Artificial Intelligence

partnerships

High school students build AI solutions at College of Public Health summer camp

Heather Carroll — Mon, 17 Aug 2026 15:34:46 +0000

High school students build AI solutions at College of Public Health summer camp Heather Carroll Mon, 08/17/2026 - 11:34

Key Takeaways

Through the AI in Health Camp, the College of Public Health helped high school students develop a practical understanding of artificial intelligence and how it can be used responsibly to improve health and health care.
Through hands-on projects, students created their own AI agents and applied what they learned to real-world challenges ranging from healthy sleep habits to athlete recovery and braces care.
The camp gave students confidence using emerging technology while introducing them to career opportunities at the intersection of health, technology, and public service.

Through hands-on projects and mentorship from experts, high school students learned how to use artificial intelligence (AI) responsibly, create an AI agent, and explore how AI is used to improve health care.

Students attending the AI in Health Summer Camp
Photo by Mary Cunningham

At 911��'s College of Public Health, 30 juniors and seniors spent a week doing what few adults have done: building their own AI agent. While many high schools restrict the use of AI, the AI in Health Summer Camp—hosted by the Department of Health Administration, Policy, and Informatics—showed students how AI can be used responsibly to solve real-world problems and improve lives.

By the end of the week, students could explain how AI works, build and customize their own AI agents in Microsoft Copilot, identify health care applications of AI, and better understand careers at the intersection of technology and public health. Students reported learning about the growing role of AI in health care, the complexity of hospital systems, the importance of responsible AI design, and how to apply AI skills to healthcare and future career opportunities.

"We're not allowed to use AI in school. I haven't learned anything [about AI] in school, which is why I'm here," said a participant.

Abdul Hafeez, Assistant Professor in Health Administration, Policy, and Informatics, works with students at the AI in Health Summer Camp
Photo by Mary Cunningham

For many students, the camp challenged their assumptions about AI.

“I learned a lot about how AI is used in health care. One of my favorite parts was programming AI agents and seeing how they could be used to solve specific healthcare problems. The camp was engaging, informative, and a great learning opportunity,” said a participant in the post-camp survey.

Others left with greater confidence in their ability to use emerging technology.

"Before, I thought AI would be complicated, but it's really simple to figure out," said a participant.

Working in teams of two, students created customized AI agents designed to address specific health-related needs. Their projects ranged from tools that encourage healthy sleep habits among students to AI assistants that support athlete nutrition and injury recovery, as well as chatbots that answer questions about braces and agents that help users learn essential cooking skills.

“Learning about chatbots and AI through the AI in Health Summer Camp was a rewarding experience and something I will carry with me as I continue exploring my interests. I am grateful to the professors and teaching assistants who made the camp such a meaningful learning experience,” said a participant in the post-camp survey.

Student learning about a chatbot during the AI in Health Summer Camp
Photo by Mary Cunningham

In addition to working with the college's faculty, students learned directly from health care professionals who shared perspectives on AI, technology, and the changing health workforce. Guest speakers included Jim Borda, public health informatics and IT manager at the Fairfax County Health Department; Jessica Rocca Czelusniak, associate vice president of applications and CNIO at VHC Health; and College of Public Health alums Kate Rzucidlo, Claire Leonard, and Iqra Chaudhry.

For the College of Public Health, the camp represents an investment in the next generation of leaders who will help shape the future of health care. For students, it offered something just as valuable: the knowledge and confidence to begin imagining their place in that future.

Topics

Summer Camps

Health Informatics

Katherine Scafide, PhD, RN, FAAN, FAAFS

Students

911��’s Injury Analytics Lab receives patent for AI bruise detection system

Heather Carroll — Mon, 10 Aug 2026 14:11:25 +0000

911��’s Injury Analytics Lab receives patent for AI bruise detection system Heather Carroll Mon, 08/10/2026 - 10:11

Newly patented AI technology helps get bruise detection tools into the hands of clinicians, law enforcement, and researchers

Can new technology make it easier to understand bruises? That's the question researchers at 911��'s Injury Analytics Lab (IAL) are answering with a patented technology that combines artificial intelligence, advanced imaging, and clinical data to give clinicians, forensic professionals, and researchers better information about skin injuries.

This technology reflects years of collaboration among experts in nursing, health informatics, and engineering, who worked together to transform research into a practical tool for injury assessment.

How the technology works

Image by College of Public Health.

The newly patented technology addresses current challenges in injury assessment by analyzing images of the skin alongside relevant clinical and demographic information from electronic health records. Similar to popular plant identification apps that combine image data with location information, the system uses AI to detect and assess bruise lesions.

