영어 모의고사 (자동 편성)

Dual coding theory, first proposed to explain why certain kinds of information are remembered more readily than others, holds that the mind process ① verbal and visual material through two partially independent channels rather than a single unified system. According to this account, words and other linguistic symbols are encoded ② primarily through a verbal channel that represents meaning in an abstract, sequential form, while pictures and concrete objects are encoded through an imagery channel that preserves spatial and perceptual detail. When information engages both channels simultaneously, as when a textbook pairs ③ a written explanation with an accompanying diagram, the resulting memory trace benefits from two separate, mutually reinforcing pathways of retrieval rather than just one. This redundancy explains the well-documented picture superiority effect, whereby concrete, easily visualized words tend to be recalled ④ more accurately than abstract terms that resist mental imagery. Consequently, dual coding theory has profound implications for instructional design, suggesting that combining ⑤ carefully chosen visuals with concise verbal explanations can strengthen students' long-term retention of complex material.

As companies collect enormous amounts of personal data to improve their products and target advertisements, serious ethical questions ①have emerged about how that information should be handled. Employees who work with big data now face responsibilities ②that previous generations rarely encountered, since a single careless decision can expose millions of people's private information. Some argue that strict regulations should govern data collection, even if such rules ③slow innovation and increase costs for businesses. Others insist that self-regulation within companies, guided by clear ethical training, can ④protects consumers just as effectively without stifling progress. Universities have begun adding data ethics courses to computer science programs, ⑤recognizing that future employees will need to balance innovation with responsibility.

Self-driving technology is advancing rapidly, and its impact on the transportation industry ①is becoming increasingly visible. Companies are testing autonomous trucks ②that can travel long distances without human drivers, potentially transforming how goods move across countries. Millions of professional drivers worry about ③lose their jobs as automation spreads through the industry. Experts predict that certain driving jobs, particularly long-distance trucking, may ④shrink significantly within the next two decades. New roles are also emerging, including remote vehicle operators who monitor fleets of autonomous trucks and technicians ⑤who maintain the complex sensors these vehicles depend on.

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.

Social media platforms rarely show users a neutral stream of information. Instead, recommendation algorithms are optimized to maximize engagement, and content that provokes strong emotion tends to hold attention longer than balanced reporting ever could. Some platforms have experimented with reducing the visibility of outrage-driven content, only to see user engagement drop, which reveals _______________.

Many large companies now rely on artificial intelligence to 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 drawn 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 intended discrimination. Unlike a human interviewer, the system offers no explanation for its choices, which means that _______________. Some researchers argue that fairness cannot be added afterward; it must be built into the system's design from the very beginning.

As governments and companies work to address climate change, a new category of employment known as green jobs has expanded rapidly. Critics point out that transitioning workers from fossil fuel industries to green jobs is not always simple, since the required skills can differ significantly. Governments are therefore introducing training programs designed to help such workers shift smoothly into renewable energy careers. As climate goals become more urgent worldwide, ___________.

Online reviews have transformed the way consumers evaluate products before buying them. (A) Unfortunately, some of these reviews are not genuine, having been written by paid promoters rather than actual customers. (B) Shoppers now trust the opinions of strangers online almost as much as recommendations from friends or family. (C) For this reason, several platforms have started using technology to detect and remove suspicious reviews automatically.

Instead, these trainers help workers use AI tools wisely, without becoming fully dependent on them. As artificial intelligence becomes part of everyday life, entirely new job titles are appearing in the workplace. ( ① ) Prompt engineers, for instance, design effective instructions that help AI programs produce accurate and useful results. ( ② ) AI ethics officers examine whether a company's technology treats users fairly and avoids harmful bias. ( ③ ) Meanwhile, human-AI collaboration trainers do not simply teach employees to use every new AI feature available. ( ④ ) These jobs did not exist even ten years ago, yet they are growing quickly across many industries. ( ⑤ ) This suggests that success in an AI-driven future will depend more on uniquely human skills.

There is also a quieter cultural cost that rarely appears in productivity reports. Sensors buried in the soil now report moisture levels in real time, drones survey entire fields for signs of disease within minutes, and automated systems adjust irrigation without a farmer ever walking the rows. ( ① ) For large operations with the capital to invest, these tools have measurably increased yields while reducing water waste. ( ② ) Smaller family farms, however, often cannot afford the upfront cost of this equipment. ( ③ ) Some worry that the gap between high-tech and traditional operations will only widen as the technology advances further. ( ④ ) Generations of farming knowledge, passed down through direct observation of soil, weather, and crops, risk being replaced by dashboards and automated alerts. ( ⑤ )