Category: English

Source: The Conversation – USA (2) – By Saharnaz Babaei-Balderlou, Teaching Assistant Professor of Economics, University of Wisconsin-La Crosse

Students using AI to cheat on homework or tests is a source of much discussion. But some scholars argue the greater risk of students using AI is that they will simply not learn.

Approximately 90% of 1,100 U.S. students surveyed at two-year and four-year colleges in 2025 reported using generative AI for everything from drafting assignments to clarifying complex concepts.

But when students use AI as a tutor or study partner, not as an immediate answer generator, does it make it easier or harder for them to learn?

We are economists who tried to answer this question by designing an AI tool using ChatGPT’s custom GPT feature, with the web access of the chatbot disabled.

We named the tool Macro Buddy and trained it to guide some students at one of our undergraduate macroeconomics classes at the University of Wisconsin, La Crosse, through their reasoning rather than giving them direct answers.

We found in our research, conducted in spring 2025, that students who used Macro Buddy, alongside peer discussion, earned higher exam scores than students who worked alone, without this AI tutor.

Meet your new tutor

One of our macroeconomics courses enrolled 140 undergraduate students, mostly in their first or second year of college, divided across four sections.

Students’ course materials, assignments and exams were identical across all four sections. Students were generally not allowed to use AI tools or collaborate with classmates during exams. Students took all tests in person and were not allowed to reference any notes or other materials during the exam.

As a result, exam scores reflected what students understood and could explain on their own – without the help of AI or any other outside source.

After all students took their first exam, we randomly assigned the four class sections to take on a different study format.

We prompted one group of students to work individually, without Macro Buddy; another group of students worked in groups, without Macro Buddy; a third group of students worked individually, with Macro Buddy; and a fourth group of students worked in groups, with Macro Buddy.

We wanted to compare how different study approaches – working alone, working with classmates, using Macro Buddy or combining both – altered how well students did on exams.

Macro Buddy’s skills

We trained Macro Buddy with the help of lecture transcripts, slides and homework questions specifically from this macroeconomics course.

Macro Buddy had internet access turned off, so it relied only on the instructor’s course materials.

Macro Buddy was designed to act like a tutor, not an answer machine. Instead of giving students complete solutions, Macro Buddy asked follow-up questions meant to guide students toward an answer.

For example, if a student asked why lower prices might increase consumers’ spending, Macro Buddy would not offer a quick, full explanation. It might instead ask what happens to people’s purchasing power when prices fall. The student would then have to connect the concepts and explain their reasoning, in their own words, step by step.

This distinction between explaining an idea and receiving a finished answer matters.

An AI tool that simply delivers answers can allow students to skip thinking through a problem. One study found that when college students rely on a chatbot as a crutch, they perform worse when they no longer have access to it. A tool that asks questions requires students to do the work themselves, even while receiving guidance. This is the very process that makes learning stick.

What happened to students’ learning

The one group of students that continued working individually, without AI, served as our control group.

The other three groups changed how they studied: One began working in groups without AI, one worked individually with Macro Buddy, and the last group combined group work with Macro Buddy.

All of the students’ average scores declined when they took their second exam, across all four study groups.

By the third exam, however, differences across sections became clearer.

Students who used both Macro Buddy and group discussion earned the highest average scores. Students who used Macro Buddy alone also scored higher than those who worked alone without Macro Buddy. Students who worked in groups without Macro Buddy showed smaller improvements, when compared to the students in other groups.

The third exam happened several weeks after we introduced the new study formats.

By that point, students in the combined group may have grown more comfortable using Macro Buddy to test their understanding, while also explaining ideas to classmates. Working with peers meant having to articulate reasoning clearly and respond to questions, which can deepen understanding over time.

Why this matters

Some critics of AI worry that students will rely on AI to do the hardest parts of learning for them. This reflects a fear that students may stop practicing the skills that build expertise. Students become experts in their fields while struggling with confusing material, revising explanations and seeing whether they truly understand an idea.

Our experiment suggests erosion of learning when using AI is not inevitable.

We found that when AI is designed as a tutor that asks questions instead of simply giving answers – and when students are also required to explain their reasoning to classmates – the technology can support learning rather than replace it.

Most students today use general-purpose chatbots that are not designed as tutors. They type in a question and receive a response. But our findings suggest that even small design choices, such as building an AI chatbot with guiding questions, can shape how students engage with the material.

Peer discussion also adds something to the learning process that AI cannot provide: social accountability and exposure to alternative reasoning.