"Documenting injuries accurately is essential for both medical treatment and seeking justice for victims of violence," said Associate Professor of Nursing and IAL Co-Director Katherine Scafide. "This patent recognizes our innovative approach that combines artificial intelligence, advanced imaging, and clinical data to help professionals make more informed decisions."

The patented artificial intelligence technology is built on a dedicated injury platform, which includes a large repository of injury images used by computer vision systems, and data from electronic health records that work together to support accurate injury assessment and real-world use. Development of the platform that hosts the data and systems, and related software is led by Professor of Health Informatics and IAL Co-Director Janusz Wojtusiak, while Professor of Engineering and IAL Co-Director David Lattanzi leads development of the computer vision systems and user-centered design.

One of the most innovative aspects of the patented technology is its ability to tailor imaging conditions such as light wavelength and camera filter to each individual.

Rather than relying on a standard lighting setup, the system can determine a person's skin color and select the most effective light wavelengths, filters, and imaging conditions for capturing injury evidence. By optimizing imaging for each person, the technology can improve injury detection and assessment across a wide range of skin tones, especially for people with darker skin tones, whose injuries can be harder to identify with traditional imaging.

But the technology goes beyond simply determining whether a bruise is present.

The system can estimate characteristics such as a bruise's age, size, color, and shape while also providing information about the confidence of its predictions. By combining visual data with information such as medical history, medications, demographics, and skin color, the technology can account for factors that influence how injuries appear, resulting in more accurate and reliable assessments than images alone.

Why the technology matters

Janusz Wojtusiak and Katherine Scafide in the Injury Analytics Lab. Photo provided

Bruises and other skin injuries have traditionally been difficult to identify and assess. Factors such as skin tone, lighting conditions, and the age of an injury can affect how visible an injury appears, creating challenges for clinicians, forensic professionals, and researchers.

This patented technology helps address those challenges by providing more objective, consistent injury assessments. More accurate documentation can help clinicians make informed treatment decisions, strengthen forensic evidence, support investigations, and give researchers better data to improve future care. By improving injury detection across diverse skin tones, the technology also has the potential to reduce disparities in how injuries are documented and evaluated.

It was also shaped by the needs of the professionals who will use it in real-world settings. “This technology is the result of a user-centric research process that included countless hours of interviews and workshops with health care providers and forensic professionals,” explains Lattanzi. “These groups told us that they need easy-to-use tools that make documentation faster and more reliable. So that’s exactly what we’re building.”

Built on years of research

The patented technology is supported by a library of more than 100,000 images of bruises and other skin characteristics collected through years of interdisciplinary research and collaboration.

The technology identifies and analyzes a wide range of skin features, including bruises, burns, scars, birthmarks, rashes, eczema, psoriasis, and other conditions. This extensive dataset helps train the system's deep-learning models and enables it to recognize patterns that may be difficult for the human eye to detect consistently.

Moving research into practice

The patent marks an important milestone in IAL's broader effort to develop evidence-based technologies that can improve injury assessment and documentation.

Beyond protecting the intellectual property behind the innovation, the patent creates opportunities for new partnerships, commercialization, and broader adoption of the technology. Researchers envision future applications that support health care providers, forensic specialists, law enforcement professionals, researchers, and patients through interfaces tailored to their specific needs.

As the technology continues to advance, the goal remains clear: transforming cutting-edge research into practical tools that improve accuracy, support equity, and help professionals make better-informed decisions when injury documentation matters most.

About the Injury Analytics Lab

Launched in 2026, 911��’s IAL brings together researchers from clinical science, engineering, and health informatics to advance ongoing research focused on injury detection, assessment, and documentation. The lab serves as a hub for interdisciplinary projects that build on years of research to improve patient care, investigations, and legal proceedings.

IAL is directed by an interdisciplinary team including Katherine Scafide (School of Nursing, College of Public Health [CPH]), who leads the lab’s clinical research; Janusz Wojtusiak (Department of Health Administration, Policy, and Informatics, CPH), who leads development of the injury platform and software systems; and David Lattanzi (Volgenau School of Engineering, College of Engineering and Computing), who leads development of the computer vision systems and user-centered design.

Key Takeaways

911�� researchers received a U.S. patent for technology that helps identify, document, and assess bruises and other skin injuries.
The technology tailors imaging to each individual, helping improve injury detection across a wide range of skin tones, particularly for people with darker skin tones.
The patented technology is the result of interdisciplinary collaboration among researchers in nursing, engineering, and health informatics, demonstrating how expertise across multiple fields can advance injury assessment and documentation.
The patent creates opportunities to bring the technology into healthcare, forensic, and research settings, supporting better clinical care, investigations, and future innovation.

