Artificial intelligence is quickly moving from novelty to infrastructure. In many schools and universities, AI is already shaping writing, assessment, tutoring, research, student support, and administrative decision-making. UNESCO’s 2026 report, The Algorithm in the Room: Seeing and Confronting the Implications of AI for the Future of Education, argues that educational leaders should resist treating this transformation as inevitable.
The more important leadership question is not simply, “How do we adopt AI?” but “What purposes of education do we want AI to support, and what should remain distinctly human?” In some cases, the broader conversation might also include questions such as, “What is the purpose of our school, program, or course?”
For school and district leaders, higher ed administrators, and instructional technology leaders, the report offers several immediate action items.
1. Start with educational purpose, not the tool
One of the primary principles of effective educational technology has always been to focus on the instructional goal rather than the technology. The UNESCO report repeatedly returns to the problem of alignment. AI systems are designed to optimize toward goals, but schools are complicated, and not all educational goals can be easily quantified in a dashboard or reduced to a simple metric.
The immediate leadership implication is straightforward: before approving a new AI product, ask what educational problem it is intended to solve. Leaders should require a clear instructional or administrative rationale rather than adopting tools simply because of a promise of efficiency or personalization. A useful leadership question is, “What do we want students or educators to be better able to do because this tool exists?”
2. Audit where algorithms are already making decisions
UNESCO’s first recommended horizon is to “see the algorithm.” Educational leaders may be surprised by how many algorithmic systems are already embedded in their institutions, from adaptive learning tools and plagiarism detection systems to enrollment platforms, analytics dashboards, recommendation engines, and automated assessment tools.
An immediate action is to conduct an institutional AI and algorithm inventory. Identify which systems are being used, what data these collect, what decisions they influence, and whether those decisions can be challenged by a human.
This is especially important because UNESCO warns that algorithmic decisions can be opaque, difficult to appeal, and capable of reinforcing existing inequities.
Algorithms need to be tested from time to time. Do not assume that the conditions that allowed an algorithm to be effective a decade ago are still in place.
3. Protect human agency in teaching and learning
One of the report’s strongest warnings concerns the gradual transfer of intellectual work from people to machines. UNESCO describes the possibility of a “classroom AI doom loop,” in which AI generates assignments, students use AI to complete the work, AI evaluates the responses, and automated systems record the grades. The issue goes beyond academic dishonesty and into whether meaningful human learning is taking place.
Leaders should therefore ask instructors to identify the parts of learning that must remain human. These might include forming questions, interpreting evidence, explaining reasoning, engaging in discussion, solving unfamiliar problems, reflecting on mistakes, and defending conclusions. Assessment seems like a key process that needs to remain under human agency. AI can support those processes, but it should not replace any.
4. Rethink assessment instead of relying on AI detection
UNESCO’s discussion of pedagogy and assessment suggests that educational institutions need to move beyond the search for increasingly sophisticated AI detectors.
If AI can produce conventional academic products, leaders should help educators reconsider what those products are intended to demonstrate. Assessment should increasingly make student thinking visible. That could mean more oral explanation, iterative drafts, demonstrations, presentations, authentic projects, reflective commentary, in-class problem solving, and assignments in which students document how they reached a conclusion.
The leadership challenge is to support redesign of assessment to disclose, document, and reflect on AI use by students.
5. Make AI literacy broader than prompt engineering
Many current AI literacy initiatives focus heavily on how to use generative AI effectively. UNESCO suggests that students need something more substantial.
AI literacy should include understanding how algorithmic systems work, how training data influence outputs, how bias can emerge, why hallucinations occur, whose knowledge may be overrepresented or absent, and how commercial incentives shape technology. Students should learn to question AI outputs, not simply generate better ones.
The UNESCO report raises concerns about linguistic and cultural flattening. Dominant languages and highly digitized knowledge traditions may receive disproportionate representation in large language models, while local, indigenous, and less digitized perspectives risk being further marginalized in some cases.
6. Establish clear human-rights and privacy guardrails
UNESCO emphasizes that AI use in education should be grounded in dignity, non-discrimination, participation, equity, informed consent, and protection—especially when children or vulnerable learners are involved.
Educational leaders should immediately review policies governing student data, AI account creation, vendor access, automated decision-making, and the use of personally identifiable information in generative AI platforms.
Institutions also need a clearly articulated guiding principle that consequential decisions affecting students should not be delegated entirely to automated systems.
7. Strengthen teachers rather than designing around them
One of the most important leadership messages in the report is that AI should not become an excuse to hollow out educational institutions. UNESCO argues that hopeful AI futures depend on strengthening libraries, arts and humanities programs, teacher development, and public educational infrastructures.
To achieve this, professional learning should move beyond one-time workshops on individual AI products. Educators need ongoing opportunities to examine AI’s instructional, ethical, cultural, and assessment implications. Teachers should be positioned as designers of learning environments, not simply supervisors of automated systems.
8. Move from consumers to shapers of AI
UNESCO’s final horizon is the most ambitious: “rewrite the algorithm.” The report encourages educational communities to participate actively in shaping AI rather than simply consuming commercially produced systems. It highlights possibilities such as community-trained models, citizen science, indigenous-language projects, locally developed small language models, and collaborative rather than purely individualized AI tools.
For educational leaders, the immediate step may be modest: involve teachers, students, librarians, technology staff, and community members in AI governance and purchasing decisions. Those who experience the consequences of educational technology should have a voice in determining how it is used.
A leadership agenda for right now
Perhaps the most useful takeaway from The Algorithm in the Room is that educational leaders still have agency. AI is not an external force that schools must simply accommodate.
UNESCO’s three-part framework provides a practical leadership sequence: see the algorithm, steer the algorithm, and ultimately help rewrite the algorithm.
For leaders, that means conducting an AI inventory, establishing governance and privacy expectations, supporting assessment redesign, expanding AI literacy, protecting meaningful human interaction, and ensuring that technology remains subordinate to educational purpose.
The most important AI strategy may therefore begin with a question that has very little to do with technology: What kind of education are we trying to provide, strengthen, and create? Once leaders can answer that clearly, decisions about AI become considerably easier.
Of course, what kind of education we should be providing might be the most difficult question of all.
