AI in education is changing the teacher’s role from delivering information to developing critical thinking, ethical judgment, resilience and human connection. As AI becomes a powerful learning tool, teachers may become more important as mentors and guides.

Every few decades, technology forces higher education to step in front of an existential mirror. When search engines made the world’s information queryable in milliseconds, critics prematurely declared the traditional classroom obsolete. Today, as Generative AI (Artificial Intelligence) tools evolve from search interfaces into systems capable of explaining corporate finance models, writing complex code, and breaking down multi-variable calculus step-by-step, we face a far more provocative question: If AI can explain everything, what is actually left for a teacher to teach?

AI in Education Is Changing What Teachers Need to Teach

For decades, higher education was built on an implicit assumption that the teacher’s primary job was the delivery of content. The educator was the traditional authority figure holding access to knowledge and transmitting it to passive recipients. But if education is reduced merely to information delivery, AI will indeed render the traditional teacher redundant. As academic leaders navigating a multidisciplinary university environment, we know that information is not wisdom, and an explanation is not true understanding. As AI becomes a ubiquitous, hyper-efficient personal tutor, the role of the teacher is not shrinking; it is undergoing a profound and necessary elevation.

Generative AI models are exceptional at providing fast answers, but they are fundamentally indifferent to truth, context, and nuance. They synthesise probabilistic patterns rather than human meaning. In a time when students can produce a well-written study of a business case or legal precedent in a matter of seconds, the main responsibility of educators is to teach students how to evaluate answers rather than how to obtain them. In the era of artificial intelligence, critical thinking is now about rigorous investigation rather than knowledge retrieval. Instructors must teach students how to analyse AI outputs, including how to spot algorithmic bias, find logical fallacies, confront minute errors, and comprehend the moral trade-offs and human costs associated with important choices. An algorithm cannot match the human mind’s ability to navigate ambiguity, which is necessary to develop this level of critical judgment.

Higher education is also a very emotional and social process. Vulnerability is necessary for learning; a pupil must first own their ignorance in order to acquire new information. While an AI can adjust to a student’s cognitive tempo, it is unable to recognise the faint spark of inquiry in a quiet seminar room, read the exasperation on a student’s face, or detect a sudden lack of confidence before a significant presentation. Teachers work as mentors, fostering character, ethical responsibility, and resilience. They foster collaborative environments where leadership thrives, set an example of sensitivity, and encourage discussion among peers who hold different opinions. A machine can outline the technical concepts of corporate governance or bioethics, but only a human mentor can instil the moral compass required to lead responsibly in an unpredictable world.

If AI can explain the what and the how, the teacher remains the master of the why.

Instead of replacing teachers, AI presents a unique opportunity by relieving them of repetitious explanations and mundane administrative duties. Teachers might recover their primary role as learning architects by delegating foundational drills and basic synthesis to automated technologies. Project-based challenges, real-world simulations, and interdisciplinary discussions where human collaboration is unavoidable are replacing passive lectures in forward-thinking educational institutions. In order to help students identify their unique skills, intellectual interests, and sense of agency, they concentrate on posing open-ended questions that lack tidy, algorithmic solutions.

As we evaluate the future of our educational institutions, we should not view AI as a competitor to the teacher, but as a clarity-enforcing mirror. If AI can explain the what and the how, the teacher remains the master of the why. As technology democratises access to knowledge at an unprecedented scale, the human touch in education becomes more valuable, not less. The future belongs not to machines that hold all the answers, but to human beings who can think critically, act ethically, collaborate empathetically, and lead with purpose. Guiding young minds to that destination is something only a teacher can do.

Researchers from MIT and autonomous vehicles technology company Motional have developed a system that could help humans understand what is happening inside an autonomous vehicle when it makes a decision. Called the Concept-Wrapper Network, or CW-Net, it translates parts of the vehicle’s decision-making into concepts humans can understand, such as “approaching stopped vehicle” or “close to cyclist.”

In one test, an autonomous vehicle repeatedly stopped when approaching a cyclist. The safety driver assumed the vehicle had detected the cyclist. CW-Net revealed otherwise: the system was not properly configured to detect the cyclist. The vehicle had stopped because its emergency-braking procedure was triggered when it got too close. The difference is important. If a human knows that the vehicle has failed to recognise a cyclist, they can slow down or take control sooner.