In This Story

Janusz Wojtusiak, PhD

David Lattanzi

Topics

injury analytics lab

Bruise Visibility

patent

College of Engineering and Computing

GCI-Health

GCI-Digital

911�� computer scientist receives DOE Genesis Award for breakthrough AI–HPC research

ckearney — Tue, 04 Aug 2026 17:26:41 +0000

911�� computer scientist receives DOE Genesis Award for breakthrough AI–HPC research ckearney Tue, 08/04/2026 - 13:26

911�� computer science professor Keren Zhou has been selected by the U.S. Department of Energy (DOE) for a prestigious Genesis Award, part of the Trump Administration’s national Genesis Mission—an initiative designed to accelerate scientific discovery through advanced artificial intelligence (AI) and high‑performance computing.

Keren Zhou. Photo provided

The Genesis Mission aims to build an integrated scientific discovery platform that unites national laboratories, universities, industry partners, and philanthropic organizations to drive breakthroughs in energy, discovery science, and national security. Nearly 300 projects were selected nationwide following what DOE described as the largest response to a funding opportunity in its history.

Zhou’s project, “An End‑to‑End AI Framework for Performance Prediction and Optimization in High‑Performance Computing (HPC) Applications,” received $750,000 in Phase I funding, supporting a nine‑month research period. His work tackles a challenge that affects nearly every field that relies on supercomputers—from climate science to drug development. Today, even small changes to scientific software can require days or months of trial‑and‑error testing to make sure programs run efficiently on different machines. That slows down research and delays discoveries.

Zhou’s goal is to remove that bottleneck. “Today’s HPC applications may need days or even months of tuning every time a parameter changes or a new hardware configuration is introduced,” he explained. “We want to eliminate that costly cycle by building a proxy model that can rapidly estimate performance—reducing simulation time from months to seconds.”

In practical terms, his system acts like a “speed‑check” for scientific computing: Instead of running full, time‑consuming tests, researchers could quickly predict how their programs will perform and automatically identify faster, more efficient ways to run them. With AI systems now capable of rewriting code and suggesting improvements, Zhou’s framework could help scientists optimize their work in minutes rather than weeks.

Zhou is the sole 911�� investigator on the project but will hire postdoctoral researchers as the work advances. Through the Genesis Mission’s shared platform, 911�� students will gain access to industrial partners such as AWS, OpenAI, and Google, along with AI tokens and cloud computing resources that support hands‑on research. He is also collaborating with Oak Ridge National Laboratory and Brookhaven National Laboratory.

As Phase I progresses, Zhou aims to show that his approach works across different scientific applications and hardware systems, delivers significantly faster performance, and does so without requiring long simulation runs. Strong results would position his team for Phase II funding, which the DOE expects to award next year.

For Zhou, the Genesis Award marks an important milestone. “It’s a recognition of my past contributions to strengthening U.S. scientific discovery,” he said. “It also motivates me to continue collaborating with national labs and advancing this domain.” The project aligns closely with his long‑term goal of making advanced computing systems easier to program while maintaining high efficiency.

“The Genesis Mission rests on a clear premise: AI is becoming an instrument of science, not only an object of it. Professor Zhou’s framework is a clear case, a model that predicts and optimizes the systems on which discovery now depends, compressing months of tuning into seconds,” said Amarda Shehu, 911��’s vice president and chief AI officer. “911��’s AI strategy is aimed precisely at this layer, the capability that makes every other field move faster.”

Andre Marshall, 911��’s vice president for research, innovation, and economic impact, said this award underscores the university’s growing role in advancing the nation’s scientific and technological capabilities. “Professor Zhou’s project elevates our visibility in the federal research ecosystem and demonstrates how 911�� is contributing to the next generation of AI‑driven discovery.”

Zhou hopes the project will serve as a catalyst for future partnerships and position 911�� as a leading contributor to the next generation of AI infrastructure—advancing both national scientific priorities and the university’s growing research impact.

