National Technology Day Agentic AI & Ethics in Education

Ten years ago, the biggest technology concern in schools was whether smartphones were becoming too much of a distraction. Today, we are grappling with something far more complex—intelligent systems making decisions about students’ futures, often without anyone fully understanding how or why.

The rise of Agentic AI - these self-directed systems that can think through problems, take action, and learn as they go – forces us to rethink what teaching even means anymore. It's not theoretical anymore. its's happening in learning management systems, in adaptive tutoring platforms, in the background of tools we already use.

Ethical AI in Schools

Ethical AI in education can be understood simply: technology should make us better teachers, not replace what makes teaching meaningful.

This means:

• Keeping students as active thinkers—AI can support learning, but it cannot replace thinking.

  
  

• Being transparent—if we cannot explain how a system works to students or parents, we should not use it.

  
  

• Not letting technology widen gaps between students, it should level the playing eld, not tilt it further.

  
  

• Keeping human connection at the heart of what we do because relationships maer more than efficiency.

  
  

• Protecting student data like we protect our children’s information.

  
  

What is Agentic AI?

Let’s break it down this way. Basic AI tools, the ones we're more familiar with, are like calculators on steroids. You give them a task, they execute it.“Generate a quiz on photosynthesis” — and boom, you get a quiz.

Agentic AI is different. It's more like hiring a thoughtful teaching assistant. You give them a goal — “Track our weaker math students this semester, figure out what's holding them back, and suggest ways we can help” — and they work independently toward that goal. They observe patterns, make decisions, take actions, and adjust based on outcomes.

The power is undeniable. The responsibility is massive.When something works on its own, without constant human direction, we need to understand its motive because it directly impacts our students.

Why the Conversation Matters

Ethics become urgent when systems have real power. Let’s explore some scenarios that might sound hypothetical—but aren't:

• AI reviews grades, labels students as low potential, limits opportunities without scrutiny, reinforcing historical biases and steering them away from paths their demographic rarely pursued.

  
  

• A system decides which students need intervention, resources, or who gets pushed toward remedial versus advanced tracks—without a parent’s awareness of the process.

  
  

• Here’s the uncomfortable truth: schools already use these systems; most parents and students don’t realise continuous evaluation and categorisation are happening.

  
  

The Biggest Risks

• Invisible Bias at Scale: AI trained on biased data amplifies inequality. If girls historically scored lower in STEM, AI predicts continued struggle, reinforcing bias invisibly and creating self-fulfilling outcomes that are difficult to detect and challenge.

  
  

• Reducing Humans to Data: Machines see patterns, not potential or resilience. Letting autonomous systems decide students’ futures risks replacing nuanced human judgment with narrow algorithms.

  
  

• Eroding Trust: When students feel a “black box” is judging them, it damages the psychological safety that learning requires. Some students shut down when they think a system has already decided their future.

  
  

• Data as Commodity: Student data is valuable. Vendors want it. Schools need to ensure that privacy isn’t traded for better-looking tools or unnecessary data collection.

  
  

• No Clear Appeal: When AI makes a wrong or harmful decision about a student, who fixes it? What’s the process for saying “No, that’s not right”? Often, there isn’t one.

  
  

Accountability: Who’s Actually Responsible?

Accountability gets murky in complex systems, often by design. Yet one truth stands: responsibility lies with us—not vendors or algorithms.

School management must be accountable for AI implementation. Educators should understand systems before using them and inform parents about data collection. Students should have appeal rights, with human review of AI-led decisions. Regular audits must check for disparities or anomalies.

Preventing bias requires ongoing vigilance: question data sources, test for impact, ensure human oversight for critical decisions, and include diverse voices to avoid blind spots.

The Smartphone Paradox

As AI tools become central to learning, blanket smartphone bans risk becoming gatekeeping. In affluent schools, bans are an inconvenience; in others, where students rely on phones for internet access, they become a barrier.

Research is mixed—focus may improve and anxiety may reduce, but effects are limited, with usage often shifting to home.

The solution is education, not prohibition: teach critical consumption, create screen-free safe spaces, strengthen mental health support, and engage families in honest conversations about technology use.

The Indian Context

In many Indian homes, especially in rural areas, mobile phones are the primary source of internet access. Schools with fewer laptops hit poorer students hardest. With learning platforms designed for smartphones, bans restrict access and deepen inequity.

A Realistic Path Forward

Know your students. What percentage have reliable home internet? How many rely on phones as their main access point? Educate your community. Have real conversations about AI with staffs and parents, not fear-mongering, but genuine understanding. Finally, be selective. Not all "AI tools" are valuable. Figure out which ones align with what you're actually trying to teach.