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The U.S. House Committee on Education and Workforce held its first artificial intelligence field hearing of the year, titled "Building an AI-Ready America: How AI Is Creating Opportunities Across America's Workforce," at the Georgia Cyber Center at Augusta University on July 24.
U.S. Rep. Rick W. Allen, who represents Georgia's 12th District and chairs the committee's Subcommittee on Health, Employment, Labor, and Pensions, chaired the hearing. He was joined by U.S. Rep. Joe Wilson of South Carolina and U.S. Rep. Lucy McBath of Georgia.
Jeffery Talbert, PhD, chair of Augusta University's Department of Artificial Intelligence and Health at the Medical College of Georgia and a Georgia Research Alliance Eminent Scholar, served as one of four witnesses at the hearing.
"Today, we examine how artificial intelligence, AI, is creating economic opportunities for American workers, job creators and our communities," Allen told the committee. "There's no better place to hold this hearing than right here in Augusta." Allen said the combination of the Georgia Cyber Center and Fort Gordon provides the expertise needed to discuss AI. "Fort Gordon is just a short 30-minute drive from where we currently sit," he said. "It hosts the Army Cyber Center of Excellence and is home to Army Cyber School, which trains, educates and develops the Army's Cyberspace and Electronic Warfare workforce. Every year, thousands of people leave Fort Gordon looking for work."
He added that the Georgia Cyber Center "was created to meet this growing demand and drive collaboration between academia, government and industry stakeholders to equip a superior cybersecurity workforce with the skills they need."
Augusta University President Russell T. Keen told the committee it was an honor for AU's Georgia Cyber Center to host the hearing.
"Congressman Allen has long recognized the important role that education, innovation and workforce development play in strengthening our state and our nation," Keen said. He added that AI "will continue to change how we live, how we learn, how we work and how we solve problems," and that Augusta University "intends to lead in that transformation in ways that strengthen our workforce, advance discovery, improve lives, change lives and save lives."
Talbert, who has more than 30 years of experience in biomedical informatics, has published approximately 260 times and has led more than 100 funded research projects totaling more than $130 million. He told the committee that in health care, AI is demonstrating its greatest value through augmentation, helping professionals "work more effectively, reducing administrative burdens, improving patient outcomes and expanding workforce capacity."
He pointed to ambient documentation technology, which converts clinical conversations into draft notes clinicians review and approve, as one of the industry's most successful AI applications. "Multiple studies show these tools reduce documentation burden, after-hours work and burnout," Talbert said. "One multisystem implementation found clinician burnout fell from approximately 52 percent to 39 percent." He also cited studies showing AI-assisted breast cancer screening detecting approximately 29 percent higher cancer rates, "helping more patients benefit from earlier diagnosis and treatment."
America's opportunity, Talbert said, "is not simply to adopt AI, but to lead its responsible development, education and implementation." He pointed to Augusta University's Department of AI and Health, the first of its kind in Georgia, as an example of that approach. Allen closed the hearing by cautioning against a one-size-fits-all approach to AI policy. "The needs of a family farm are not the same as those of a hospital, a manufacturing plant or a small business," he said. "Congress must pursue flexible policies that allow businesses to adopt AI in ways that best suit their industry and workforce."
JagWire has the full recap of the hearing and the Augusta Chronicle, Innovation & Entrepreneurs News and Traders Union also covered the field hearing.
Augusta University experts in artificial intelligence, healthcare innovation and workforce development are available for interviews. If you're covering similar stories, reach out to schedule time.
Artificial intelligence has moved from novelty to daily habit in classrooms across the country, and the debate over what that means for learning shows no sign of slowing down. Some schools have built AI into their curriculum. Others have banned it outright. Caught in the middle are students trying to figure out where the technology actually helps and where it quietly gets in the way.
Trent Kays, PhD, an assistant professor and Director of Professional Writing and Rhetoric at Augusta University, has spent time researching exactly that question, and he recently sat down with FOX54 News to talk through what he's seeing firsthand.
Kays, who also serves as a faculty affiliate in Women's and Gender Studies and specializes in digital rhetoric and machine writing, says students are already using AI for far more than quick homework help, from brainstorming and studying to building out entire lesson plans on their own.