Explore Artificial Intelligence at 911��

Topics

College of Engineering and Computing

Department of Energy

Genesis Award

Mason Enterprise and Akiak Technology establish strategic partnership to advance innovation, entrepreneurship, and economic opportunity

Katarina Benson — Thu, 23 Jul 2026 20:24:14 +0000

Mason Enterprise and Akiak Technology establish strategic partnership to advance innovation, entrepreneurship, and economic opportunity Katarina Benson Thu, 07/23/2026 - 16:24

Mason Enterprise^TM, 911��’s hub for innovation and entrepreneurial development, established a new partnership with Akiak Technology LLC, an Alaskan tribal-owned, mission-driven technology firm dedicated to delivering impactful artificial intelligence (AI) solutions while supporting Indigenous communities. The collaboration is formalized through a Memorandum of Understanding (MOU) and reflects a shared commitment to fostering business innovation, workforce development, and mutually beneficial growth opportunities.

Mason Enterprise and Akiak Technology will collaborate on technology and business development initiatives designed to expand access to entrepreneurial resources, training, and expertise, while creating new pathways to strengthen innovation ecosystems locally and nationally.

From left, Paula Sorrell, Sharon Hamer, and Kevin Hamer. Photo provided

“This partnership with Akiak Technology aligns with Mason Enterprise’s mission to support entrepreneurs and accelerate innovation,” said Paula Sorrell, associate vice president for innovation and economic development at 911��. “By combining Mason Enterprise’s entrepreneur support programs, tech talent, and convening power with Akiak’s technology expertise and mission-driven approach, we are adding more building blocks in the Northern Virginia innovation corridor.”

Under the MOU, Mason Enterprise will provide Akiak Technology with access to its entrepreneur programs, innovation expertise, counseling and training resources, while facilitating collaboration across the university. Together, the organizations will identify mutually beneficial opportunities and explore pathways to support Akiak Technology’s business growth in alignment with its mission, including its commitment to its tribal community.

This synergy can speed up the translation of research and discoveries into products and services, addressing complex challenges in areas such as quantum computing, AI, health care technology, and energy. Akiak Technology will also offer strategic insight and feedback to Mason Enterprise programs and help identify opportunities for revenue-generating activities that further Mason Enterprise’s mission of supporting entrepreneurs and innovation-driven organizations.

“This partnership with Mason Enterprise represents an important step forward for Akiak Technology,” said Sharon Hamer, cofounder of Akiak Technology and enrolled Akiak tribal member. “Collaborating with a leading research university gives us access to world-class talent and new opportunities to expand our AI-driven innovations, while remaining true to our mission and values.”

Akiak’s CEO Kevin Hamer added, “Mason Enterprise helped us launch our first business years ago. Deepening this relationship allows us to tap into 911��’s expansive network and innovation programming as we continue to grow as an AI product and solutions innovator.”

The public–private partnership strengthens technology transfer, supports federal research initiatives, enhances education and workforce readiness, and contributes to regional economic growth—particularly as demand for STEM talent continues to rise.

The collaboration underscores Mason Enterprise’s role as a connector between academia, industry, and community, and highlights Akiak Technology’s commitment to collaboration, innovation, and mission-driven growth.

Learn about Artificial Intelligence at 911��

Topics

National Science Foundation

NSF CAREER award allows researcher to look under AI’s hood

Nathan Kahl — Mon, 20 Jul 2026 16:30:48 +0000

NSF CAREER award allows researcher to look under AI’s hood Nathan Kahl Mon, 07/20/2026 - 12:30

In This Story

Ziyu Yao

Artificial intelligence (AI) systems are now part of everyday life, writing software, tidying up your shopping list, and answering questions. While we know they sit on top of large language models (LLMs), we still understand surprisingly little about what happens inside these systems or what is helping them generate responses.

Ziyu Yao. Photo provided

911�� Department of Computer Science Assistant Professor Ziyu Yao received a prestigious National Science Foundation (NSF) CAREER award for $674,100 over five years to peel the layers back on AI. Specifically, Yao will develop new methods for understanding the internal mechanisms of LLMs. Her research aims to make these AI systems more transparent and useful.

"We use language model–powered products every day, but we have very limited understanding of how they actually work," Yao said. Most efforts to evaluate AI systems focus on their behavior. Researchers give a model a prompt and judge the quality of its response. Yao's work instead focuses on mechanistic interpretability, an emerging field that examines what happens inside a language model as it processes information.

"If we send something to the model, we want to see how it extracts features or signals from my input and gradually performs computations before producing an answer," she said.

As language models have grown dramatically in size, that task is increasingly difficult. Today's leading models contain potentially hundreds of billions of parameters. And Yao said, "The interpretability research community has not caught up. Many traditional approaches cannot be efficiently applied to today's language models."

