Featured Post from University of Florida

The AI Journal: UF and other research universities will fuel AI. Here’s why

Alina Zare Ph.D.

Feb 2, 2026

3 min

In the global AI race between small and major competitors, established companies versus new players, and ubiquitous versus niche uses, the next giant leap isn’t about faster chips or improved algorithms. Where AI agents have already vacuumed up so much of the information on the internet, the next great uncertainty is where they’ll find the next trove of big data.

The answer is not in Silicon Valley. It’s all across the nation at our major research universities, which are key to maintaining global competitiveness against China.

To teach an AI system to “think” requires it to draw on massive amounts of data to build models. At a recent conference, Ilya Sutskever, the former chief scientist at OpenAI — the creator of ChatGPT — called data the “fossil fuel of AI.” Just as we will use up fossil fuels because they are not renewable, he said we are running out of new data to mine to keep fueling the gains in AI.

However, so much of this thinking assumes AI was created by private Silicon Valley start-ups and the like. AI’s history is actually deeply rooted in U.S. universities dating back to the 1940s, when early research laid the groundwork for the algorithms and tools used today. While the computing power to use those tools was created only recently, the foundation was laid after World War II, not in the private sector but at our universities.

Contrary to a “fossil fuel problem,” I believe AI has its own renewable fuel source: the data and expertise generated from our comprehensive public academic institutions. In fact, at the major AI conferences driving the field, most papers come from academic institutions. Our AI systems learn about our world only from the data we offer them.

Current AI models like ChatGPT are scraping information from some academic journal articles in open-access repositories, but there are enormous troves of untapped academic data that could be used to make all these models more meaningful. A way past data scarcity is to develop new AI methods that leverage all of our knowledge in all of its forms. Our research institutions have the varied expertise in all aspects of our society to do this.

Here’s just one example: We are creating the next generation of “digital twin” technology. Digital twins are virtual recreations of places or systems in our world. Using AI, we can develop digital twins that gather all of our data and knowledge about a system — whether a city, a community or even a person — in one place and allow users to ask “what if” questions.

The University of Florida, for example, is building a digital twin for the city of Jacksonville, which contains the profile of each building, elevation data throughout the city and even septic tank locations. The twin also embeds detailed state-of-the-art waterflow models. In that virtual world, we can test all sorts of ideas for improving Jacksonville’s hurricane evacuation planning and water quality before implementing them in the actual city.

As we continue to layer more data into the twin — real-time traffic information, scans of road conditions and more — our ability to deploy city resources will be more informed and driven by real-time actionable data and modeling. Using an AI system backed by this digital twin, city leaders could ask, “How would a new road in downtown Jacksonville impact evacuation times? How would the added road modify water runoff?” and so on.

The possibilities for this emerging area of AI are endless. We could create digital twins of humans to layer human biology knowledge with personalized medical histories and imaging scans to understand how individuals may respond to particular treatments.

Universities are also acquiring increasingly powerful supercomputers that are supercharging their innovations, such as the University of Florida’s HiPerGator, recently acquired from NVIDIA, which is being used for problems across all disciplines. Oregon State University and the University of Missouri, for example, are using their own access to supercomputers to advance marine science discoveries and improve elder care.

In short, to see the next big leap in AI, don’t immediately look to Silicon Valley. Start scanning the horizon for those research universities that have the computing horsepower and the unique ability to continually renew the data and knowledge that will supercharge the next big thing in AI.

Alina Zare

Professor

Alina Zare's research focuses on developing new machine learning and artificial intelligence algorithms to process data and imagery.

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Apr 12, 2026

3 min

Study finds most cancer patients exposed to misinformation; UF researchers pilot 'information prescription'

Ninety-three percent of patients with a new cancer diagnosis were exposed to at least one type of misinformation about cancer treatments, a UF Health Cancer Center study has found. Most patients encountered the misinformation — defined as unproven or disproven cancer treatments and myths or misconceptions — even when they weren’t looking for it. The findings have major implications for cancer treatment decision-making. Specifically, doctors should assume the patient has seen or heard misinformation. “Clinicians should assume when their patients are coming to them for a treatment discussion that they have been exposed to different types of information about cancer treatment, whether or not they went online and looked it up themselves,” said senior author Carma Bylund, Ph.D., a professor and associate chair of education in the UF Department of Health Outcomes and Biomedical Informatics. “One way or another, people are being exposed to a lot of misinformation.” Working with oncologists, Bylund and study first author Naomi Parker, Ph.D., an assistant scientist in the UF Department of Health Outcomes and Biomedical Informatics, are piloting an “information prescription” to steer patients to sources of evidence-based information like the American Cancer Society. The study paves the way for other similar strategies. Most notably, the study found the most common way patients were exposed to misinformation was second hand. “Your algorithms pick up on your diagnosis, your friends and family pick up on it, and then you’re on Facebook and you become exposed to this media,” Parker said. “You’re not necessarily seeking out if vitamin C may be a cure for cancer, but you start being fed that content.” And no, vitamin C does not cure cancer. Health misinformation can prevent people from getting treatment that has evidence behind it, negatively affect relationships between patients and physicians, and increase the risk of death, research has shown. People with cancer are particularly vulnerable to misinformation because of the anxiety and fear that comes with a serious diagnosis, not to mention the overwhelming amount of new information they have to suddenly absorb. While past research has studied misinformation by going directly to the source — for instance, studying what percentage of content on a platform like TikTok is nonsense — little research has looked at its prevalence or how it affects people. The team first developed a way to identify the percentage of cancer patients exposed to misinformation. UF researchers collaborated with Skyler Johnson, M.D., at Huntsman Cancer Institute, an internationally known researcher in the field. The survey questions were based on five categories of unproven or disproven cancer treatments — vitamins and minerals, herbs and supplements, special diets, mind-body interventions and miscellaneous treatments — and treatment misconceptions. The myths and misconceptions were adapted from National Cancer Institute materials and included statements like “Will eating sugar make my cancer worse?” The team surveyed 110 UF Health patients diagnosed with prostate, breast, colorectal or lung cancer within the past six months, a time when patients typically make initial treatment decisions. Most had heard of a potential cancer treatment beyond the standard of care, and most reported they had heard of at least one myth or misconception. The most common sources were close friends or family and websites, distant friends/associates or relatives, social media and news media. The findings mark a shift in misinformation research, with major implications for the doctor-patient relationship, said Bylund, a member of the Cancer Control and Population Sciences research program at the UF Health Cancer Center. “I still think media and the internet are the source and why misinformation can spread so rapidly, but it might come to a cancer patient interpersonally, from family or friends,” she said. Most patients rarely discussed the potential cancer treatments they had heard about with an oncologist, the study also found. Next, the researchers plan to survey a wider pool of patients, then study the outcomes of interventions designed to decrease misinformation exposure, like the information prescription.

