Several missions have already attempted to land on the lunar surface in 2025, with more to come. AP Photo
NASA wants to put a nuclear reactor on the Moon by 2030 – choosing where is tricky
Clive Neal, University of Notre Dame In a bold, strategic move for the U.S., acting NASA Administrator Sean Duffy announced plans on Aug. 5, 2025, to build a nuclear fission reactor for deployment on the lunar surface in 2030. Doing so would allow the United States to gain a foothold on the Moon by the time China plans to land the first taikonaut, what China calls its astronauts, there by 2030. Apart from the geopolitical importance, there are other reasons why this move is critically important. A source of nuclear energy will be necessary for visiting Mars, because solar energy is weaker there. It could also help establish a lunar base and potentially even a permanent human presence on the Moon, as it delivers consistent power through the cold lunar night. As humans travel out into the solar system, learning to use the local resources is critical for sustaining life off Earth, starting at the nearby Moon. NASA plans to prioritize the fission reactor as power necessary to extract and refine lunar resources. As a geologist who studies human space exploration, I’ve been mulling over two questions since Duffy’s announcement. First, where is the best place to put an initial nuclear reactor on the Moon, to set up for future lunar bases? Second, how will NASA protect the reactor from plumes of regolith – or loosely fragmented lunar rocks – kicked up by spacecraft landing near it? These are two key questions the agency will have to answer as it develops this technology.
Where do you put a nuclear reactor on the Moon?
The nuclear reactor will likely form the power supply for the initial U.S.-led Moon base that will support humans who’ll stay for ever-increasing lengths of time. To facilitate sustainable human exploration of the Moon, using local resources such as water and oxygen for life support and hydrogen and oxygen to refuel spacecraft can dramatically reduce the amount of material that needs to be brought from Earth, which also reduces cost. In the 1990s, spacecraft orbiting the Moon first observed dark craters called permanently shadowed regions on the lunar north and south poles. Scientists now suspect these craters hold water in the form of ice, a vital resource for countries looking to set up a long-term human presence on the surface. NASA’s Artemis campaign aims to return people to the Moon, targeting the lunar south pole to take advantage of the water ice that is present there.Dark craters on the Moon, parts of which are indicated here in blue, never get sunlight. Scientists think some of these permanently shadowed regions could contain water ice.NASA’s Goddard Space Flight Center In order to be useful, the reactor must be close to accessible, extractable and refinable water ice deposits. The issue is we currently do not have the detailed information needed to define such a location. The good news is the information can be obtained relatively quickly. Six lunar orbital missions have collected, and in some cases are still collecting, relevant data that can help scientists pinpoint which water ice deposits are worth pursuing. These datasets give indications of where either surface or buried water ice deposits are. It is looking at these datasets in tandem that can indicate water ice “hot prospects,” which rover missions can investigate and confirm or deny the orbital observations. But this step isn’t easy. Luckily, NASA already has its Volatiles Investigating Polar Exploration Rover mission built, and it has passed all environmental testing. It is currently in storage, awaiting a ride to the Moon. The VIPER mission can be used to investigate on the ground the hottest prospect for water ice identified from orbital data. With enough funding, NASA could probably have this data in a year or two at both the lunar north and south poles.The VIPER rover would survey water at the south pole of the Moon.
How do you protect the reactor?
Once NASA knows the best spots to put a reactor, it will then have to figure out how to shield the reactor from spacecraft as they land. As spacecraft approach the Moon’s surface, they stir up loose dust and rocks, called regolith. It will sandblast anything close to the landing site, unless the items are placed behind large boulders or beyond the horizon, which is more than 1.5 miles (2.4 kilometers) away on the Moon. Scientists already know about the effects of landing next to a pre-positioned asset. In 1969, Apollo 12 landed 535 feet (163 meters) away from the robotic Surveyor 3 spacecraft, which showed corrosion on surfaces exposed to the landing plume. The Artemis campaign will have much bigger lunar landers, which will generate larger regolith plumes than Apollo did. So any prepositioned assets will need protection from anything landing close by, or the landing will need to occur beyond the horizon. Until NASA can develop a custom launch and landing pad, using the lunar surface’s natural topography or placing important assets behind large boulders could be a temporary solution. However, a pad built just for launching and landing spacecraft will eventually be necessary for any site chosen for this nuclear reactor, as it will take multiple visits to build a lunar base. While the nuclear reactor can supply the power needed to build a pad, this process will require planning and investment. Human space exploration is complicated. But carefully building up assets on the Moon means scientists will eventually be able to do the same thing a lot farther away on Mars. While the devil is in the details, the Moon will help NASA develop the abilities to use local resources and build infrastructure that could allow humans to survive and thrive off Earth in the long term. Clive Neal, Professor of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame This article is republished from The Conversation under a Creative Commons license. Read the original article.
Valerie Thomas is a true pioneer in the world of science and technology. A NASA engineer and physicist, she is best known for inventing the illusion transmitter, a groundbreaking device that creates 3D images using concave mirrors. This invention laid the foundation for modern 3D imaging and virtual reality technologies.
Beyond her inventions, Thomas broke barriers as an African American woman in STEM, mentoring countless young scientists and advocating for diversity in science and engineering. Her work at NASA’s Goddard Space Flight Center helped advance satellite technology and data visualization, making her contributions both innovative and enduring.
In our latest short video, we highlight Valerie Thomas’ remarkable journey—from her early passion for science to her groundbreaking work at NASA. Watch and be inspired by a true STEM pioneer whose legacy continues to shape the future of space and technology.
Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter. https://stmdailynews.com/the-knowledge/
A Short-Form Series from The Knowledge by STM Daily News
Every Friday, STM Daily News shines a light on brilliant minds history overlooked.
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Forgotten Genius Fridays is a weekly collection of short videos and articles dedicated to inventors, innovators, scientists, and creators whose impact changed the world—but whose names were often left out of the textbooks.
From life-saving inventions and cultural breakthroughs to game-changing ideas buried by bias, our series digs up the truth behind the minds that mattered.
Each episode of The Knowledge runs 30–90 seconds, designed for curious minds on the go—perfect for YouTube Shorts, TikTok, Reels, and quick reads.
Because remembering these stories isn’t just about the past—it’s about restoring credit where it’s long overdue.
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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.