The project focuses specifically on AI-powered code generation, in which users describe a programming task in natural language and an AI system generates software code. Code generation is an increasingly important application of LLMs, with uses including software engineering and scientific research.

Yao's team will investigate how LLMs apply programming syntax, recall semantic knowledge, and perform deliberate reasoning while writing code. The researchers will also study how training data and learning methods influence those internal mechanisms and explore how interpretability insights can be used to improve AI coding systems.

Current interpretability studies often require researchers to manually design experiments, collecting internal model signals and analyzing the results. Yao plans to develop a framework that automates much of that process using AI itself, allowing researchers to study LLMs more efficiently.

At the same time, she wants interpretability research to produce practical benefits rather than simply satisfying scientific curiosity.

"We want to build the bridge between what we discover about language models and how that knowledge can improve them," Yao said. "Can interpretability actually help us build better AI systems?"

Although commercial AI systems such as ChatGPT are proprietary, Yao and colleagues primarily study open-weight language models that are downloadable. These models are becoming increasingly competitive with commercial systems while supporting the transparency needed for scientific research.

An expectation with the award and a hallmark of the CAREER program is a plan for broader impact. Yao hopes to expand AI literacy through outreach programs, create new curricula and open-source educational resources, provide undergraduate research opportunities, and foster collaborations between researchers in artificial intelligence and software engineering.

"I think we should encourage the younger generation to embrace AI," Yao said. "But at the same time, they should be skeptical, think critically, and be careful about what they are reading from AI."

Topics

large language models

College of Engineering and Computing

Learn more about 911��'s Grand Challenge Initiative

New initiative aims to build AI literacy among educators through year-long professional learning community

Martha Bushong — Tue, 14 Jul 2026 17:55:00 +0000

New initiative aims to build AI literacy among educators through year-long professional learning community Martha Bushong Tue, 07/14/2026 - 13:55

911�� has launched the Ambassadors of Teaching in the Age of AI (ATAA) program, a new professional learning initiative designed to help educators develop the knowledge and skills needed to navigate the growing role of artificial intelligence in education and society.

The program officially debuted June 22–23 at 911��'s Science and Technology Campus during the Virginia Data Science Teacher Professional Learning Institute, which brought together more than 200 K–12 educators, higher education faculty, industry representatives, and government stakeholders. Approximately 50 participants joined ATAA as founding members of the year-long professional learning community.

Chief AI Officer Amarda Shehu at the Ambassadors of Teaching in the Age of AI kick-off event. Photo provided

The launch marks another step in 911��'s broader AI literacy efforts, by bringing together expertise from across the university to create accessible, research-based programming. ATAA is designed to complement existing educator preparation efforts while providing a foundational AI literacy framework that can be applied by professionals across disciplines.

Developed over the past year, ATAA was created through a collaboration among Peng Warweg, research program director in the College of Engineering and Computing (CEC); Padhu Seshaiyer, professor in the College of Science and director of the Center for Mathematics and Science Education Research and Outreach; Manpriya Dua, research postdoctoral fellow in computer science; and Amarda Shehu, 911��'s chief AI officer and professor of computer science.

The team focused on a growing challenge: How to build AI fluency among practitioners, particularly the K–16 teacher workforce. Warweg, who helped lead the program's design and curriculum development, said ATAA is among the first programs to translate newly released federal guidance into a practical learning experience.

"ATAA is grounded in the U.S. Department of Labor's AI Literacy Framework—a guideline that represents a national consensus on what AI fluency should look like across professions," said Warweg. "ATAA is among the first practitioner programs in the country to translate that guidance into a structured, accessible curriculum."

Peng Warweg (on right), research program director in the College of Engineering and Computing (CEC) with Padhu Seshaiyer (left), professor in the College of Science and director of the Center for Mathematics and Science Education Research and Outreach and Manpriya Dua (center), research postdoctoral fellow in computer science. Photo provided.

The launch was supported by the Virginia Data Science Teacher Professional Learning Institute, now in its fifth year and co-organized by Seshaiyer. Through longstanding partnerships with the Virginia Department of Education and decades of leadership in STEM education, Seshaiyer helped establish the foundation that enabled the program to launch at scale.

"Virginia has been building toward this," said Seshaiyer. "ATAA is the natural next step to connect research, policy, and practice around the future of learning."

Seshaiyer and Dua also helped shape the curriculum, ensuring participants receive both practical and technically grounded instruction. Dua's expertise in artificial intelligence and computer science research helped make complex concepts accessible to educators from a wide range of backgrounds.

