By Vardhan Dugar, Principal Group Engineering Manager - Microsoft, India
Generative AI can explain a difficult concept in a new way, suggest practice questions, or help a teacher prepare a lesson. But a polished answer is not the same as learning, and a faster workflow is not automatically a better one. For education leaders, the real question is how to use AI to advance learning while protecting the people and relationships at the heart of education.
That takes more than buying a tool. It takes a clear educational purpose, educator and learner involvement, appropriate safeguards, and evidence that the system is helping. The guiding principle should be augmentation, not substitution: AI can extend human expertise, but people remain accountable for teaching and consequential decisions.
Start with the learning need
Begin with a challenge educators and learners actually face—not with a vendor demonstration. Is feedback arriving too late? Are students missing opportunities to practice? Is an administrative task taking time away from teaching? Define the problem, the people affected, and what improvement would look like before selecting a system.
A tutoring assistant, for example, could ask a student to explain a step, offer a hint, and check understanding before revealing a solution. That design encourages active thinking instead of outsourcing it. Teachers should be able to shape the experience, inspect the output, and redirect the learner to reliable course materials when the system is uncertain.
These benefits are possibilities, not guarantees. The U.S. Department of Education highlights AI’s potential while emphasizing the need for human oversight and stronger evidence about its effects. A school should not treat logins, completed assignments, or user satisfaction as proof of learning. It should compare progress against a baseline and examine whether gains are sustained and shared across learner groups.
Teach students to question the machine
Preparing students for an AI-enabled world means more than teaching prompt-writing. Learners need to check claims against credible sources, recognize fabricated citations, notice bias, protect personal information, and understand when a task calls for their own reasoning. They should also know when AI use is allowed and how to disclose it.
Build these habits into ordinary coursework. Ask students to verify an AI-generated claim, trace it to an original source, and explain what the system missed. Make AI literacy age-appropriate, accessible, and responsive to language and culture. UNESCO’s guidance similarly emphasizes human agency, inclusion, and cultural and linguistic diversity.
Protect trust before data is shared
Schools hold sensitive information, from academic records to disability accommodations. Before a tool is used, map what information it receives, where it goes, who can access it, how long it is kept, and whether it may be used to improve models. Collect only what is necessary, and do not send identifiable student information to an unapproved service.
A “private” or “enterprise” label is not a substitute for checking the actual contract and configuration. Review retention and training terms, subprocessors, access controls, encryption, incident reporting, deletion, and exit provisions. Check integrations and plug-ins too. Security planning should address account compromise, data leakage, prompt injection, and unsafe tool access, with a named owner and clear response path.
Be just as careful with promises about ownership. Rights in AI-assisted work can depend on law, product terms, and human contribution. Set clear institutional rules for attribution, reuse, and acceptable student and staff use instead of promising universal ownership.
Put guardrails where the stakes are
Requirements vary with jurisdiction, learner age, data, institutional role, and purpose. FERPA and COPPA may matter in different U.S. contexts; GDPR and the EU AI Act may apply in Europe. A consent form or age gate alone does not settle compliance. Involve privacy, legal, security, accessibility, procurement, and academic leaders early, and document how a use will be governed.
The consequences matter. An AI tool that helps brainstorm classroom examples is not equivalent to one that influences admissions, placement, grades, progression, or test monitoring. The EU AI Act lists certain education and vocational-training uses as high-risk. For consequential decisions, require appropriate validation, human review, a way to challenge or correct outcomes, and a clear accountable owner. A model’s explanation is not proof that its decision is fair.
Look beyond privacy and bias. Test accessibility for learners with disabilities, language quality, cultural representation, unequal access to devices and connectivity, and the risk of inaccurate or harmful responses. The NIST AI Risk Management Framework offers a practical cycle—Govern, Map, Measure, and Manage—for identifying and addressing risks over time. Decide in advance who can pause a system if it causes harm.
Make the economics work for learners
The real cost is more than tokens or subscriptions. Include integration, security, training, support, evaluation, accessibility, and the effort needed to change vendors. Estimate both a pilot and a scaled deployment. Track the cost of a meaningful learning or educator outcome, not just the price of each query.
Choose models based on tested task quality and cost. A smaller model is not automatically accurate enough for factual work; use approved, current sources and test outputs in the intended setting. Caching can reduce expense, but stale answers need version checks. Usage limits should protect service availability without denying some students meaningful access: provide alternatives, an exception process, and monitoring for unequal effects.
Pilot, learn, and be willing to stop
A responsible rollout can start small. Select one educational need, involve the people affected, map data and risks, and set measures before the pilot. Train educators, use an approved tool, and ensure students know where to get human support. Do not use an unvalidated experiment for high-stakes decisions.
Evaluate learning progress, student reasoning, educator workload, accessibility, output errors, incidents, and total cost. Compare results with a baseline and review differences across relevant groups. State what the pilot cannot prove. Expand only when evidence and safeguards justify it; monitor changes in the model, curriculum, costs, and complaints. If the tool fails its purpose or creates harm, fix it, pause it, or stop using it.
Keep people at the center
Teachers bring context, care, and professional judgment that a system cannot replace. Involve them, students, families, and support professionals in choosing and evaluating AI. Provide time for training and co-design, and account for the new work of checking and correcting outputs. Transformation is not a software rollout; it is a continuing cycle of learning and adjustment.
Conclusion
AI can help education become more responsive, but adoption is not impact. Leaders should start with a learning need, protect student agency and data, include the people affected, measure results honestly, and scale only when value is clear and sustainable. Used with care, AI can amplify human potential. The responsibility for making that promise real belongs to people.