The bigger concern, according to Kays, isn't misuse in the obvious sense. It's what he calls cognitive offloading, students handing the thinking itself over to the machine. "Students are essentially using AI to do their thinking for them, which is not something that they should do," he said. It's part of why he keeps AI out of his first- and second-year courses entirely: those early classes are where students build the critical thinking skills they'll need before a tool can actually help. "They have to be able to think critically first before they can ask a tool to help them." Accuracy is its own problem. AI tools have gotten sharper, but Kays points out they still fabricate information regularly, including sources that were never real to begin with, something students are far less likely to catch than an instructor who actually reads the citations.
None of this means Kays thinks AI belongs outside the classroom. He describes it as a potential process partner, something that can push a student's thinking further rather than replace it, as long as the structure around it is clear.
As he put it, what schools need now is to "craft clear guidelines and structures for students to follow so that they know how to use it and how to use it well." Read the full interview and FOX54's coverage here: AI in the Classroom: Augusta University Professor Explains the Benefits and Risks Dr. Trent Kays is available for interviews on AI in education, digital rhetoric, and responsible AI use in academic settings. If you're covering similar stories, click on this icon to schedule time with him.
Quantum computing is often framed as a hardware race with attention fixed on machines that promise amazing speed. But a foundational mathematical problem remains unresolved.
No complete framework yet exists for converting massive amounts of data generated by fields such as healthcare, disease surveillance, financial transactions and climate monitoring into units a quantum machine can process.
“What exactly we are proposing is this: data is continuous,” said Arni S.R. Srinivasa Rao, PhD, professor and director of the Laboratory for Theory and Mathematical Modeling, Division of Infectious Diseases, Medical College of Georgia at Augusta University. “That means the generation of the data is continuously happening. And how to slice it down into pieces, that is what it is about. The slicing down of the data into smaller pieces, not too small pieces, but smaller enough for the computers to handle them in a meaningful way.”
Rao and Steven G. Krantz, a professor of mathematics at Washington University in St. Louis, published their findings on “The Role of Quantization in Quantum Computing” in the July/August 2026 issue of SIAM News, published by the Society for Industrial and Applied Mathematics.
“Quantization is a mathematical method, nothing to do with quantum computing,” Rao said. “But these two we are trying to mix together.” The problem with just chopping it up Classical computers store information as bits, switches that are either off or on, zero or one. Quantum computers use qubits, which can hold combinations of both states simultaneously, which is why they can theoretically process vastly more information at once.
But qubits operate at the atomic level. Getting that data into a quantum machine requires breaking it down into atomic-scale pieces first. The math for doing that well, Rao and Krantz argue, does not yet exist in any complete form.
The hard part is “slicing” it right.
“Not slicing in a random, arbitrary way,” Rao said. “If you slice it in a way which is arbitrary, then you might miss, you might necessarily slice it where no information is required. Information is broken into pieces rather than combining the information. That’s what the mathematics is playing a role here.”
Their research proposes using an ensemble of quantization techniques rather than a single method, with different mathematical tools matched to the different layers inside a dataset. A disease surveillance file might bundle together demographic data, income levels, infection status and housing type, for example. Each layer has its own structure. The partitioning, they argue, should flex to fit the problem rather than forcing every dataset through the same process.
Mixing two worlds that don’t quite line up The second major contribution is a formally defined measure the authors call “atomic uncertainty.” It quantifies the mismatch between quantized data and the atomic structure that data is supposed to travel through inside a quantum processor.
The mismatch exists because atomic models, the diagrams of electrons and nuclei that physics students study, are approximations. The actual behavior of electrons inside an atom follows probability distributions. There is inherent randomness in there that scientists have not resolved.
Planets are called spheres, and textbooks draw them as smooth perfect balls, Rao explained, but their surfaces are cratered and irregular. The sphere is a useful model, not an accurate description. Atomic structure works the same way.
When quantized data does not fit cleanly into that imperfectly understood atomic structure, the gap becomes computational error. At small scale, manageable. At the scale of real-world data inputs, those errors can compound into results that are simply wrong.
“We are not trying to fix the error. We are trying to see how big the error is,” Rao said. This study illustrates the concept with a disease-modeling example, building a dataset that layers population age, income bracket, infection status and housing type. When the data is quantized and compared against a model atomic structure, the spaces do not fully align. That leftover mismatch is the atomic uncertainty. In a real quantum system processing actual disease data, it does not disappear but shows up as wrong answers about how a disease spreads.
The next step, Rao said, requires industry partners to take the framework and build hardware around it.
“Designing a computer is different from the actual making a computer,” Rao said. “We designed the method of slicing the larger, huge continuous data into smaller digestible computer bits, that’s called qubits. The industrialists have to build it. Like designing a mobile phone is different from actually making a mobile phone.”
