영어 모의고사 (자동 편성)

Many large companies now rely on artificial intelligence ①to screen job applications before a human ever sees them. These systems promise efficiency, scanning thousands of resumes in seconds and ranking candidates according to patterns ②drawn from previous hiring decisions. However, this reliance on historical data creates a troubling risk. If a company's past hiring favored a particular group, the algorithm may learn to treat that pattern as a marker of success and quietly reproduce it, even when no one ③intending discrimination. Unlike a human interviewer, the system offers no explanation for its choices, which makes the bias difficult ④to detect until an outside audit reveals it. Some researchers argue that fairness cannot be added afterward; it must ⑤be built into the system's design from the very beginning.

When people look back on a multi-day journey, they rarely ①recall every hour with equal clarity; instead, memory tends to compress the entire experience into just a few representative moments. Psychologists have found that two points carry disproportionate weight in this process: the single moment when emotion, whether joy or discomfort, ②reached its highest intensity, and whatever happened right before the experience concluded. Consider two hikers who complete an identical five-day trek. The first finishes with a grueling, rain-soaked final climb, while the second enjoys a calm, scenic descent on the last afternoon despite having ③endure an equally difficult ascent earlier in the trip. Even though both hikers faced comparable hardship overall, the second is far more likely to remember the journey fondly, simply because the trip ④ended pleasantly. This pattern reveals that overall satisfaction is not calculated as an average of every experienced moment; rather, it is shaped disproportionately by whichever moments ⑤felt most intense and by however the experience happened to end.

Metacognition, broadly defined as the capacity to monitor and evaluate one's own cognitive processes, is often treated as a ①reliable guide to what we actually know, yet a growing body of evidence suggests that the correspondence between subjective confidence and objective accuracy is far weaker than intuition would suggest. Individuals routinely ②overestimate how thoroughly they have understood a passage of text immediately after reading it, a phenomenon partly attributable to the fluency with which the material was processed rather than to any genuine grasp of its underlying structure. Because rereading familiar material tends to feel ③effortless, learners frequently mistake that ease of processing for durable comprehension, a misattribution that can lead them to ④continue further study prematurely. This gap between perceived and actual mastery, sometimes labeled the illusion of knowing, has practical consequences for education, since students who trust their ⑤inflated self-assessments are less likely to seek out the more effortful, retrieval-based strategies that would otherwise consolidate their learning.

It would be alarming ① to believe that anything sufficiently large or obvious appearing within our field of vision will automatically capture our attention, yet decades of research into inattentional blindness suggest otherwise. When people are absorbed in a demanding task, such as counting how many times a ball is passed between players, they frequently fail to notice an unexpected and conspicuous ② event unfolding in plain sight, even something as unlikely as a person in a costume walking through the scene. This failure does not stem from a defect ③ in the eyes, which register the stimulus perfectly well, but from the fact that conscious perception depends heavily on where attention has been directed. Policymakers ought to take this finding more seriously than they currently do, because it implies that simply placing a warning sign within someone's visual range offers no guarantee ④ that it will be consciously registered. Designing safer environments, therefore, requires accounting for the limits of attention itself, not merely assuming that visibility alone ensures ⑤ awareness.

For decades, memory researchers assumed that repetition alone determined how well information would be retained, an assumption that an influential theory proposed by Craik and Lockhart eventually overturned. According to their framework, what matters most is not how many times a person rehearses material but how deeply that material is processed during encoding. ①Shallow processing, which involves attending merely to superficial features such as the shape of letters or the sound of a word, produces memory traces that fade quickly and remain ②vulnerable to interference. Elaborative processing, by contrast, requires connecting new information to existing knowledge, generating meaningful associations, and analyzing semantic content, thereby producing traces that are considerably more ③fragile. Consequently, a student who merely repeats a definition aloud without considering its implications is less likely to retain it than one who relates the concept to a personal example. This distinction reshaped educational practice, encouraging teachers to design tasks that demand ④comprehension rather than ⑤mechanical repetition, since durable memory depends on the depth, not the frequency, of cognitive engagement.

When people recall precisely where they were and what they were doing at the moment they learned of some momentous public event, the resulting memory often feels extraordinarily vivid, as though it had been etched into the mind with photographic precision; researchers have termed such recollections flashbulb memories. Proponents of this concept once argued that the emotional intensity and surprise accompanying such events trigger a distinct neural mechanism that preserves details with unusual fidelity, effectively exempting them from the gradual decay that afflicts ordinary memories. Subsequent longitudinal studies, however, have complicated this picture considerably: when researchers compared accounts gathered within days of an event to accounts gathered years later, they discovered that details had frequently been altered, omitted, or even fabricated, even though the individuals reporting them expressed unwavering confidence in their accuracy. This persistent mismatch between subjective certainty and objective consistency implies that _______________.

Blockchain technology was originally promoted as a way to remove the need for trusted middlemen. In theory, this design distributes power away from any single institution and toward the network as a whole. In practice, however, _______________, controlling enough computing power or currency to influence outcomes that were supposed to be decided collectively. Whether decentralization can survive this concentration of power remains one of the technology's most unresolved questions.

Generative AI systems learn to write, draw, and compose by studying enormous collections of existing human work, much of which was created without the artists' knowledge or consent. (A) Critics respond that the comparison breaks down at scale, since a single AI model can absorb millions of works in days and compete with the very artists who trained it. (B) Supporters of the technology argue that this process resembles how a human student learns by studying the masters. (C) Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as infringement.

A few years ago, spotting a manipulated video required a trained eye and specialized software. (A) Perhaps more troubling than any single fake video is the broader effect on public trust, sometimes called the liar's dividend, in which genuine footage can be dismissed as fabricated simply because deepfakes exist. (B) Today, freely available applications allow almost anyone to swap a face or fabricate a voice convincingly enough to fool casual viewers within minutes. (C) This shift has quietly undermined one of the oldest assumptions in human communication: that seeing and hearing something is a reliable way to confirm it happened.

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