영어 모의고사 (자동 편성)

Internet activist Eli Pariser introduced the term filter bubble to describe a phenomenon ①created by online algorithms. Social media platforms and search engines constantly track which links users click and how long they linger on certain posts. Based on this data, algorithms predict what each individual is most likely ②to enjoy and quietly prioritize similar content in future results. As a result, two people who type the same search query can receive strikingly different results, each ③tailoring to personal history. This automatic filtering happens silently, without users ever ④deciding to limit what they see. Consequently, many people gradually lose access to viewpoints ⑤that challenge their existing beliefs, even though they never intended to avoid them.

People routinely seek out information that confirms what they already believe while overlooking or dismissing evidence that ①contradicts those beliefs, a pattern known as confirmation bias. This tendency operates not because individuals are deliberately dishonest, but because the mind finds it cognitively less taxing ②to absorb information that fits an existing framework than to reconstruct that framework from scratch. Investors who believe a particular company will succeed, for instance, tend to interpret ambiguous news about that company favorably, while investors who ③are skeptical of the same company interpret identical news unfavorably, even though both groups are examining the same underlying facts. Social media platforms can intensify this effect by algorithmically presenting users with content similar to what they have previously engaged with, thereby ④narrowing the range of perspectives an individual regularly encounters. Over time, such selective exposure can harden initial impressions into convictions that ⑤feels objectively verified, even though they rest on a systematically filtered sample of evidence.

Among the many tools available to those who design choice environments, few are as quietly powerful as the default option, the alternative that ①takes effect automatically unless a person actively selects something else. Because changing a default requires deliberate effort, many individuals simply accept whatever arrangement has been preselected for them, regardless of whether that arrangement actually ②serve their interests best. This tendency has been documented across a remarkably wide range of contexts. Countries that automatically enroll citizens in organ donation programs, requiring an explicit request to opt out, consistently report far higher donor registration rates than countries that require citizens ③to actively opt in. Employers who automatically enroll new hires in retirement savings plans likewise see participation rates ④climb dramatically compared with employers who leave enrollment entirely voluntary. What makes default options so influential is not merely convenience; a default also functions as an implicit signal about what is considered normal or recommended, subtly ⑤shaping perceptions even among individuals who believe themselves to be deciding independently.

When engineers design the software ①that controls a self-driving car, they inevitably face a question that philosophers have debated for decades. If a crash becomes unavoidable, should the vehicle protect its own passenger at all costs, or should it be programmed ②minimizing total harm, even if that means steering toward the person inside? Surveys reveal an uncomfortable contradiction: most people say a car should be programmed to save the greater number of lives, yet the same people admit ③they would never buy a vehicle designed to sacrifice its own owner. This gap between the ideal and the personal choice suggests that ethical programming cannot ④be settled by public opinion alone. Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles ⑤are allowed to make such decisions on the road.

A few years ago, spotting a manipulated video ①required a trained eye and specialized software. Today, freely available applications allow almost anyone ②to swap a face or fabricate a voice convincingly enough to fool casual viewers within minutes. This shift has quietly undermined one of the oldest assumptions in human communication: that seeing and hearing something ③are a reasonably reliable way to confirm it happened. Journalists now face the burden of ④verifying footage that once would have been accepted as self-evident proof. Perhaps more troubling than any single fake video is the broader effect on public trust, in which genuine footage can ⑤be dismissed as fabricated simply because deepfakes exist.

Hospitals are increasingly adopting artificial intelligence tools that can scan medical images and flag potential tumors ①faster and, in some trials, more accurately than experienced radiologists. Patients understandably welcome any technology that catches disease earlier, yet the growing reliance on these tools raises an uncomfortable legal question that hospitals have only begun to address. When an AI system misses a diagnosis that a human doctor would likely have caught, responsibility becomes ②easy to assign. The doctor may argue that they simply trusted a tool the hospital required them to use, the hospital may argue that the software company guaranteed its accuracy, and the software company may argue that no algorithm can be expected to be ③perfect. Until legal systems ④clarify this chain of responsibility, patients harmed by an algorithmic error may find themselves with no clear party to hold ⑤accountable.

The discovery of CRISPR gene-editing technology gave scientists a tool precise enough to correct a single mistaken letter in the human genome. Yet editing a gene in a sperm, egg, or early embryo does not simply change one patient; it alters the genetic code passed down to every future generation. Scientific organizations around the world have therefore drawn a careful line, permitting gene editing to treat disease in existing patients while banning any edit that would be inherited by their descendants. Critics worry, however, that _______________.

Advertisements often rely on subtle psychological techniques that persuade viewers without their conscious awareness. One common method, known as social proof, shows images of many people using or enjoying a product, suggesting that its popularity guarantees quality. Another technique appeals to authority, featuring experts or professionals endorsing a product to make it seem more credible and trustworthy. Emotional appeals are equally common, connecting products with feelings such as happiness, nostalgia, or belonging rather than focusing on practical features. Repetition also plays a significant role, since seeing the same advertisement multiple times increases familiarity, which people often mistake for reliability. Researchers studying consumer behavior have found that ___________, since awareness disrupts the automatic psychological response advertisers depend on.

As solar and wind power expand rapidly across national grids, engineers face a challenge that receives far less public attention than the construction of the generating facilities themselves. (A) One proposed solution involves large-scale battery storage systems, which absorb surplus electricity generated during periods of high output and release it later when generation declines. Another approach relies on expanding transmission networks across wider regions, so that electricity generated in a sunny area can be redirected to places experiencing a shortfall. (B) Unlike conventional power plants, which can adjust their output to match demand at almost any moment, solar panels and wind turbines generate electricity only when sunlight or wind is available, producing supply that fluctuates constantly. This intermittency means that grid operators must balance supply and demand even when renewable output suddenly drops, such as when clouds cover a solar farm or wind speeds fall. (C) Grid operators must therefore invest in infrastructure capable of accommodating this variability, rather than assuming renewable sources can simply replace fossil fuels without adjustment. Without such investment, integrating renewable energy could paradoxically undermine the very reliability that modern electricity grids are expected to provide.

Similar changes are taking place in other fields as well. As technology develops rapidly, many new jobs are appearing that did not exist a decade ago. ( ① ) Data analysts, for example, help companies make decisions based on huge amounts of information. ( ② ) Renewable energy technicians install and repair solar panels and wind turbines, meeting the growing demand for clean power. ( ③ ) These emerging jobs share one common feature: they combine technical skill with the ability to solve real-world problems. ( ④ ) Experts predict that future workers will need to keep learning new skills throughout their careers, since technology continues to change so quickly. ( ⑤ ) For students planning their future, staying curious and adaptable may matter more than a single fixed career path.