Apr 9, 2026

2 min

New AI tool matches students with high-impact internships

Finding the right internship can be an important step for students, but it’s not always clear which opportunities will lead to the strongest growth. To help solve that problem, University of Florida researchers have developed an AI-powered tool that helps students identify internships most likely to accelerate their technical and professional development. Unlike traditional recommendation engines, Pro-CaRE not only predicts which opportunities will lead to stronger outcomes, it also explains why each suggestion is a good fit. In testing data collected from the students, Pro-CaRE’s predictions proved highly accurate, accounting for more than 72% of the differences in learning gains among participants. While the pilot is being tested in engineering, the tool could be adopted for other disciplines. “Internships are one of the most critical parts of an engineering education, but students often struggle to know which experiences will actually help them grow,” said Jinnie Shin, assistant professor of research and evaluation methodology in the UF College of Education. “What makes Pro-CaRE unique is that it doesn’t just offer a list of options. It provides personalized recommendations backed by data and it tells students clearly why an opportunity is a good match for them.” Pro-CaRE creates matches by analyzing each student’s coursework, major, background and self-reported interest, confidence and self-efficacy in engineering skills. It then compares that profile with a carefully chosen set of similar peers to refine suggestions. The result is more precise guidance that adapts to students at different stages of their degree programs. “Students shouldn’t have to guess or hope that an internship will be worthwhile,” Shin said. “With Pro-CaRE, they can approach opportunities knowing they’re backed by evidence, whether the role is onsite, hybrid or remote and whether it’s at a startup or a Fortune 500 company.” The system is designed to work across a wide range of companies and contexts, giving students flexibility while ensuring their choices align with their personal and professional goals. Each recommendation comes with a clear “why this?” explanation, so students can make confident decisions and discuss options more effectively with advisors. Pro-CaRE was developed by a cross-disciplinary UF team combining expertise in education and engineering. Alongside Shin, the project’s co-principal investigators include Kent Crippen in the College of Education and Bruce Carroll in the Herbert Wertheim College of Engineering. The team is exploring external funding opportunities to expand the usage and test the efficacy on a larger scale. “Ultimately, our goal is to empower students to invest their time in experiences that will have the greatest impact,” Shin said. “Pro-CaRE bridges the gap between what students hope to gain and what internships can truly deliver.”

Apr 7, 2026

2 min

Using AI tools empowers and burdens users in online Q&A communities

Whether you’ve searched for cooking tips on Reddit, troubleshooted tech problems on community forums or asked questions on platforms like Quora, you’ve benefited from online help communities. These digital spaces rely on people across the world to contribute their knowledge for free, and have become an essential tool for solving problems and learning new skills. New research reveals that generative artificial intelligence tools like ChatGPT are creating a double-edge effect on users in these communities, simultaneously making them more helpful while potentially overwhelming them to the point of decreasing their responses. “On the positive side, AI helps users learn to write more organized and readable answers, leading to a noticeable increase in the number of responses,” explained Liangfei Qiu, Ph.D., study coauthor and PricewaterhouseCoopers Professor at the University of Florida Warrington College of Business. “However, when users rely too heavily on AI, the mental effort required to process and refine AI outputs can actually reduce participation. In other words, AI both empowers and burdens contributors: it enables more engagement and better readability, but too much reliance can slow people down.” The study examined Stack Overflow, one of the world’s largest question-and-answer coding platforms for computer programmers, to investigate the impact of generative AI on both the quality and quantity of user contributions. Qiu and his coauthor Guohou Shan of Northeastern University’s D’Amore-McKim School of Business measured the impact of AI on users’ number of answers generated per day, answer length and readability. Specifically, they found that users who used AI tools to generate their responses contributed almost 17% more answers per day compared to those who didn’t use AI. The answers generated with AI were both shorter by about 23% and easier to read. However, when people relied too heavily on AI tools, their participation decreased. Qiu and Shan noted that the additional cognitive burden associated with heavier AI usage negatively affected the impact on a user’s answer quality. For online help communities grappling with AI policies, this research provides valuable insight into how these policies can be updated in the current AI environment. While some communities, like Stack Overflow, have banned AI tools, this research suggests that a more nuanced approach could be a better solution. Instead of banning AI entirely, the researchers suggest striking a balance between allowing AI usage while promoting responsible and moderated use. This approach, they argue, would enable users to benefit from efficiency and learning opportunities, while not compromising quality content and user cognition. “For platform leaders, the takeaway is clear: AI can boost participation if thoughtfully integrated, but its cognitive demands must be managed to sustain long-term user contributions,” Qiu said.