Several missions have already attempted to land on the lunar surface in 2025, with more to come. AP PhotoZhenbo Wang, University of Tennessee Half a century after the Apollo astronauts left the last bootprints in lunar dust, the Moon has once again become a destination of fierce ambition and delicate engineering. This time, it’s not just superpowers racing to plant flags, but also private companies, multinational partnerships and robotic scouts aiming to unlock the Moon’s secrets and lay the groundwork for future human return. So far in 2025, lunar exploration has surged forward. Several notable missions have launched toward or landed on the Moon. Each has navigated the long journey through space and the even trickier descent to the Moon’s surface or into orbit with varying degrees of success. Together, these missions reflect both the promise and difficulty of returning to the Moon in this new space race defined by innovation, competition and collaboration. As an aerospace engineer specializing in guidance, navigation and control technologies, I’m deeply interested in how each mission – whether successful or not – adds to scientists’ collective understanding. These missions can help engineers learn to navigate the complexities of space, operate in hostile lunar environments and steadily advance toward a sustainable human presence on the Moon.
Why is landing on the Moon so hard?
Lunar exploration remains one of the most technically demanding frontiers in modern spaceflight. Choosing a landing site involves complex trade-offs between scientific interest, terrain safety and Sun exposure. The lunar south pole is an especially attractive area, as it could contain water in the form of ice in shadowed craters, a critical resource for future missions. Other sites may hold clues about volcanic activity on the Moon or the solar system’s early history. Each mission trajectory must be calculated with precision to make sure the craft arrives and descends at the right time and place. Engineers must account for the Moon’s constantly changing position in its orbit around Earth, the timing of launch windows and the gravitational forces acting on the spacecraft throughout its journey. They also need to carefully plan the spacecraft’s path so that it arrives at the right angle and speed for a safe approach. Even small miscalculations early on can lead to major errors in landing location – or a missed opportunity entirely. Once on the surface, the landers need to survive extreme swings in temperature – from highs over 250 degrees Fahrenheit (121 degrees Celsius) in daylight down to lows of -208 F (-133 C) at night – as well as dust, radiation and delayed communication with Earth. The spacecraft’s power systems, heat control, landing legs and communication links must all function perfectly. Meanwhile, these landers must avoid hazardous terrain and rely on sunlight to power their instruments and recharge their batteries. These challenges help explain why many landers have crashed or experienced partial failures, even though the technology has come a long way since the Apollo era. Commercial companies face the same technical hurdles as government agencies but often with tighter budgets, smaller teams and less heritage hardware. Unlike government missions, which can draw on decades of institutional experience and infrastructure, many commercial lunar efforts are navigating these challenges for the first time.
Successful landings and hard lessons for CLPS
Several lunar missions launched this year belong to NASA’s Commercial Lunar Payload Services program. CLPS is an initiative that contracts private companies to deliver science and technology payloads to the Moon. Its aim is to accelerate exploration while lowering costs and encouraging commercial innovation.An artist’s rendering of Firefly Aerospace’s Blue Ghost lander, which navigated and avoided hazards during its final descent to the surface.NASA/GSFC/Rani Gran/Wikimedia Commons The first Moon mission of 2025, Firefly Aerospace’s Blue Ghost Mission 1, launched in January and successfully landed in early March. The lander survived the harsh lunar day and transmitted data for nearly two weeks before losing power during the freezing lunar night – a typical operational limit for most unheated lunar landers. Blue Ghost demonstrated how commercial landers can shoulder critical parts of NASA’s Artemis program, which aims to return astronauts to the Moon later this decade. The second CLPS launch of the year, Intuitive Machines’ IM-2 mission, launched in late February. It targeted a scientifically intriguing site near the Moon’s south pole region.An artist’s rendering of Intuitive Machines’ IM-2 mission, which is scheduled to land near the lunar south pole for in-situ resource utilization demonstration on the Moon.NASA/Intuitive Machines The Nova-C lander, named Athena, touched down on March 6 close to the south pole. However, during the landing process, Athena tipped over. Since it landed on its side in a crater with uneven terrain, it couldn’t deploy its solar panels to generate power, which ended the mission early. While Athena’s tipped-over landing meant it couldn’t do all the scientific explorations it had planned, the data it returned is still valuable for understanding how future landers can avoid similar fates on the rugged polar terrain. Not all lunar missions need to land. NASA’s Lunar Trailblazer, a small lunar orbiter launched in February alongside IM-2, was intended to orbit the Moon and map the form, abundance and distribution of water in the form of ice, especially in shadowed craters near the poles. Shortly after launch, however, NASA lost contact with the spacecraft. Engineers suspect the spacecraft may have experienced a power issue, potentially leaving its batteries depleted. NASA is continuing recovery efforts, hoping that the spacecraft’s solar panels may recharge in May and June.An artist’s rendering of NASA’s Lunar Trailblazer spacecraft. If recovered, it will orbit the Moon to measure the form and distribution of water on the lunar surface.Lockheed Martin Space