Together, these practices encourage students to think through problems more actively.

The evidence from our experiment highlights a practical distinction: AI can be used to replace thinking, or it can be used to support it. The impact may depend less on the technology itself and more on how it is structured and integrated into learning.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. We designed an AI tutor that helps college students reason rather than give them answers – https://theconversation.com/we-designed-an-ai-tutor-that-helps-college-students-reason-rather-than-give-them-answers-276584

Source: The Conversation – USA (3) – By Gillian Russell, Assistant Research Professor, Penn State

Nearly three times as many Pennsylvania adults gamble online today than just a few years ago.

And as online platforms make gambling easier and more convenient, some Pennsylvanians are gambling more often and may be more prone to developing problems.

We are researchers at Penn State’s Criminal Justice Research Center and the University of Kentucky’s College of Social Work who recently published these findings in a report on online gambling in Pennsylvania. The report was produced in partnership with the Pennsylvania Department of Drug and Alcohol Programs.

We surveyed over 3,500 Pennsylvania adults and found that between 2.5% and 6.4% could be classified as problem gamblers.

An additional nearly 30% fell into “at-risk” categories, meaning they show meaningful signs of harm but do not yet meet the threshold for problem gambling.

Here are five of our key findings and why they matter:

1. Most Pennsylvanians still gamble offline, not online – but the distance between the two is shrinking

When Pennsylvania legalized online gambling in 2017, it required an annual assessment on the impacts of the legislation on Pennsylvania residents’ gambling behaviors. The assessment began two years after the first licenses were issued.

In the first two years of the survey, which were used to produce the 2021 and 2022 annual reports, approximately 11% of Pennsylvania adults reported gambling online. That number rose to as high as 30% in the most recent 2025 report, which was released in January 2026.

The 2025 report, which used both online and phone survey methods, identified that between 61% and 74% of Pennsylvania adults had gambled at least once in the past year. These numbers were consistent with previous reports. Depending on the sampling method, between 56% and 69% of adults reported they had gambled offline – for example, playing slot machines at brick-and-mortar casinos or buying lottery tickets at a store. Between 17% and 30% had gambled online.

Lottery games and raffles remain the most popular offline gambling format, while sports betting remains the most popular online gambling format. This finding has been consistent through all five years of the report.

But the line between “online gamblers” and “offline gamblers” is blurry. Among those who had gambled online, more than three-quarters also gambled offline.

We grouped Pennsylvania gamblers into three subgroups and found that about 43% gambled exclusively offline, about 4% exclusively online, and somewhere between 14% to 27% were “mixed-mode” gamblers, meaning they gambled both online and offline.

2. The more ways people gamble, the more they tend to gamble overall

Those who engaged in mixed-mode gambling participated in nearly twice as many gambling formats as those who gambled only offline or only online.

They also gambled most often: close to once a week on average, compared with about once a month for those who gamble offline only.

Mixed-mode gamblers also spent more time and more money on gambling. People who gambled offline-only or online-only spent a median of about US$20-$40 per month on gambling. Mixed-mode gamblers, meanwhile, spent about $105-$230 per month. Mixed-mode gamblers also had the largest single-day gambling losses.

3. Nearly a third of Pennsylvania gamblers are at risk of problem gambling

This year’s report is the first to estimate how many Pennsylvanians meet criteria for problem gambling in the general population.

Between 2.5% and 6.4% of adults could be classified as current problem gamblers, according to the “problem gambling measure.” This is an evidence-based measure of problem gambling that classifies individuals as recreational, at-risk and problem gamblers.

Nearly 30% fell into “at-risk” gambling categories. They showed meaningful signs of harm but did not yet meet the threshold for problem gambling.

People who engage in mixed-mode gambling are significantly more likely to fall into at-risk and problem gambling categories than people who gamble offline only or online only.

4. Most people with gambling problems do not seek help

Despite the size of the at-risk and problem gambling groups, very few people seek treatment or other assistance.

Only about 1.5% of Pennsylvanians said they felt they had a gambling problem in the past year. Just 0.2% said they had sought help.

Even among those who met criteria for problem gambling, only about 6% reported getting help.

Some people did reach out proactively for others. About 0.4% of residents said they had contacted the 1-800-GAMBLER helpline because of someone else’s gambling.

This gap between harm and help-seeking is not unique to Pennsylvania. But it matters more as gambling becomes easier, faster and more continuous, and as people are more exposed to gambling content through social media and streaming platforms.

