Exemplification Essay Examples & Samples for Students

Artificial intelligence arrived in classrooms quietly. It appeared first as a spelling checker, then as a translator, and finally as a tutor that answers questions at midnight. Students and teachers now use these tools dozens of times a week, often without thinking of them as artificial intelligence at all. Critics worry that the technology encourages shortcuts and weakens independent thought, while supporters call it the most useful teaching aid since the printed textbook. Both sides are arguing about outcomes that are already visible. Artificial intelligence has changed education in three concrete ways: it has altered how students study, how instructors evaluate work, and who can reach a classroom at all.

The first change is visible in daily study habits. Duolingo adjusts the difficulty of each language lesson to the learner’s recent mistakes, so two students working through the same unit see different exercises. Khan Academy’s Khanmigo, released in 2023, answers a student’s algebra question with a follow-up question rather than a solution, imitating the way a human tutor works. Quizlet schedules review sessions around the cards a student keeps missing. None of these products existed a generation ago, and none of them requires a teacher to be present. A student preparing for an exam at eleven at night now has a study partner that responds in seconds.

Assessment has changed just as visibly. Turnitin compares a submitted paper against a database of published work and student submissions, flagging overlapping passages before an instructor reads a single sentence. Gradescope groups identical answers on a mathematics exam so that one comment can be applied to every student who made the same error. Both tools handle the mechanical part of evaluation, and both change what an instructor spends time on. Grading did not disappear; the hours once spent checking arithmetic now go to written feedback.

The third change is a question of access. In 2016, researchers at Georgia Tech introduced Jill Watson, an artificially intelligent teaching assistant that answered student questions in the discussion forum of an online graduate computing course. Many students did not realize she was software. Live captioning now lets a deaf student follow a lecture in real time, and machine translation lets a first-generation student read an assigned article in the language they think in. Each of these examples removes a barrier that had nothing to do with a student’s ability.

Taken together, the three changes describe a classroom that looks familiar and works differently. The tools have not replaced teachers, and there is no sign that they will. What they have done is redistribute effort, away from repetition and toward the parts of teaching and learning that require a person. Artificial intelligence in education is no longer a prediction. It is a set of ordinary tools with ordinary names, already installed on the devices students carry to class.

Every generation has invented a new way to stay in touch, from the letter to the telephone to the text message. Social media is the latest, and it is the first to keep a permanent, public record of the relationship itself. A friendship that once existed only in conversation now exists in comments, tagged photographs, and a visible count of shared connections. That record does more than preserve a relationship; it shapes how the relationship is formed, maintained, and ended. Three ordinary features of social platforms—the public status, the algorithmic feed, and the read receipt—have rewritten the rules of modern relationships.

Consider first how relationships begin and get announced. Facebook’s relationship status turned a private understanding into a declaration, and “It’s complicated” became a joke precisely because it named something couples had never had to name before. Dating applications such as Hinge and Tinder go further. They present a person as a set of photographs and prompts, and they reduce the first decision about a stranger to a single swipe. The introduction, once handled by a mutual friend, is now handled by an interface.

Next, the feed decides what happens after that first contact. An algorithm selects which friends a person sees, and it favors the accounts they already interact with most. A close friend who moves abroad and posts rarely can disappear from view entirely, while an acquaintance who posts daily becomes a constant presence. Distance no longer determines closeness; posting frequency does. Many people describe drifting away from a friend without a single argument, and the feed explains a good deal of that drift.

Finally, endings have changed. “Ghosting” entered common use because platforms made silence a complete and available response: a message can be delivered, marked as read, and never answered. The read receipt turned a private act into evidence. Blocking, muting, and unfollowing offer three different degrees of separation, each visible to a different audience, and none of them requires a conversation. A relationship can now end without either person saying that it has.

Taken together, these three features do not prove that social media has damaged relationships, and the evidence rarely supports so simple a conclusion. What they show is that the tools have added new steps to an old process. The status announces, the feed selects, and the receipt records. A generation raised inside those features learned a version of friendship and romance that includes them, and the platform has entered the relationship itself.

Environmental news is dominated by loss: hotter summers, thinner ice, and species that will not return. That coverage is accurate, and it is incomplete. Over the past fifty years, several conservation efforts have not merely slowed damage but reversed it, and they did so under ordinary political conditions with ordinary budgets. Their common feature is worth noticing. Conservation strategies succeed when they combine an enforceable legal rule, a direct economic incentive, and a measurable target, as the ozone layer, Costa Rica’s forests, and the bald eagle each demonstrate.

The Montreal Protocol, signed in 1987, is the clearest case of a legal rule producing a measurable result. The agreement required its signatories to phase out chlorofluorocarbons, the industrial chemicals responsible for thinning the ozone layer. Manufacturers resisted, then adapted, because the treaty set firm deadlines and applied to every major producer. Decades later, the ozone hole over Antarctica is smaller than it was at its worst, and atmospheric scientists expect it to close within this century. The rule was narrow, it was enforceable, and it was tied to a single quantity that could be measured from a satellite.