Ongoing and future missions
Launched on the same day as the Blue Ghost mission in January, Japanese company ispace’s Hakuto-R Mission 2 (Resilience) is on its way to the Moon and has successfully entered lunar orbit. The lander carried out a successful flyby of the Moon on Feb. 15, with an expected landing in early June. Although launched at the same time, Resilience took a longer trajectory than Blue Ghost to save energy. This maneuver also allowed the spacecraft to collect bonus science observations while looping around the Moon. The mission, if successful, will advance Japan’s commercial space sector and prove an important comeback for ispace after its first lunar lander crashed during its final descent in 2023.The Resilience lunar lander days before its launch in the payload processing facility at the U.S. Space Force station. The Resilience lander has completed its Earth orbit and a lunar flyby. It is now completing a low-energy transfer orbit and entering an orbit around the Moon.Business Wire The rest of 2025 promises a busy lunar calendar. Intuitive Machines plans to launch IM-3 in late 2025 to test more advanced instruments and potentially deliver NASA scientific experiments to the Moon. The European Space Agency’s Lunar Pathfinder will establish a dedicated lunar communications satellite, making it easier for future missions, especially those operating on the far side or poles, to stay in touch with Earth. Meanwhile, Astrobotic’s Griffin Mission-1 is scheduled to deliver NASA’s VIPER rover to the Moon’s south pole, where it will directly search for ice beneath the surface. Together, these missions represent an increasingly international and commercial approach to lunar science and exploration. As the world turns its attention to the Moon, every mission – whether triumph or setback – brings humanity closer to a permanent return to our closest celestial neighbor. Zhenbo Wang, Associate Professor of Mechanical and Aerospace Engineering, University of Tennessee This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Beneath the Waves: The Global Push to Build Undersea Railways
Undersea railways are transforming transportation, turning oceans from barriers into gateways. Proven by tunnels like the Channel and Seikan, these innovations offer cleaner, reliable connections for passengers and freight. Ongoing projects in China and Europe, alongside future proposals, signal a new era of global mobility beneath the waves.
Trains beneath the ocean are no longer science fiction—they’re already in operation.
For most of modern history, oceans have acted as natural barriers—dividing nations, slowing trade, and shaping how cities grow. But beneath the waves, a quiet transportation revolution is underway. Infrastructure once limited by geography is now being reimagined through undersea railways.
Undersea rail tunnels—like the Channel Tunnel and Japan’s Seikan Tunnel—proved decades ago that trains could reliably travel beneath the ocean floor. Today, new projects are expanding that vision even further.
Around the world, engineers and governments are investing in undersea railways—tunnels that allow high-speed trains to travel beneath oceans and seas. Once considered science fiction, these projects are now operational, under construction, or actively being planned.
Undersea Rail Is Already a Reality
Japan’s Seikan Tunnel and the Channel Tunnel between the United Kingdom and France proved decades ago that undersea railways are not only possible, but reliable. These tunnels carry passengers and freight beneath the sea every day, reshaping regional connectivity.
Undersea railways are cleaner than short-haul flights, more resilient than bridges, and capable of lasting more than a century. As climate pressures and congestion increase, rail beneath the sea is emerging as a practical solution for future mobility.
What’s Being Built Right Now
China is currently constructing the Jintang Undersea Railway Tunnel as part of the Ningbo–Zhoushan high-speed rail line, while Europe’s Fehmarnbelt Fixed Link will soon connect Denmark and Germany beneath the Baltic Sea. These projects highlight how transportation and technology are converging to solve modern mobility challenges.
Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain
With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.
Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.
Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.
As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.
Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.
A faster IEP, but how individualized?
AI is already shaping special education planning, personnel preparation and assessment.
One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.
But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.
Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.
Training and diagnosing disabilities
There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.
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Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.
Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.
Privacy and trust concerns
There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.
What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.
These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.
But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.
Embedded bias
At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.
Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.
Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.
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By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.
Filling the gap
Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.
The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.
But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.
Fact Check: Did Mike Rogers Admit the Travis Walton UFO Case Was a Hoax?
A fact check of viral claims that Mike Rogers admitted the Travis Walton UFO case was a hoax. We examine the evidence, the spotlight theory, and what the record actually shows.
In recent years, viral YouTube videos and podcast commentary have revived claims that the 1975 Travis Walton UFO abduction case was an admitted hoax. One of the most widely repeated allegations asserts that Mike Rogers, the logging crew’s foreman, supposedly confessed that he and Walton staged the entire event using a spotlight from a ranger tower to fool their coworkers.
So, is there any truth to this claim?
After reviewing decades of interviews, skeptical investigations, and public records, the answer is clear:
There is no verified evidence that Mike Rogers ever admitted the Travis Walton incident was a hoax.
He Vanished for 5 Days After a UFO Encounter | Travis Walton Case
Where the Viral Claim Comes From
The “confession” story has circulated for years in online forums and was recently amplified by commentary-style YouTube and podcast content, including popular personality-driven shows. These versions often claim:
Rogers and Walton planned the incident in advance
A spotlight from a ranger or observation tower simulated the UFO
The rest of the crew was unaware of the hoax
Rogers later “admitted” this publicly
However, none of these claims are supported by primary documentation.
What the Documented Record Shows
No Recorded Confession Exists
There is no audio, video, affidavit, court record, or signed statement in which Mike Rogers admits staging the incident.
Rogers has repeatedly denied hoax allegations in interviews spanning decades.
Even prominent skeptical organizations do not cite any confession by Rogers.
If such an admission existed, it would be widely referenced in skeptical literature and would have effectively closed the case. It has not.
The “Ranger Tower Spotlight” Theory Lacks Evidence
No confirmed ranger tower or spotlight installation matching the claim has been documented at the location.
No ranger, third party, or equipment operator has ever come forward.
No physical evidence or corroborating testimony supports this explanation.
Even professional skeptics typically label this idea as speculative, not factual.
Why Skepticism Still Exists (Legitimately)
While the viral claim lacks evidence, skepticism about the Walton case is not unfounded. Common, well-documented critiques include:
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Financial pressure tied to a logging contract
The limitations and inconsistency of polygraph testing
Walton’s later use of hypnosis, which is controversial in memory recall
Possible cultural influence from 1970s UFO media
Importantly, none of these critiques rely on a confession by Mike Rogers, because none exists.
Updates & Current Status of the Case
As of today:
No new witnesses have come forward to confirm a hoax
No participant has recanted their core testimony
No physical evidence has conclusively proven or disproven the event
Walton and Rogers have both continued to deny hoax allegations
The case remains unresolved, not debunked.
Why Viral Misinformation Persists
Online commentary formats often compress nuance into dramatic statements. Over time:
Speculation becomes repeated as “fact”
Hypothetical explanations are presented as admissions
Entertainment content is mistaken for investigative reporting
This is especially common with long-standing mysteries like the Walton case, where ambiguity invites exaggeration.
Viral Claims vs. Verified Facts
Viral Claim:
Mike Rogers admitted he and Travis Walton staged the UFO incident.
Verified Fact:
No documented confession exists. Rogers has consistently denied hoax claims.
Viral Claim:
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A ranger tower spotlight was used to fake the UFO.
Verified Fact:
No evidence confirms a tower, spotlight, or third-party involvement.
Viral Claim:
The case was “officially debunked.”
Verified Fact:
No authoritative body has conclusively debunked or confirmed the incident.
Viral Claim:
All skeptics agree it was a hoax.
Verified Fact:
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Even skeptical researchers acknowledge the absence of definitive proof.
Viral Claim:
Hollywood exposed the truth in Fire in the Sky.
Verified Fact:
The film significantly fictionalized Walton’s testimony for dramatic effect.
Bottom Line
❌ There is no verified admission by Mike Rogers
❌ There is no evidence of a ranger tower spotlight hoax
✅ There are legitimate unanswered questions about the case
✅ The incident remains debated, not solved
The Travis Walton story persists not because it has been proven — but because it has never been conclusively explained.
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