“My people are destroyed for lack of knowledge.” This oft-quoted biblical lament has, for generations, been used to justify the expansion of schooling, the proliferation of textbooks, and the relentless push for access to information. But in 2026, that statement demands reinterpretation.
The crisis of our time is no longer scarcity of knowledge. It is the inability to interpret, prioritise, and apply an overwhelming abundance of it.
A recent guide by Harvard Business School on “AI in 2026” captures this shift with striking clarity. Artificial intelligence is no longer a peripheral tool used to enhance productivity. It has become the very infrastructure through which decisions are made, systems are designed, and organisations function. In such an environment, the question is no longer whether institutions will adopt AI, but whether they understand how to operate within its logic.
For education systems, particularly in contexts like Kenya, this transition is both urgent and unsettling. Schools were designed for an era in which information was scarce, teachers were gatekeepers of knowledge, and assessments measured recall. Today, information is abundant, instantly accessible, and increasingly generated by machines. The traditional architecture of schooling is therefore misaligned with the realities of the present.
One of the most celebrated promises of AI is productivity. Studies show that tasks such as writing, research, and ideation can be completed two to three times faster with AI assistance. Learners and professionals alike can now attempt tasks that previously required specialised training. But there is a crucial caveat: AI does not turn novices into experts. It can generate answers, but it cannot supply judgment. It can suggest solutions, but it cannot replace experience.
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This distinction is where education must now concentrate its energies. If machines can provide information, then schools must focus on cultivating understanding. If AI can generate content, then teachers must emphasise interpretation. The value of education is shifting from knowledge acquisition to knowledge application.
The HBS guide introduces a concept that should become central to educational discourse: “change fitness.” This refers to the ability of individuals and organisations to continuously adapt to shifting technological and social environments. It is not a single skill, but a composite of curiosity, flexibility, data literacy, and the capacity to redesign one’s approach to work.
In practical terms, this means that learners must be prepared not for a fixed body of knowledge, but for a lifetime of learning, unlearning, and relearning. The Competency-Based Education (CBE) gestures toward this ideal, but its implementation often remains tethered to old habits of content coverage and examination performance. Change fitness demands something deeper: an education system that is itself adaptive, responsive, and informed by real-time data.
And here lies a paradox. Even as AI promises to make decision-making more efficient, it also introduces new complexities. The guide distinguishes between predictive AI, which enhances accuracy and consistency, and generative AI, which fosters creativity and diversity of thought. These two forms of intelligence pull in different directions. An overreliance on predictive systems risks producing rigid, standardised outcomes. An overreliance on generative systems risks sacrificing rigour for novelty.
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Education systems must therefore navigate a delicate balance. Assessment frameworks, for instance, must move beyond narrow standardisation without descending into subjectivity. The ongoing conversations at forums such as the KNEC Annual Educational Assessment Symposium are timely in this regard. The challenge is no longer simply to collect data on learner performance, but to use that data intelligently—to inform teaching, guide interventions, and shape policy.
Yet the integration of AI also brings risks that cannot be ignored. There are security concerns, as AI systems become targets and tools of sophisticated cyber threats. There are cognitive risks, including bias and the now well-documented phenomenon of AI “hallucinations,” where systems generate plausible but incorrect information. And there are strategic risks, particularly when institutions automate decisions without fully understanding the underlying processes.
These risks point to a critical need: AI literacy must go beyond technical proficiency. It must include critical thinking, ethical reasoning, and an awareness of the limitations of machine intelligence. Learners must not only know how to use AI, but when to question it.
Perhaps the most counterintuitive insight from the HBS guide is that, as AI becomes more powerful, human qualities become more valuable. Judgment, empathy, contextual understanding, and ethical reasoning are not diminished by technology; they are amplified in importance. In a world where machines can process data at unprecedented speed, the ability to make sense of that data becomes the defining human advantage.
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This has profound implications for how we think about teaching and learning. The teacher is no longer merely a transmitter of knowledge, but a curator of meaning. The classroom is no longer a site of information delivery, but a space for dialogue, inquiry, and critical engagement. Assessment is no longer a measure of what learners know, but of what they can do with what they know.
At a systemic level, education must also respond to the changing nature of work. AI is not eliminating jobs wholesale, but it is reshaping them. Routine, repetitive tasks are increasingly automated, while demand grows for roles that require analytical thinking, creativity, and complex problem-solving. This shift calls for an education system that prepares learners not just for employment, but for adaptability, innovation, and, increasingly, entrepreneurship.
The Kenyan context adds another layer of urgency. Our schools already grapple with large class sizes, limited resources, and significant disparities in access and quality. The introduction of AI into this landscape could either exacerbate these inequalities or help to address them, depending on how it is managed. If leveraged wisely, AI can support personalised learning, enhance teacher effectiveness, and improve data-driven decision-making. If adopted uncritically, it risks deepening existing gaps.
Ultimately, the central lesson of AI in 2026 is deceptively simple: access to information is no longer the problem. The challenge is making sense of it. Education systems must therefore pivot from accumulation to application, from content to competence, from data to decision-making.
The lament of Hosea may still hold, but its meaning has evolved. We are no longer destroyed for lack of knowledge. We are at risk of being overwhelmed by it. The task before educators, policymakers, and institutions is not to provide more information, but to cultivate the wisdom to use it well.
By Ashford Kimani
Ashford Kimani teaches English and Literature in Gatundu North Sub-county and serves as Dean of Studies.
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