It raises a larger question for autonomous driving: when a vehicle makes a mistake, can humans understand the mistake quickly enough to respond?

When the Decision Comes from Software

Autonomous vehicles use cameras, lidar and other sensors to interpret their surroundings. Machine-learning systems then use that information to plan the vehicle’s movement. But deep-learning models can be difficult to interpret. Engineers may see what the vehicle detected and what it eventually did without having a clear view of the reasoning connecting the two.

That problem has already appeared in crash investigations. In March 2018, an Uber test vehicle operating with an automated driving system struck and killed a pedestrian in Tempe, Arizona. The US National Transportation Safety Board found that the system had detected the pedestrian but classified her at different points as an unknown object, a vehicle and a bicycle. Its predictions about her path also changed.

The investigation showed why an autonomous-vehicle crash can require a different kind of analysis. Investigators may need to determine not just what happened, but what the automated system detected, how it classified an object and what it predicted before deciding how the vehicle should move.

What Happens After a Crash?

The US National Highway Traffic Safety Administration requires specified manufacturers and operators to report certain crashes involving automated driving systems and Level 2 driver-assistance systems. For covered automated-driving-system crashes, the reporting order includes cases in which the system was engaged within 30 seconds of a crash meeting specified injury, fatality, towing or property-damage criteria.

Such reports give regulators information to identify safety concerns and investigate potential defects. But a crash report does not necessarily reveal every step in an AI system’s decision-making. NHTSA also notes that information in initial reports can be incomplete or unknown and may later be updated.

A 2024 case involving Cruise showed another side of the problem. NHTSA reached a consent order with Cruise over incomplete reporting related to crashes involving its automated-driving systems. Cruise agreed to pay a 1.5 million dollars civil penalty and submit a corrective action plan. Reuters reported that one of the cases involved a pedestrian who had first been struck by another vehicle and was then hit and dragged by a Cruise robotaxi.

The case was about reporting rather than AI interpretability. But it highlighted the importance of having reliable information when automated systems are involved in crashes.

Why Knowing “Why” Matters

Consider a vehicle that suddenly brakes. A passenger might assume that it has detected a pedestrian or another obstacle. But the same action could result from the system misunderstanding its surroundings and triggering an emergency response.
From outside, the vehicle’s behaviour looks the same. For the person supervising it, the difference can determine whether they trust the system or intervene.

CW-Net is designed to make some of the concepts influencing the vehicle’s planning visible. The researchers integrated the system into the machine-learning planner rather than generating an explanation only after the decision had been made. They say this makes the explanations causally connected to the vehicle’s behaviour.

The system was trained using 130 million examples of self-driving scenes with labelled concepts. In tests with a Motional autonomous vehicle on a private track, the explanations helped safety drivers predict vehicle behaviour more accurately. A larger simulation study using real driving situations from Las Vegas produced similar results among non-expert participants.

That does not mean CW-Net prevents crashes. Its potential value is more immediate: it can give humans information that may help them recognise a failure and give engineers another way to investigate it.

India is Moving Towards More Connected Vehicles

India’s immediate road-safety challenge is still conventional road crashes. At the same time, India is introducing technologies that allow vehicles to exchange and process more information about their surroundings. In August 2026, the Ministry proposed phased implementation of vehicle-to-vehicle communication. Under the draft proposal, L, M and N category vehicles manufactured from October 1, 2028 would be required to have V2V systems conforming to AIS-230. The proposed systems would allow vehicles to exchange information including speed, position, direction and acceleration.

The government says such communication could support warnings for sudden braking, forward collisions, unsafe lane changes and approaching emergency vehicles. V2V is not autonomous driving. It does not transfer control of the vehicle to an AI. But it is part of a broader shift towards vehicles relying more heavily on sensors, software and communication systems.

Reuters also reported in June that India had removed licensing requirements affecting spectrum used by automotive radar and V2X technologies, potentially making it easier to deploy some crash-avoidance and connected-vehicle technologies. As these systems become more sophisticated, understanding their failures becomes important too.

