영어 모의고사 (자동 편성)

Cognitive psychologists ①distinguish between two modes by which the mind handles information, a distinction that helps explain why experienced drivers can hold conversations while navigating familiar roads without conscious effort. Automatic processing occurs rapidly, ②requires minimal attention, and typically operates outside conscious awareness, allowing multiple automatic tasks to proceed without interfering with one another. Controlled processing, by contrast, unfolds more slowly, demands deliberate attention, and can generally handle only one demanding task at a time, since it draws upon a limited pool of cognitive resources that becomes rapidly depleted under competing demands. Skills that once required intense concentration, such as reading or typing, gradually ③become automatic through extensive practice, freeing attentional resources for other purposes. This shift ④explains why novice drivers find it difficult to talk while merging onto a highway, whereas seasoned drivers manage the same maneuver with comparative ease. However, automaticity ⑤carry hidden costs, since automatized responses can be triggered inappropriately by habitual cues even when circumstances have changed, occasionally producing errors that controlled deliberation would have prevented.

City governments increasingly market dense networks of surveillance cameras as a straightforward safety upgrade, ①promising faster emergency response and lower crime rates. In many cases, the technology delivers exactly what it promises, ②helping investigators identify suspects within hours rather than weeks. Yet the same infrastructure that catches criminals also creates a permanent record of where ordinary, law-abiding citizens ③go about their daily lives. Once a camera network exists, its purpose can quietly expand, often without new public debate or explicit approval. Civil liberties groups argue that a city should decide how much surveillance it wants before the cameras go up, not years afterward when the infrastructure ④is already impossible removing. Convenience installed today, in other words, can quietly ⑤become control tomorrow.

Devices that translate brain activity directly into digital commands have moved from laboratory experiments to real medical use, ①allowing paralyzed patients to control a robotic arm or type a message using thought alone. As the technology improves, some developers envision a future in which brain-computer interfaces extend beyond restoring lost function ②toward enhancing healthy minds, offering faster memory or expanded attention to anyone ③willing to undergo the procedure. This prospect raises a philosophical question that medicine alone cannot answer: if a device can alter memory, mood, or decision-making, at what point does the assistance stop ④being a tool and start reshaping the very identity of the person using it. Neuroethicists argue that mental privacy, the ability to keep one's own thoughts free from external access or manipulation, may need ⑤recognizing as a fundamental right before this technology becomes widespread.

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

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. During recent years, this gap became ①impossible to ignore, as families in rural areas or low-income neighborhoods struggled to complete schoolwork that assumed a stable connection and a personal device. Some policymakers initially treated internet access as a ②convenience, comparable to cable television, rather than as infrastructure essential to modern life. That assumption is increasingly being reconsidered, particularly as more essential services move online by default. Closing this divide will require ③far less than simply distributing devices; it will demand ④sustained investment in the physical networks that carry information into every home, ⑤regardless of income or location.

Every time a smartphone connects to a cell tower or a Wi-Fi network, it ①quietly records a data point that can later be pieced together into a detailed map of where its owner has been. Most users agree to this tracking without reading the fine print, assuming that a single company's database poses ②little threat. The real danger, however, emerges when location data from multiple sources is combined. A pattern that reveals a person's home, workplace, and daily route can ③expose far more than any single app ever intended to share. Companies often argue that anonymized data cannot be traced back to individuals, yet researchers have ④repeatedly shown that a handful of location points is enough to identify a specific person with striking accuracy. As a result, privacy experts increasingly insist that location data deserves ⑤minimal legal protection, similar to that of medical records.

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.

When engineers design the software that controls a self-driving car, they inevitably face a question that philosophers have debated for decades: who should the vehicle protect in an unavoidable crash? (A) This gap between the ideal and the personal choice suggests that ethical programming cannot be settled by public opinion alone. (B) Surveys reveal an uncomfortable contradiction: most people say a car should minimize total harm, yet the same people admit they would never buy a vehicle designed to sacrifice its own owner. (C) Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles are allowed to make such decisions on the road.

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

Until clearer rules emerge, artists, developers, and publishers remain locked in a legal gray zone that satisfies almost no one. 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. ( ① ) Supporters of the technology argue that this process resembles how a human student learns by studying the masters. ( ② ) Critics respond that the comparison breaks down at scale, since a single AI model can absorb millions of works in days. ( ③ ) Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as infringement. ( ④ ) This is a question the law was never designed to answer. ( ⑤ )