Economic incentives explain the recovery of Costa Rica’s forests. In the 1990s, the country began paying landowners to leave forests standing rather than clear them for cattle, funding the payments partly through a tax on fuel. Farmers who had every financial reason to cut trees suddenly had a financial reason not to. Forest cover, which had fallen to roughly a quarter of the national territory, has since climbed back above half. No new technology was involved. The incentive simply changed direction.

The return of the bald eagle shows what a measurable target adds to the other two. After the United States banned agricultural DDT in 1972 and gave the bird federal protection, the nesting population in the lower forty-eight states climbed from a few hundred pairs to tens of thousands. Because the target was countable, progress could be verified rather than assumed, and the eagle was removed from the endangered species list in 2007. A goal that can be counted is a goal an agency can be held to.

These three cases differ in scale, geography, and cost, but not in structure. Each set a rule that could be enforced, gave the people affected a reason to comply, and defined success as a number someone could check. Conservation works when it stops asking people to care more and starts making the better choice the easier one. The examples show that this strategy is available, tested, and unglamorous, which may be why it is so often overlooked.

Leadership training tends to treat empathy as a soft addition to the real work of setting direction and making decisions. The record suggests the opposite. In organizations under pressure, leaders who accurately understand what their people are experiencing make better decisions than leaders who do not, because they are working from better information. Empathy is not a substitute for judgment; it is an input to it. Three cases, drawn from a corporation, a government, and a factory floor, show empathy operating as a practical tool rather than a personal virtue.

When Satya Nadella became chief executive of Microsoft in 2014, the company was widely described as internally competitive and slow to cooperate across divisions. Nadella made empathy an explicit management theme, wrote about it at length in his 2017 book Hit Refresh, and tied it to a shift from a know-it-all culture toward a learn-it-all one. The change was not sentimental. Teams that had withheld information from one another began sharing it, and Microsoft’s expansion into cloud services depended on exactly that cooperation. Understanding why engineers protected their territory was the first step toward changing the conditions that made them protect it.

Jacinda Ardern’s response to the Christchurch mosque attacks in March 2019 offers a second case. Within days, the New Zealand prime minister met the victims’ families, wore a headscarf out of respect for the community she was addressing, and refused to say the attacker’s name in public. Her government passed a ban on military-style semi-automatic weapons within a month. The gestures and the legislation were not separate acts. Ardern’s attention to what the Muslim community actually needed shaped both the speed and the content of the policy response.

Alan Mulally’s turnaround at Ford illustrates the same principle at a smaller scale. Mulally asked his executives to color-code the status of their divisions in a weekly review, and for weeks every chart came back green while the company lost money. When one executive finally marked a program in red, Mulally applauded. He had understood that his managers were reporting what they believed was safe to report, so he changed the meeting rather than the managers. The honest reporting that followed is what made the recovery possible.

A software corporation, a national government, and a car manufacturer share little else. In each case, though, a leader who took the trouble to understand other people’s experience gained information that was otherwise unavailable, and then acted on it. Empathy did not make these leaders gentler. It made them better informed, and the decisions that followed were stronger for it.

In 2013, Yahoo told its remote employees to return to the office, and the decision made headlines precisely because remote work was still unusual enough to argue about. Seven years later, offices across the world emptied in a matter of weeks. What began as a temporary emergency measure turned into a permanent change in how organizations think about where work happens. Remote work culture did not appear suddenly in 2020. It developed through three distinct stages, and at every stage a few companies were already living in the next one.

The first stage was the exception. Remote work was a privilege granted to individuals, usually senior ones, and it was understood as a favor. Yahoo’s 2013 recall made the logic explicit: Marissa Mayer argued that speed and quality suffer when employees are not physically together. The assumption underneath that decision was that the office is the default, and that anyone outside it is, in some sense, absent. Few companies questioned the assumption, and the ones that did were treated as curiosities.

The second stage was the experiment. Automattic, the company behind WordPress, has operated without a central office since its founding in 2005. GitLab published a public handbook documenting in detail how a company with no headquarters runs its meetings, its hiring, and its promotions. These organizations were not escaping offices; they were designing something to put in their place. They wrote down what office companies never had to write down, because inside an office the answer to most process questions is to ask the person sitting nearby.

The third stage arrived in 2020, and nobody chose it. Companies that had never seriously considered remote work discovered within a week that most of it was possible. The tools were unremarkable and largely already installed. What proved difficult was the culture: the informal conversation, the new employee who had never met a colleague, the manager who could not tell whether a quiet team was doing well or struggling. Organizations solved those problems, when they solved them, by borrowing practices the experimenters had documented years earlier.

The three stages explain why the argument about remote work never quite ends. A company asking employees back to the office is reasoning from the first stage, where presence meant participation. A company hiring across three continents is reasoning from the third, where written communication has replaced proximity. The examples show that remote work culture was not invented in a crisis. It was tested quietly for fifteen years, and the crisis only made everyone read the results.