5. Young men are particularly vulnerable

Pennsylvania’s experience with gambling mirrors what public health research has long shown with alcohol, tobacco and more recently cannabis: When a behavior becomes more accessible and easier to repeat, overall use rises and harm concentrates among a smaller, more vulnerable group.

Features of online gambling – such as ease of access and acceptance of online payment methods, including cryptocurrencies – are particularly appealing to younger adults, many of whom have shown declining interest in traditional forms of gambling, such as casinos or lotteries. Online platforms offer them the opportunity to gamble on their phones, at all hours, with rapid feedback and minimal barriers to entry. This matters because younger people, especially young men, are disproportionately vulnerable to different types of addictive behaviors, including misuse of alcohol and illicit substances.

For most Pennsylvanians, gambling remains a casual pastime. But as with drinking or substance use, increased availability expands both experimentation and the number of people who progress to harmful levels of engagement. As the online gambling market grows, the data suggests that entertainment and harm may be rising together, following a pattern that public health has seen before with alcohol and cannabis.

Read more of our stories about Philadelphia and Pennsylvania, or sign up for our Philadelphia newsletter on Substack.

Gillian Russell receives funding from the Commonwealth of Pennsylvania.

Glenn Sterner receives funding from the Commonwealth of Pennsylvania.

– ref. Nearly a third of Pennsylvania gamblers are at risk of problem gambling − but few seek treatment – https://theconversation.com/nearly-a-third-of-pennsylvania-gamblers-are-at-risk-of-problem-gambling-but-few-seek-treatment-272240

Source: The Conversation – USA – By Sarah Greene, Instructor in Pediatrics and Infectious Diseases, Washington University in St. Louis

In Greek mythology, King Sisyphus was condemned by the god Zeus to spend eternity rolling a boulder up a hill only to have it roll back down, having to start anew every day.

His story captured our attention as researchers studying neglected tropical diseases – a collection of conditions that primarily affect poor people in low-income countries. These diseases do not kill people at the rates of more well-known infections, such as HIV, malaria and tuberculosis, but cause significant pain and disability. As chronic infections that can cause disfigurement, they are also stigmatizing and economically devastating.

For over 50 years, researchers, clinicians and policymakers in the global health community have worked to eliminate infections such as onchocerciasis (also known as river blindness) and lymphatic filariasis (elephantiasis). They’ve done this through controlling black flies and mosquitoes, the vectors that spread these diseases, as well as huge campaigns to distribute antiparasitic medications to entire communities in areas where these diseases are endemic.

Unfortunately, the Trump administration’s sudden defunding of the U.S. Agency for International Development in 2025 has created a real-life Sisyphean struggle for countries working to eliminate neglected tropical diseases. Withdrawal of USAID support is abandoning the boulder of disease elimination partway up the mountain. When countries are unable to provide treatment, the parasites that cause these diseases will spread to infect more people.

Inevitably, the boulder will roll backward, undoing decades and billions of dollars of work.

Ancient foes

Lymphatic filariasis and onchocerciasis are centuries-old afflictions. An Egyptian statue of Pharaoh Mentuhotep II from 2000 B.C. is suggestive of lymphatic filariasis.

Filarial worms can live for years within humans, causing a variety of different problems. In lymphatic filariasis, adult worms live in the lymphatic vessels, a network running throughout the body that returns fluid back to the circulatory system. This disruption of the lymphatic system can cause the extremities or the scrotum to swell tremendously.

In onchocerciasis, adult worms live in small nodules under the skin. The larval forms of these parasites migrate through the skin and can invade the eyes, causing chronic inflammation that can lead to blindness.

In recognition of the significant suffering these diseases cause, the World Health Organization coordinated global efforts to eliminate them: onchocerciasis starting in 1974 and lymphatic filariasis in 2000.

Since then, these public health campaigns have distributed hundreds of millions of doses of treatment for lymphatic filariasis and onchocerciasis. Medications vary somewhat by location, but they often involve the use of the Nobel Prize-winning drug ivermectin. Merck, the manufacturer of ivermectin, provides the drug for free to each country’s disease control program. Similarly, the companies GlaxoSmithKline and Eisai respectively donate the antiparasitic medications albendazole and diethylcarbamazine citrate for these campaigns.

Together, these programs have dramatically reduced the numbers of people exposed to these infections. For lymphatic filariasis, as of 2024, 871 million people no longer need preventive medications, and 21 countries have eliminated this infection. Five countries have eliminated onchocerciasis.

A job partially completed

Despite significant progress in controlling these infections, it remains logistically challenging to map endemic areas, deliver medications, test for ongoing infection and decide which areas have active outbreaks.

