decisive role. Acheampong stresses that “school heads, district directors, and civil servants need targeted capacity building to understand the pedagogical and operational value of AI.” Without leaders who are confident in both the educational and administrative implications of AI, initiatives tend to remain fragmented. As he puts it, “without digitally confident leadership, AI remains stuck in isolated experiments.”

The cumulative effect of these gaps is familiar: promising pilots that never become standard practice. Acheampong argues that avoiding this outcome requires shifting from fragmented experimentation to coordinated implementation. “True sustainability demands system-wide implementation rather than fragmented pilots,” he says. When AI initiatives are designed to reach only a handful of schools, they reinforce siloed thinking. When they are planned and coordinated across regions, integration becomes the norm rather than the exception.

As African education systems work to close infrastructure gaps and strengthen human capacity, public–private partnerships increasingly shape how AI readiness is built in practice. Acheampong views these partnerships not as supplemental funding channels, but as mechanisms for coordination across sectors that rarely align on their own. “Public–private partnerships are essential to building AI-ready education systems in Africa because they combine the reach of the government with the agility and innovation of the private sector,” he says.

Connectivity is one of the clearest areas where this coordination matters. Acheampong argues that telecommunications providers and governments must work together to reflect the realities of underserved communities. “Telcos should work with governments to design pricing models that reflect the realities of rural communities,” he explains, pointing to approaches such as differential tariffs or targeted subsidies. Without such adjustments, connectivity risks remaining a commercial service rather than functioning as the public good AI-enabled education requires.

Partnerships also play a role in addressing device access at scale. Coordinated procurement and sustainable distribution can help avoid fragmented efforts that leave schools with incompatible hardware or unsupported systems. Acheampong emphasizes that access alone is insufficient if devices cannot be maintained. Building local capacity to service and repair equipment reduces dependence on external vendors and keeps infrastructure operational over time.

Beyond physical infrastructure, Acheampong stresses that partnerships must extend into curriculum and learning design. He argues that preparing students for AI-driven economies requires moving away from models that reward memorization. “With AI automating routine tasks, learners must be equipped to think critically and even challenge machine outputs,” he says. This shift has implications for how schools define learning objectives and how private-sector expertise is integrated into education.

Data sharing is another area where coordination is critical. Acheampong notes that governments can enable innovation by sharing datasets across ministries, allowing developers to build AI tools that reflect local conditions. At the same time, he encourages private-sector actors to be more transparent about the real problems they face. “When these challenges are integrated into project-based learning, students gain real work experience and build AI-ready skills long before they enter the workforce,” he says.

Sustaining these efforts requires attention to maintenance and operations, not just initial deployment. Acheampong points to the importance of training local technicians who can “repair devices, update systems, and keep school networks running.” Without this ecosystem, even well-funded initiatives risk deterioration once pilot funding ends.

In this sense, public–private partnerships function as connective tissue, linking infrastructure investment, curriculum reform, data access, and workforce demand. When designed with long-term sustainability in mind, they help ensure that AI adoption strengthens education systems across diverse African contexts rather than remaining confined to isolated or short-lived projects.

As AI tools move more deeply into education systems, questions of trust, safety, and long-term governance become unavoidable.

Acheampong argues that rapid adoption without clear safeguards risks undermining confidence among educators, parents, and students. “To balance rapid AI adoption with the need for privacy, safety, and trust, schools must anchor their efforts in ethics and capacity building,” he says. For him, responsible use is not a constraint on innovation, but a prerequisite for its durability.

Teacher preparation again plays a central role. Acheampong emphasizes that AI ethics should be embedded into professional development so educators understand the implications of data use, student protection, and system integrity. When teachers are equipped with this awareness, he notes, they are better positioned to use AI tools thoughtfully rather than defensively. Ethical literacy, in this sense, becomes part of everyday instructional judgment rather than a separate compliance exercise.

Government and development agencies also shape whether systems can manage risk. Acheampong points to the need for stronger cybersecurity capacity, observing that “many schools lack the technical expertise to evaluate AI tools or safeguard sensitive information.” Without investment in IT staff and local cybersecurity professionals, schools remain vulnerable to data breaches and misuse, regardless of how sophisticated the tools are.

Control over data is another source of long-term trust. Acheampong cautions against overreliance on externally governed platforms, arguing that “prioritizing local or regionally governed platforms strengthens data sovereignty and builds public trust in the system.” When student data is stored and managed by institutions accountable to local authorities, schools retain greater oversight over how information is used and shared.

At the system level, Acheampong advocates for a deliberate approach to scale. “Schools should adopt a start small, measure, and scale approach,” he says, emphasizing the importance of clear metrics. Evaluating learning gains, reductions in teacher workload, and parent feedback allows systems to adjust policies before expanding adoption. This feedback-driven process helps balance urgency with caution, enabling progress without sacrificing trust.

Looking ahead to workforce readiness, Acheampong emphasizes that technical skills alone will not determine students’ competitiveness. By 2035, he says, students must develop the ability to question, interpret, and challenge AI outputs. “Critical thinking will be essential,” he explains, alongside strong data literacy that allows learners to recognize bias and assess the reliability of algorithmic predictions.

He also points to skills that lie outside formal curricula but are increasingly shaping opportunities. Visibility and agency, Acheampong argues, matter in global labor markets. At Blossom Academy, he observes that learners who secure remote roles often share their work publicly, building credibility through projects rather than credentials alone. Entrepreneurial agility, too, remains critical in contexts where infrastructure constraints persist. Students who can identify local problems and adapt creatively to limitations are often best positioned to turn AI into practical solutions.

Taken together, these priorities suggest that Africa’s AI education agenda is as much about governance and human capability as it is about technology. Preparing students for an AI-driven economy requires systems that protect trust, invest in people, and align innovation with local realities. Where those conditions are in place, AI becomes not just a tool for participation in the global economy, but a means for shaping it.