Geoffrey Alphonso, CEO of Alef Education.
The shift toward AI-based education is evident worldwide as early-stage AI-learning initiatives gain funding and schools experiment with adaptive learning. According to the Digital Education Council Global AI Student Survey 2024, 86% of students globally are already using AI in their studies, with more than half engaging with it weekly.
Yet, despite this rapid uptake, most education systems are still not ready to guide that use effectively. In my experience, that gap is the real policy challenge we need to address.
From Adoption To Impact: Bridging The Gap
This raises uncomfortable but necessary questions. If students are already using AI so extensively, why are education systems still struggling to guide its use? Are national strategies aligned with classroom realities, or are they still operating at a policy level removed from day-to-day learning? And if AI can enhance learning outcomes, why are so many educators still unprepared to integrate it into their teaching?
At the heart of the issue is misalignment across the ecosystem. Governments, schools, educators and edtech providers are often moving forward, but not always together. AI in education cannot succeed as a collection of isolated initiatives. It must be treated as a coordinated, systemwide transformation. Countries making meaningful progress are those treating AI not as digital experimentation, but as structural reform. For example, the UAE National Strategy For AI 2031 demonstrates how national policy can move systems beyond siloed innovation and toward structured implementation.
Policy As An Enabler: Aligning AI Strategy With Education Outcomes
Policy must evolve from high-level direction to practical enablement. While many countries have established comprehensive AI frameworks, the challenge lies in aligning these strategies with education-specific goals.
The UAE provides a strong benchmark that other countries should learn from. Their strategy, as linked above, prioritizes talent development, governance and data infrastructure, alongside early adoption of AI across public services. Similarly, frameworks such as the Australian Framework for Generative Artificial Intelligence in Schools reflect a broader shift: Policy is no longer solely about encouraging innovation. Rather, it’s about ensuring responsible, ethical and outcome-driven adoption.
A key area where this alignment must materialize is assessment. Traditional evaluation models are not designed for AI-enabled learning environments. As students increasingly use AI tools, systems must transition toward process-based assessments that evaluate understanding, reasoning and creativity, rather than final output. At the same time, safeguards must ensure academic integrity and responsible usage.
Policy sets the conditions for scale, but without clear pathways to implementation, even the most well-designed strategies remain theoretical.
From Vision To Execution: Bringing AI Into The Classroom
This is where the gap between ambition and reality becomes most visible. Many AI initiatives fail because they remain optional rather than integral. When AI is not embedded into curriculum design, adoption becomes inconsistent and dependent on individual teacher confidence.
To move from vision to execution, AI must be integrated into everyday learning—aligned with curriculum standards, supported by clear guidelines and reinforced through continuous teacher training.
This requires translating policy into structured frameworks that define how AI is used in classrooms, how success is measured and how educators are equipped to deliver it. Without this level of clarity, implementation can become fragmented, and impact may remain inconsistent.
Educators are central to this transformation. Teachers are not just end users of AI tools—they are the bridge between technology and meaningful learning. However, many are expected to integrate AI without sufficient training or support. Professional development must shift from one-off workshops to ongoing, embedded learning models that build technical confidence and pedagogical understanding. When teachers feel empowered, AI can transition from a disruptive force to an enabling one.
Scaling The Backbone: Infrastructure And Interoperability
Even well-designed strategies fail without the right infrastructure. Many education systems still operate on fragmented digital ecosystems, limiting AI scalability.
Evidence highlights the scale of this challenge. One study found that over 40% of educators cite insufficient technical support as a key challenge to implementation. In some contexts, respondents reported broader infrastructure gaps, including limited devices and unreliable internet.
Addressing this means governments must build a robust digital backbone for education systems. This includes connectivity, equitable device access and interoperable platforms that allow data to flow securely across systems. Without interoperability, AI tools can remain isolated, limiting their ability to deliver systemwide impact.
The Strategic Role Of Edtech: Enabling Implementation At Scale
With the online education market projected to reach $221.71 billion in 2026, edtech has become a core driver of education transformation, facilitating personalized learning, automated teacher support and nationwide data analytics monitoring.
However, to deliver impact at scale, edtech must shift from product-centric thinking to system-centric design. This begins with cocreation. Solutions cannot be built in isolation and then introduced into classrooms. They must be developed in partnership with governments, aligned with national curricula and tested within real learning environments to ensure relevance and long-term adoption.
Equally critical is scalability by design. Many tools succeed in pilot programs but fail to scale across diverse contexts, languages and regulatory environments. Edtech providers must therefore design flexible, modular platforms that can adapt while maintaining consistency in quality and outcomes.
Additionally, edtech platforms are uniquely positioned to provide real-time insights into student performance, learning gaps and systemwide trends. When aligned with national objectives, this data can inform policy decisions, optimize resource allocation and continuously improve learning outcomes.
The Strategic Path Forward
The next phase of AI in education will be decided not by the sophistication of the technology, but by the quality of the systems built around it. That means governance that keeps pace with innovation, infrastructure that reaches every classroom and teachers who are genuinely equipped, not just expected, to lead this transformation.
The countries that get this right will not be those that move the fastest. They will be those that move most deliberately. Those that act with urgency and intention today will be the ones shaping learning outcomes for generations to come.
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