The Indian Question

An autonomous vehicle operating in India would have to navigate roads shared by cars, buses, motorcycles, bicycles and pedestrians, often in complex and unpredictable situations. That does not mean Indian roads are inherently unsuitable for autonomous driving. It does mean that automated systems need to be tested in the environments in which they are expected to operate, including unusual situations that can challenge their perception and decision-making.

This is where technologies such as CW-Net could eventually have a role. If a vehicle responds incorrectly, an understandable record of what influenced its decision could help a safety operator recognise the problem. The same information could help engineers identify weaknesses in the system and work on correcting them. For regulators and investigators, such information could also become useful when reconstructing what happened after a crash.

The goal, however, should not be to make people trust autonomous vehicles simply because the systems can explain themselves. An explanation is useful only when it is accurate and helps a person make a better decision. A self-driving car will still have to make the right decision. But when it does not, being able to understand where the decision went wrong could help humans respond sooner—and help engineers make the next system better.

Meta has agreed to pay up to 18 billion dollars to settle a sweeping legal challenge brought by nearly all U.S. states over allegations that Facebook and Instagram were designed to keep children and teenagers engaged despite risks to their wellbeing. Announced on August 26, the settlement ends a major federal trial in California and requires Meta to introduce significant changes to how teenagers use its platforms. Meta has not admitted wrongdoing.

More importantly, the agreement reflects a changing approach to online child safety: platforms, rather than parents alone, are increasingly being held responsible for how their products affect children.

What will Change?

Under the agreement, teenagers will receive a default two-hour daily limit across Facebook and Instagram, although parents can override it. Meta will also introduce a default Night Mode, restricting use between midnight and 6 a.m. Notifications will be muted during school hours, from 8 a.m. to 3 p.m., except for direct messages and safety-related alerts.

Teenagers will receive reminders after extended periods of use and at certain daily-use thresholds. Meta will also strengthen age-assurance measures aimed at keeping children under 13 off its platforms. The measures are expected to remain in place for 10 years, with independent oversight of Meta’s compliance.

Financially, the settlement could reach 18 billion dollars. About 12.7 billion dollars is guaranteed over 10 years, while another 5.3 billion dollars is conditional on TikTok and YouTube adopting specified safety measures and making corresponding payments.

Tackling the Screen Time

The lawsuits were not simply about teenagers spending too much time online. States accused Meta of designing features that encouraged prolonged engagement while allegedly downplaying risks to young users. They also raised concerns over the collection and use of personal information from children under 13. That puts the focus on the architecture of social media itself.

Recommendation systems, notifications, engagement metrics and endless feeds can influence how long users remain on a platform. The larger question is whether companies should be responsible when those design choices contribute to harm among young users. The settlement pushes the debate towards platform responsibility rather than individual responsibility.

Is Two Hours Enough?

A daily limit may reduce screen time, but it does not necessarily address what happens during those two hours. What content teenagers encounter, how algorithms recommend it and how platforms respond to vulnerable users remain important questions.

Meta’s advertising-driven business model also remains intact. The settlement does not fundamentally change the commercial incentives behind user engagement. But can social media be made safer simply by limiting how long children use it?

The Age-verification Dilemma

The agreement also highlights another challenge: how platforms determine who is a child. Stronger age assurance can make it harder for children to bypass restrictions. But systems involving facial analysis, identity checks or other forms of age estimation can create new privacy risks.

Protecting children online, therefore, also requires deciding how much information platforms should collect to establish a user’s age.

A Test for the Industry

Although the settlement applies to Meta’s U.S. services, its implications extend beyond the company. Meta is calling on TikTok and YouTube to adopt similar safeguards, potentially pushing child-safety measures towards an industry-wide standard.

The settlement could ultimately prove to be less about the 18 billion dollars than about who bears responsibility for children’s digital lives. For years, parents have been asked to manage screen time, adjust settings and monitor what children see online.

Meta’s settlement suggests a different expectation: if platforms are built in ways that can affect children, the platforms themselves may have to change. The next decade will show whether that shift can produce a genuinely safer social-media environment for young users.