ATAA also builds on 911��'s broader AI strategy under Shehu's leadership. A key component of the curriculum draws from UNIV 182 AI for All: Understanding and Building Artificial Intelligence (Mason Core) a course developed, by Shehu, to help nontechnical learners gain AI literacy. Research conducted by Shehu has demonstrated significant AI literacy gains among students who complete the course.

"Foundational AI literacy cannot belong only to people who already saw themselves as technical," said Shehu. "What we learned from AI for All is that anyone can develop real fluency when the teaching meets them where they are. ATAA takes that same conviction to the practitioners who multiply it: the educators."

Designed using a train-the-trainer model, ATAA aims to extend its impact beyond the initial cohort. Participants will complete a structured year-long learning experience and earn Continuing Education Units (CEUs). After completing 15 hours of learning, participants will receive a digital badge recognizing their AI literacy achievement, with a full credentialing pathway scheduled to launch in fall 2026.

While educators are the program's first audience, ATAA envisions expanding AI literacy training to professionals across sectors, including healthcare, government, technology, and community organizations. With its strengths in AI research, governance, and education, 911�� hopes to help prepare a broader workforce for an AI-driven future.

In This Story

Amarda Shehu

Topics

Science and Technology Campus

Campus News

GCI-Education

Can AI help make medical records less biased? New study suggests yes—with caveats

Heather Carroll — Fri, 10 Jul 2026 16:50:49 +0000

Can AI help make medical records less biased? New study suggests yes—with caveats Heather Carroll Fri, 07/10/2026 - 12:50

Large language models can identify judgmental language in clinical notes but the settings play a major role in accuracy.

Teenu Xavier PhD, RN. Photo provided

“Addict,” “non-compliant,” “failed treatment,” and “obese person” are examples of stigmatizing language that can appear in medical records. At 911��’s College of Public Health, researchers are exploring whether artificial intelligence (AI) can help identify this kind of language in clinical notes before it impacts patient care.

Nurse scientist Teenu Xavier and colleagues found that large language models (LLMs) show promise in identifying stigmatizing language in clinical documentation, but their performance is highly dependent on their settings. Model size, temperature settings, prompting strategies, and even note type can substantially influence results.

One finding was consistent across every model tested: Providing examples of stigmatizing language improved accuracy.

“Simply selecting an LLM is not enough when used for clinical documentation,” said Xavier, an assistant professor in the School of Nursing. “Careful attention must be paid to settings and prompting before these tools can be reliably used in health care environments.”

Why does this matter?

The use of stigmatizing language in clinical documentation can reinforce bias and affect a patient’s future care. AI tools may be able to help identify this kind of language, promoting more equitable care and improving patient trust and experience.

“Pre-trained models, when optimized for identifying stigmatizing language, could help enable more timely interventions and modifications to the documentation process,” Xavier said. “Our research highlights the need for continued collaboration between health care professionals and AI developers to create tools that improve communication, reduce bias, and improve the overall patient experience.”

What are the detailed study findings?

The largest LLM (trained on large amounts of data) was the best at predicting “stigmatizing” language (94%), but the worst at predicting “not stigmatizing” correctly (47%).
The smallest LLM was the best at predicting “not stigmatizing” correctly (99.7%), but worst at predicting “stigmatizing” correctly (2%).
When researchers gave the LLM an example of stigmatizing language, accuracy improved in all models.
Emergency provider notes were most accurately (69%) categorized as “stigmatizing” or “non-stigmatizing,” and plan of care notes had the lowest accuracy (56%). Misclassifications most commonly arose in long, clinically dense notes where neutral descriptions of complex illness or adverse events were mistaken by the models for judgmental language.
Larger models worked best at lower temperature (how predictable or random the LLMs’s output is when making a classification) and smaller models improved with higher temperature, which means the LLM took more risks in interpretation.

“Detecting stigmatizing language with large language models: mind the settings” was published in JAMIA Open in April 2026. Co-authors include Jane M. Carrington from the University of Florida and Joshua Lambert from the University of Cincinnati.

Thumbnail photo by issaronow from Adobe Stock.

Key Takeaways

A 911�� study found that large language models show promise in identifying stigmatizing language in clinical documentation, but their performance is highly dependent on their settings, such as model size, temperature settings, prompting strategies, and note type.
The ability to detect and correct stigmatizing language early can reduce bias in patient care and lead to improved patient trust and better health outcomes.
Continued collaboration between health care workers and AI developers is needed to create tools that enhance communication, reduce bias, and improve the overall patient experience.