While each country runs its own medication delivery programs, these efforts have been supported by the World Health Organization as well as USAID or USAID-funded nongovernmental organizations. The Trump administration’s funding cuts to USAID halted over 40 drug distribution drives in 2025, affecting over 140 million people. Importantly, the U.S was the largest financial contributor to the World Health Organization until it withdrew its membership in January 2026.

We and others working on eliminating these neglected diseases are concerned that the rapid decrease in funding for these programs will destabilize efforts to treat infections. Stopping medication delivery now can allow these infections to spread unchecked and roll back decades of progress. Donated medications can be effective only if they are delivered to those who need them. This might mean that these campaigns will have to be combined with other public health efforts already underway, or that each country reallocates resources toward these efforts.

If the world turns its back on eliminating these diseases, millions of people will be hurt by the boulder rolling back down.

Sarah Greene has received funding from the NIH.

Philip Budge has received funding from the Gates Foundation and the NIH. He is a member of the Infectious Diseases Society of America and the American Society of Tropical Medicine and Hygiene.

– ref. Billions of dollars, decades of progress spent eliminating devastating diseases may be lost with undoing of USAID – https://theconversation.com/billions-of-dollars-decades-of-progress-spent-eliminating-devastating-diseases-may-be-lost-with-undoing-of-usaid-266195

Source: The Conversation – USA – By Sagar Lekhak, Ph.D. Student in Imaging Science, Rochester Institute of Technology

At least 57 nations have live antipersonnel land mines in their territories. In 2024 alone, 1,945 people were killed by mines and 4,325 were injured, 90% of whom were civilians. Nearly half of those were children. Demining operations removed 105,640 mines in the same year.

With new conflicts, the number of mines continues to grow. For farmers, children and others returning to areas after a conflict, a single step can mean permanent injury or death.

I am a Ph.D. student in the Imaging Science Department at Rochester Institute of Technology, working with Emmett Ientilucci. My research focuses on using drone-based, multisensor imagery and artificial intelligence to improve the speed, accuracy and reliability of land mine and unexploded ordnance detection.

Our research aims to do this in three ways: developing techniques for combining data from multiple types of sensors, building benchmark datasets for developing and evaluating detection systems, and improving the reliability of AI detection methods by incorporating estimates of uncertainty.

Multiple sensors from above

Land mine detection still relies heavily on ground-based methods, each with serious limitations. Handheld metal detectors often struggle in mineral-rich soils and have difficulty reliably detecting low-metal or predominantly plastic mines. Ground-penetrating radar can detect nonmetallic objects but performs poorly in wet or uneven terrain, or ground covered by vegetation, and often generates high false-alarm rates. Manual probing and trained detection animals remain effective methods for locating land mines, but the process is slow, resource-intensive and exposes deminers to considerable risk. At the scale of land mine deployment seen in Ukraine and other conflict and post-conflict regions, ground surveys alone cannot keep pace.

Demining operations are increasingly using drone-based aerial imagery to accelerate land mine surveys, particularly for mines laid on the surface of the ground. However, camouflage, vegetation and changing lighting conditions often make these mines nearly invisible in conventional images.

Earlier research my colleagues and I conducted examined whether aerial sensing can realistically replace or support traditional ground-based surveys. We evaluated the viability of substituting an airborne metal-detection system for handheld metal detectors for detecting land mines and unexploded ordnance.

The results showed that drone-mounted magnetic sensing can detect metallic targets with accuracy comparable to ground-based methods in a controlled test site, while reducing human risk and increasing survey speed approximately tenfold. Our heat map, generated by an airborne electromagnetic-induction metal detector over a test site, highlights likely locations of buried land mine and unexploded ordnance targets, illustrating how drone-based sensing can safely and efficiently survey areas where land mines have been deployed.

Aerial detection benefits from complementary sensors. RGB cameras, which detect visible light images in color, capture visual features of land mines. Thermal sensors reveal temperature differences between mines and the ground around them. Multispectral and hyperspectral sensors identify signatures of different materials. Synthetic-aperture radar detects changes in land surfaces. LiDAR maps subtle surface disturbances. And magnetometers detect underground metallic components. Together, these sensors can address the variety of mine types and deployment conditions found in real-world environments.

Despite their potential, multisensor, drone-based land mine detection systems remain underexplored. Progress is limited by the lack of publicly available benchmark datasets with data captured from multiple types of sensors using realistic mine deployments and precise ground truth, meaning the actual positions and depths of the target mines. Without such datasets, researchers cannot accurately compare algorithms, validate test results or develop AI models that work well outside of test environments.

