영어 모의고사 (자동 편성)

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.

Planning a multi-city trip once meant hours ① spent comparing flight schedules, reading hotel reviews, and mapping out which sights were close enough to visit in a single day. AI travel planners now compress much of that work into a short conversation, ② asking about a budget, a travel style, and a handful of preferences before generating a full day-by-day itinerary within minutes. The tool draws on enormous amounts of data, including flight prices, seasonal weather patterns, and reviews ③ leaving by previous travelers, to suggest a route that avoids obvious scheduling conflicts. Many travelers appreciate ④ having a solid starting point rather than a blank page. Still, seasoned travelers warn that these itineraries can feel oddly generic, often ⑤ steering users toward the same popular landmarks.

Many people believe that building a new habit requires strong willpower or motivation. However, psychologist BJ Fogg argues that habits form more ①easily when a behavior itself becomes simple to perform. His theory, called the Fogg Behavior Model, states that a behavior happens only when motivation, ability, and a prompt occur together. When motivation is ②low, the model suggests, the task must be made extremely ③difficult, or it will not happen at all. For instance, a person who wants to exercise daily succeeds more often by committing to one push-up than to a long workout. Fogg calls this idea the law of ④least effort, since people naturally choose the option requiring the least energy. A student who wants to read more might leave a book open on the desk rather than setting a vague goal. By lowering the required effort, people reduce the ability barrier and make steady action far more ⑤achievable.

Automation is transforming labor markets more ①quickly than economists predicted a decade ago. Factories once staffed by hundreds of workers now rely on robotic arms that assemble products with remarkable ②precision. This shift does not simply eliminate jobs; it also creates roles that never existed before, so technicians who maintain robotic equipment are now in high ③demand. Companies that invested early in automation often report steady increases in productivity, since machines handle repetitive tasks while employees focus on ④creative problem-solving. Without proper retraining support, the benefits of automation may not be shared fairly, ⑤narrowing inequality between skilled and unskilled labor.

Modern factories increasingly rely on collaborative robots, often called cobots, which are designed to work ①safely alongside human employees. Unlike traditional industrial robots that operate behind safety cages, cobots use sensors to detect nearby workers and adjust their movements accordingly. Cobots typically handle repetitive or physically demanding tasks, while human workers focus on tasks that require ②judgment and fine motor skills. This division of labor increases overall ③efficiency, since each side contributes what it does best. Manufacturers who have introduced cobots report ④more workplace injuries, largely because employees no longer perform exhausting repetitive motions all day. As the technology becomes more affordable, smaller companies are also beginning to ⑤adopt cobots.

Only a decade ago, translation apps produced sentences so awkward that users often laughed at the results. Today, many of these apps rely on neural networks that analyze entire sentences at once, capturing context instead of translating word by word. This shift has made everyday tasks such as reading a foreign menu or chatting with an overseas friend remarkably smooth. Even so, the technology still struggles with humor, sarcasm, and cultural nuance, so that _______________. For this reason, professional translators argue that these apps are best treated as a helpful starting point rather than a final, polished result.

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, ___________.

The growing presence of AI in creative fields has sparked lively discussion among artists and critics alike. (A) In contrast, some artists worry that relying too heavily on such tools may weaken the value placed on original human creativity. (B) Many artists have embraced AI as a way to explore new styles quickly and overcome creative blocks. (C) Given these opposing views, most experts now agree that AI works best as a partner rather than a replacement for human creators.

주어진 문장: This heightened sensitivity can manifest in a variety of noticeable ways. In recent years, mental health experts have identified a distinct emotional response to environmental crisis, commonly called eco-anxiety. ( ① ) Eco-anxiety describes persistent worry, grief, or fear triggered by awareness of climate change and its consequences for the future. ( ② ) Although people of all ages can experience it, teenagers and young adults seem especially vulnerable, since they anticipate living through decades of worsening conditions. ( ③ ) Common symptoms include difficulty sleeping, a sense of hopelessness, and guilt over ordinary choices such as travel or diet. ( ④ ) For example, some students report feeling paralyzed whenever they read alarming climate news, unable to focus on schoolwork or hobbies. ( ⑤ ) However, experts emphasize that eco-anxiety is not a disorder to be eliminated but a natural reaction that can be channeled productively. Rather than avoiding the topic, young people are encouraged to join community projects, since taking constructive action tends to restore a sense of control and ease overwhelming distress.

This kind of quiet anticipation is precisely what makes the technology feel almost invisible in daily life. A smart thermostat that learns a family's daily rhythm can lower the temperature automatically before everyone wakes up cold, while smart lights that follow a resident's schedule no longer need to be switched on and off by hand. ( ① ) These small conveniences rest on a shared foundation: sensors collect data about temperature, motion, and light throughout the home, and an algorithm gradually learns which settings match each household's actual habits rather than a factory default. ( ② ) Over months of use, the system becomes noticeably better at anticipating needs, often adjusting settings moments before a resident would have reached for a switch. ( ③ ) Yet convenience comes with a trade-off that many households overlook. ( ④ ) Every one of these small adjustments depends on constant data collection, meaning that a detailed record of when people wake, leave, and return home is quietly being stored somewhere beyond their immediate control. ( ⑤ )