영어 모의고사 (자동 편성)

Facial recognition software has moved rapidly from airport security lines into shopping malls, schools, and even private apartment buildings, ①promising convenience in exchange for a quick scan of one's face. Proponents note that the technology can unlock doors, catch shoplifters, and speed up identity checks far faster than any human guard ②could manage. Independent studies, however, have repeatedly found that many commercial systems ③performs less accurately on women and people with darker skin tones, a gap that can lead to wrongful identification with serious real-world consequences. Several cities have already reported cases in which an innocent person ④was detained based on a flawed match. Until these systems can prove equally reliable for everyone, deploying them at all represents a form of quiet discrimination ⑤dressed up as efficiency.

Factories that once employed hundreds of workers on an assembly line now run with a ①fraction of that staff, as robotic arms handle tasks that used to require human hands. Economists have long debated whether this shift destroys jobs or simply transforms them. Historical evidence offers some ②comfort: previous waves of automation eventually created new categories of work that no one had predicted. However, critics point out that earlier transitions unfolded over generations, giving workers ③ample time to retrain, whereas today's robots are being deployed at a pace that leaves little room for adjustment. A factory worker displaced this year cannot simply wait for a new industry to emerge decades from now. For this reason, many economists argue that the real ④challenge is not the robots themselves, but the ⑤sluggish speed at which society is expected to adapt to them.

Beyond restoring lost function, engineers are now exploring implants and prosthetics designed to give healthy people abilities beyond ordinary human limits, from night vision to reaction times faster than any athlete could achieve through training alone. Supporters frame this as simply the next step in a long history of human enhancement, ①no different in principle from eyeglasses or vaccines, only more direct. Critics counter that earlier enhancements were broadly available to anyone regardless of wealth, while cutting-edge neural and physical implants are likely to remain ②expensive for decades, potentially splitting society into those who can afford enhancement and those who cannot. If such a divide hardens, competitive advantages in school, sports, and the workplace could come to depend ③less on effort than on access to capital. What began as a medical tool for restoring what was lost, in other words, may end up ④preserving the baseline of what counts as an ordinary human being, keeping the definition of "normal" ⑤unchanged for future generations.

Millions of people now wear devices that continuously track their heart rate, sleep patterns, and daily activity, generating an ①intimate stream of health data that would have been unthinkable to collect just two decades ago. Users generally assume this information belongs to them, since it is drawn directly from their own bodies. In practice, however, the terms of service that few people read often grant the manufacturer ②narrow rights to store, analyze, and even sell anonymized versions of that data to insurers or advertisers. This arrangement becomes especially troubling when health data influences decisions far beyond fitness, such as insurance premiums or employment screening. Advocates for reform argue that biological data deserves a category of protection ③stronger than ordinary consumer information, precisely because it can never truly be ④replaced or reset once it has been ⑤exposed.

As more schools shift toward digital textbooks and online assignments, a student without reliable internet access at home faces a disadvantage that has little to do with talent or effort. Some policymakers initially treated internet access as a convenience, comparable to cable television, rather than as infrastructure essential to modern life. That assumption is increasingly being reconsidered, particularly as _______________. Closing this divide will require more than simply distributing devices.

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 society is often judged by how much intergenerational mobility it permits, since high mobility suggests that outcomes depend more on effort and ability than on the accident of birth. Social mobility refers to the movement of individuals or families between different positions within a society's system of stratification, whether upward, downward, or sideways. ( ① ) Sociologists distinguish intragenerational mobility, changes within a single person's lifetime, from intergenerational mobility, differences between the social position of parents and their children. ( ② ) Structural mobility, caused by large-scale economic shifts such as the growth of new industries, must be distinguished from individual mobility, which results from a single person's particular choices. ( ③ ) Research across many nations has found that education functions as the primary channel of upward mobility. ( ④ ) Yet access to quality education is itself unevenly distributed, meaning the very mechanism meant to promote fairness can end up reproducing the inequality it was designed to overcome. ( ⑤ )

주어진 문장: Because this competition can occur in either temporal direction, learners cannot avoid it simply by focusing on new material, which is precisely why deliberately spacing out study sessions matters so much. Memory researchers have long recognized that learning is not simply a matter of accumulating information without cost. Newly acquired material can collide with what the mind already holds, producing measurable disruptions in recall. ( ① ) When earlier knowledge interferes with the retrieval of information learned afterward, psychologists call this proactive interference. This phenomenon explains why someone who memorized an old phone number for years may struggle to recall a new one. ( ② ) Conversely, when material learned later undermines the recollection of something acquired earlier, the process is labeled retroactive interference. This second mechanism explains why switching to a new keyboard layout can temporarily impair typing on a familiar one. ( ③ ) Both processes suggest that memories compete for the same limited retrieval pathways rather than resting in isolated compartments. ( ④ ) This insight carries real implications for how students approach studying multiple similar subjects. ( ⑤ ) Instead of cramming closely related material back to back, spacing out review sessions can reduce competition between overlapping traces. Varying study contexts further helps preserve accuracy for both old and new information alike.

Critics note a further irony: when something goes wrong, such as a hacked exchange or a lost password, there is often no institution left to appeal to. Blockchain technology was originally promoted as a way to remove the need for trusted middlemen, allowing strangers to exchange value directly and verify transactions through a shared, tamper-resistant ledger. ( ① ) In theory, this design distributes power away from any single institution and toward the network as a whole. ( ② ) In practice, however, a small number of large players have come to dominate many blockchain networks. ( ③ ) They control enough computing power or currency to influence outcomes that were supposed to be decided collectively. ( ④ ) Whether decentralization can survive this concentration of power remains one of the technology's most unresolved questions. ( ⑤ )

This finding is particularly striking given that the two groups of employees faced no difference whatsoever in the financial terms of the plan itself. Employers have long experimented with how retirement savings plans are presented to new hires. ( ① ) In one common arrangement, employees receive a form and must actively check a box to begin contributing a portion of their salary to a savings account. ( ② ) Under this opt-in arrangement, a substantial share of eligible employees never get around to enrolling, often citing simple procrastination rather than genuine disinterest in saving. ( ③ ) When employers instead enroll employees automatically and require them to check a box only if they wish to withdraw, participation rates climb to well over ninety percent. ( ④ ) Only the framing of the choice, not its economic substance, appears to determine whether employees end up saving for retirement. ( ⑤ ) This gap between the two arrangements reveals just how powerfully a default setting can shape decisions that people generally regard as carefully considered.