Building mine-detection datasets

To address this challenge, our team along with several other researchers collaborated with the nonprofit Demining Research Community to collect a comprehensive dataset. We used the Demining Research Community’s controlled test field in Oklahoma, which included over 140 inert land mine and unexploded ordnance targets.

We collected a large, georeferenced, multisensor dataset using both ground-based and drone-based platforms at multiple altitudes. We used hyperspectral, multispectral, thermal, RGB, LiDAR, synthetic-aperture radar, ground-penetrating radar, electromagnetic induction metal detectors and magnetometers. This dataset will be released through a journal paper that is currently under review. We have released a portion of this collection – specifically a visible and near-infrared hyperspectral dataset acquired at an altitude of 20 meters – through a conference publication.

We expanded this effort internationally through a collaboration with the Royal Military Academy of Belgium during a large data-collection campaign. Together, we deployed over 110 replicas of PFM-1 mines across varied terrains and vegetation conditions.

To simulate realistic minefields, we scattered the inert mines to approximate aerial dispersal. We precisely surveyed and geolocated each mine using GPS base stations. We then collected data at multiple altitudes using drones outfitted with hyperspectral, multispectral, thermal, RGB, LiDAR and polarization sensors that reduce glare.

Other research groups, participants and industry partners, including sensor manufacturers, collected additional datasets over the same test field. These datasets are currently being processed and will be released as open-access in the near future.

To our knowledge, these will be the first publicly available datasets of their kind, opening new opportunities not only for land mine detection research but also for the broader AI and remote sensing community. By making these datasets openly available, we aim to accelerate research on multisensor data fusion, improve the reliability of AI-based detection systems, and help bridge the gap between academic research and the needs of industry developers and humanitarian organizations.

Measuring reliability

But even if you carefully calibrate your sensors using our dataset, you still need to recognize the limitations of the technology. In applications like land mine detection, a single mistake can be fatal. A major part of my research focuses on AI reliability and uncertainty estimation. In a recent study, we developed a measure of an AI model’s uncertainty about its predictions.

Rather than forcing models to produce confident predictions at all times, we are developing methods that allow systems to say, “I’m not sure.” Our goal is to provide an uncertainty metric alongside predictions: The noisier or more ambiguous the input, the higher the uncertainty score. This information can help demining operators make safer and more informed decisions, particularly in challenging or uncertain conditions.

With the release of these datasets, we believe new opportunities will emerge for researchers in AI and remote sensing to explore multisensor data fusion. The datasets include a wide variety of targets in terms of size, shape and orientation, with all data fully georeferenced and with precise ground truth. Because each target was observed by multiple sensors at multiple altitudes, researchers will be able to conduct comparative analyses of individual sensors versus combined sensing approaches. This will support the development of more reliable, safer and faster detection algorithms tailored to real-world demining needs.

At its core, this research is not about algorithms or drones, it is about people. It is about farmers reclaiming their land, children walking safely to school, and communities rebuilding without fear. By combining AI, drones and open science, we aim to transform land mine detection from a slow and dangerous practice into a safer, smarter and more scalable process, one that helps turn post-conflict landscapes back into places where life can grow again.

I have research collaborations with the Demining Research Community and the Royal Military Academy of Belgium, but no financial or commercial relationships related to this work.

– ref. Researchers are combining drones and AI to make removing land mines faster and safer – https://theconversation.com/researchers-are-combining-drones-and-ai-to-make-removing-land-mines-faster-and-safer-272248

Source: The Conversation – USA – By Vahe Peroomian, Professor of Physics and Astronomy, USC Dornsife College of Letters, Arts and Sciences

As a space scientist, every time I go outside with my family, I tell my children to look up at the sky. The front door of our home looks southeast, and on winter nights the constellation Orion hangs majestically just above the horizon as soon as it grows dark enough to see stars.

One summer night, my son came running in and exclaimed, “Dad, Orion’s not there!” It was time for his first real astronomy lesson.

We went outside and I asked him to find the Big Dipper, the easily identifiable pattern of stars that make up a portion of the constellation Ursa Major. I reminded him that we could always see the Big Dipper no matter what time of the year it was.

So, why is it that Orion is not always visible in the night sky, and certainly not in the same location month after month, while the Big Dipper always is? The answer is intimately tied to a few concepts: how astronomers measure the length of a day, the motion of the Earth around the Sun during a year, and the cadence with which stars rise and set night after night.