Learn about Artificial Intelligence at 911��

Topics

large language models

Machine Learning in Health Care

Health Care Services

Test out the interactive tool: "The Carbon Footprint of AI in K–12 Education"

911�� researcher develops interactive tool to measure carbon footprint of AI use in K-12 education

Katarina Benson — Fri, 10 Jul 2026 15:53:35 +0000

911�� researcher develops interactive tool to measure carbon footprint of AI use in K-12 education Katarina Benson Fri, 07/10/2026 - 11:53

As artificial intelligence (AI) applications like ChatGPT become more commonplace in K-12 classrooms, school district leaders and policymakers will face the challenge of ensuring these tools are used in an ethical, responsible, and environmentally sustainable way. Seth B. Hunter, associate professor of education leadership and senior fellow in the EdPolicyForward center at 911��, has developed an interactive tool to help.

The tool enables school district policymakers to make better informed decisions on selecting AI software that meets the learning needs of their students and promotes environmental sustainability.

Seth Hunter. Photo by John Boal Photography

“The many conversations I’ve had with leaders across the commonwealth and nation have focused almost exclusively on the potential benefits of AI—benefits that, I should point out, we don’t yet understand well,” said Hunter. “Almost no one is talking about the costs beyond the purchase price. This tool is one way to start filling that gap—by estimating a cost that’s seemingly invisible: the additional energy and emissions that come with putting these tools in students’ hands.”

The tool enables users to input data on the total number of students enrolled in a school district, the number of students expected to use AI (based on surveys from peer-reviewed research), the anticipated number of daily queries they are likely to conduct, and the level of AI processing power needed to handle those queries. It also allows users to select their state from a dropdown menu. Next to each state is a number indicating the amount of carbon dioxide in grams released per kilowatt hour of electricity consumed.

Using these inputs, the tool calculates the increases in kilowatt hours and the carbon emissions generated by AI use. To help users better understand the significance of these estimates, the tool translates the data into every day, real-world equivalents—such as the number of miles driven with a car, the number of days a lightbulb could run continuously, or how many times a smartphone could be charged.

In addition, the tool shows what the trajectory of carbon emissions associated with the use of AI in schools will be through 2031 if no policies are put in place to reduce the associated carbon footprint.

“Even leaders with very little technical understanding of AI have many opportunities to influence its energy consumption, and it starts with a simple conversation with the vendor,” Hunter stated. “I’m not talking about choosing one brand over another, like Gemini versus ChatGPT. I'm talking about the type of model. Within a single company’s family of models, the ones built to ‘reason’ or ‘think’ through problems at length currently consume far more energy than lighter models designed for simpler tasks. The most useful question a procurement office can ask is how much a model relies on that extended ‘reasoning,’ and whether a lighter model would meet students’ needs.”

School district leaders will need to consider several factors, such as the complexity of the task the AI tool will be used to support. For example, if an AI chatbot is used to help students with multi-step math problem-solving, it will use more energy than simpler tasks such as quick factual lookups or summarizing an article. Increases in energy requirements and carbon emissions will also be influenced by the number of students accessing AI and the average number of daily queries they conduct. The type of energy source used in the school district will also have an impact. Renewable energy sources like solar, wind, or hydropower produce less carbon compared to power sources like coal.

“No one wants their students to fall behind in what can feel like an AI arms race,” said Hunter. “But I argue that policymakers and system leaders should ask how much those advantages outweigh the financial, environmental, and social costs. This tool doesn’t make that decision for anyone, but it estimates one of the costs that usually gets left out, so leaders can weigh student learning and environmental sustainability in the same conversation.”

Explore Artificial Intelligence at 911��

Topics

College of Education and Human Development

Katherine Scafide, PhD, RN, FAAN, FAAFS

The AI-powered classroom: tutors, avatars, and new models for learning

Mary Cunningham — Thu, 09 Jul 2026 19:17:45 +0000

The AI-powered classroom: tutors, avatars, and new models for learning Mary Cunningham Thu, 07/09/2026 - 15:17

As health informatics expert Sanja Avramovic introduces new artificial intelligence (AI) tools into her classrooms at 911��, she's also made a point of asking students what they think, both through formal research and casual conversation.

In one early experiment, Avramovic designed AI avatars to present course material in short, on-demand videos students could watch at their own pace. Some students balked a little, finding the digital instructors monotone and robotic. They said they preferred a human.

So Avramovic asked a follow-up question: Should she remove the videos?

AI avatar created by Sanja Avramovic. Image created by AI.

"They said, 'don't you dare,'" she recalled. "'They're excellent supplemental material. Leave them. But we want you to teach.'"