Sidereal time

If you look eastward at the same hour for two nights in a row, you’ll find that the stars seem to be in the same place. But they’re not, and this movement becomes apparent if you continue observing at the same hour for a week or more. A combination of the Earth’s daily rotation on its axis and its yearly orbit around the Sun cause them to appear to move across the sky.

Earth spins on its axis, which runs from the South Pole through the center of the Earth to the North Pole, once a day. Astronomers measure a day in two different ways: They measure a solar day, 24 hours long, with the position of the Sun from high noon to high noon. They measure a sidereal day with respect to distant stars that are fixed in the sky. A sidereal day is 23 hours and 56 minutes long.

The constellation Orion – and every star in the night sky – will appear in exactly the same place every 23 hours and 56 minutes. Because of this slight offset, stars will appear to rise four minutes earlier every 24 hours on successive nights. Over the course of a month, a star that was close to the eastern horizon at 10 p.m. will now be much higher in the sky, having risen two hours earlier.

So while the constellation Orion appears close to the horizon at sunset in late December, it is nearly overhead in February and March.

You can use an interactive star chart to see this phenomenon. Do you want to find Orion in August in North America? Just wake up at 4:30 a.m. and look eastward.

Unlike Orion, the Big Dipper is always visible at night in most of the Northern Hemisphere. This is because of how Earth’s daily rotation is projected onto the stars.

Circumpolar stars

Astronomers use a common set of reference points to project Earth’s north and south poles, and the equator, onto the celestial sphere, an imaginary sphere encompassing the sky.

The idea of the celestial sphere evolved in ancient times from the notion that the Earth was the unmoving center of the universe. The projection of Earth’s equator delineates the celestial equator, and the poles project onto the north and south celestial poles.

The motion of stars near the celestial poles differs from how Orion and other constellations behave. Presently, the north celestial pole is very close to the star Polaris, also known as the North Star. Stars close to Polaris never rise or set. They appear to circle counterclockwise around that star as the Earth spins on its rotation axis once a day.

The number of these circumpolar stars increases as you move toward the North Pole. There are no circumpolar stars at the equator. Every star and constellation rises in the east and sets in the west because Earth rotates west to east on its axis.

If you are standing at the North Pole, every northern constellation is circumpolar, circling the North Star and never rising or setting. The pattern is similar in the Southern Hemisphere, with the southern constellations circling clockwise around the south celestial pole.

Earth’s precession

Millennia ago, people charted the path of the Sun through the constellations of the zodiac, which birthed the practice of astrology.

What does it mean for the Sun to be in Sagittarius, for example? It means that to see the constellation Sagittarius, you have to be looking toward the Sun. That would make it daytime, when the stars are not visible. Wait for nightfall, and you can see Gemini high in the sky. Six months later, the Sun is in Gemini, and Sagittarius is visible in the night sky. This pattern repeats year after year, as the Earth orbits the Sun. Your zodiac signs depend on which constellation the Sun was in when you were born.

There is one other change in the night sky that occurs on time scales much longer than a human lifetime. Because of the gravitational influence of the Sun, and to a lesser extent Jupiter, on Earth’s daily rotation, Earth’s spin axis precesses, or moves in a circle, like a toy top spun on a table.

Because of this motion, which also subtly changes Earth’s orbit in space, Polaris will no longer be the North Star a thousand years from now. Wait 12,000 years, and the bright star Vega will be closest to the north celestial pole, more than 50 degrees across the night sky from its present location near Polaris.

Another consequence of this motion, sometimes referred to as the precession of the equinoxes, is that today the constellations of the zodiac no longer align with the traditional dates associated with them.

For example, when horoscopes and astrological signs were originally devised, the Sun was in the constellation Sagittarius from Nov. 22 to Dec. 21. However, because of precession over thousands of years, the Sun now crosses this constellation from Dec. 18 to Jan. 19. It spends the early part of December in Ophiuchus, which is not part of the traditional 12 constellations of the zodiac.

These changes in the night sky take weeks, months or even hundreds of years to be visible. If you’re not that patient, you can fly to the opposite hemisphere to see Orion upside down and the night sky turning in the opposite direction above.

Vahe Peroomian has, in the past, received funding from the National Science Foundation (NSF) and from the National Aeronautics and Space Administration (NASA) for research in the field of space science.

– ref. Why are some stars always visible while others come and go with the seasons? – https://theconversation.com/why-are-some-stars-always-visible-while-others-come-and-go-with-the-seasons-274096