That push and pull has become a central dynamic at the College of Public Health, as faculty respond and adapt to the undeniable rise of AI in higher education. The question is no longer whether AI belongs in the classroom, but how to use it in ways that actively benefit students.

“The goal isn’t AI-free learning,” Avramovic said. “It’s AI-augmented thinking.”

Learning to think with AI

Sanja Avramovic, assistant professor in the Department of Health Administration and Policy

One challenge is persistent: How do you keep students learning when AI can provide answers in seconds?

Research by Avramovic, assistant professor in the Department of Health Administration and Policy Abdul Hafeez, and colleagues found that students can become overly dependent on AI-generated answers, reducing their engagement, interaction, and critical thinking.

Rather than prohibiting AI use, faculty developed and tested AI tutors that function more like coaches than answer generators, walking students through assignments one step at a time. In order to build them, instructors map out common mistakes and teach the systems how to respond in ways that promote deeper learning. “It interviews students,” Hafeez said. “It compels them to think critically.”

Early findings suggest this approach can help counter passive AI use, with students showing stronger performance and engagement than their peers. In one analysis, undergraduates using the tutors improved performance by nearly 9 percentage points. This research was supported by 911��'s Stearns Center for Teaching and Learning Curriculum and Teaching Leadership Funds.

Tempering the trust

Along with the enthusiasm for AI’s potential, faculty are just as focused on teaching students where the technology falls short.

Vanessa Chee, an assistant professor in the Department of Global and Community Health, warns students against relying on it too early in the learning process, likening it to “using roller blades when you’re crawling.”

In one global health course, Chee requires students to develop a 2,500-word narrative and a presentation proposing a public health intervention entirely on their own. Afterward, they use AI to generate a version of the same project, and compare the two.

That exercise is often revealing. Many students walk away disappointed by the quality of the generated work, but with a sharper understanding of what their own thinking contributes, Chee said.

Katherine Scafide, an associate professor in the School of Nursing, introduces AI early in the research process, using it to help students scan the literature and identify possible gaps worth investigating. But she draws a line between assistance and analysis. Students are expected to verify sources, refine their own research questions, and critically test whether AI summaries actually hold up.

"I encourage AI to assist in some structural aspects of writing, like outlining and grammar, but not to author the work," she said. " AI can be a writing tool, but it cannot be an author."

For Avramovic, the biggest concern is that students will trust AI too quickly. She recalled one student who relied heavily on AI while studying, learned flawed logic, and then repeated it on an exam.

“Don’t use AI until you know enough to correct it,” she tells her students.

Rebuilding the classroom

As AI changes how students learn, faculty are also rethinking how they create and deliver their own course content.

With one undergraduate health informatics class, Avramovic and colleagues realized that much of the content felt disconnected from the clinical realities that nursing students—who are required to take the course—would eventually face. So, the instructors used AI tools to rewrite assignments around nursing-specific scenarios, like identifying problems in electronic health records. They also introduced AI-generated videos narrated by digital avatars, guiding students through technical material.

The updates made a difference. Nursing students in the AI-redesigned course showed statistically significant learning gains, more than half said AI-generated videos were more helpful than assigned readings, and nearly four out of five reported that the tools improved their overall learning experience.

Farrokh Alemi, a professor in the Department of Health Administration and Policy, took a similar approach in his graduate statistics course, using AI to help deliver lectures and tutor students.

For students, the change meant more access to their instructor. “Because the AI handled a lot of mechanical tasks, I used the extra time to have more interactions with students, give them career advice, call up at-risk students, just be more available,” he said.

Evolving the toolkit

Faculty at the College of Public Health compare the current moment to earlier technological shifts in education, from the arrival of calculators in math classrooms to the rise of the internet and online learning.

Students still need to learn the fundamentals, faculty said, but once they understand the underlying concepts, AI can help them explore more complex problems and apply knowledge in new ways.

“You still need the human in the loop,” Avramovic said.

The technology continues to evolve quickly. But faculty say the larger goal remains to help students build the skills they’ll need in a world where AI is already becoming part of the job.

Key Takeaways

911�� College of Public Health faculty are using artificial intelligence tools including AI tutors and digital avatars to enhance teaching and student learning.
Instead of prohibiting tools like ChatGPT, instructors are teaching students to think critically, verify AI outputs, and use AI responsibly.
AI is helping redesign courses, personalize learning, and prepare College of Public Health students for an AI-integrated workforce.

In This Story

Sanja Avramovic, PhD

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