Most health science students are already using artificial intelligence tools in their studies, but a new survey suggests their institutions have largely failed to keep pace. The findings paint a picture of rapid, largely self-directed adoption of AI alongside persistent gaps in formal instruction and widespread ethical unease.
The scale of everyday use stands out. Ninety percent of participants, 279 students, reported using ChatGPT, one of the most widely known generative AI chatbots. Yet formal education on these tools has not followed: 82.6 percent of respondents said they had received no instruction on AI at all. Students described only moderate familiarity with the technology, with a mean knowledge score of 3.08 on the study’s scale, with a standard deviation of 1.06. That combination, near-universal use paired with minimal structured teaching, suggests students are learning to work with AI largely on their own.
The study also examined how AI use related to academic performance. Students who used AI tools more frequently reported higher grade point averages, and the association was statistically significant in an analysis of variance, with F = 15.56. The authors emphasize, however, that the cross-sectional design of the study cannot establish that AI use causes better academic outcomes. It is equally possible that stronger students gravitate toward these tools, or that other factors influence both usage and grades. Self-reported measures add a further layer of caution, since students’ own accounts of their study efficiency and comprehension may not align with objective performance.
Despite those caveats, the self-reported academic picture was broadly positive. Participants linked AI tools to greater study efficiency and better comprehension of their coursework. For students juggling dense curricula across medicine, nursing, and allied health disciplines, the appeal of tools that can summarize complex material or help draft study notes is easy to understand. The survey results indicate that this appeal translates into heavy real-world use, even in the absence of institutional guidance on how to use such tools well.
Ethical concerns emerged as a major theme. Sixty-one percent of participants reported ethical worries about AI in their education. Those concerns were not evenly distributed: they varied significantly by gender, with p = 0.0015, and by academic program, with p = 0.0114. The variation across programs suggests that students in different health disciplines encounter AI differently, whether through differing coursework, clinical exposure, or expectations about professional practice, and that their ethical reservations reflect those distinct experiences.
The authors argue that the combination of high use, low formal training, and prevalent ethical concern points to a need for structured curriculum change. Their recommendation for practice is that faculty implement what they call proactive AI training, designed in advance of problems rather than in reaction to them. Such training, they suggest, should address automation bias, the tendency to over-trust outputs from automated systems, and build ethical literacy so that students can critically evaluate AI-generated content rather than accept it uncritically.
The stakes extend beyond individual study habits. Health professions graduates will enter fields where AI tools are increasingly present in clinical and research settings, and equitable competence across disciplines matters for patient care. The finding that ethical concern differs significantly by program hints that some student groups may be better prepared than others to navigate these tools, a disparity the authors link to the need to ensure equitable technological competency across healthcare disciplines.
It documents associations, such as the link between usage frequency and GPA, and patterns, such as the gender and program differences in ethical concern, but it cannot determine direction of causation or long-term effects. The authors state this limitation explicitly in their conclusion, noting that the cross-sectional design does not establish causality between AI use and academic achievement.
What the survey does establish is a clear baseline. AI tools are already woven into the daily study routines of the vast majority of these health science students, formal instruction has not kept up, and a majority carry ethical reservations that vary across the student body. The authors’ conclusion is that these disparities in formal training and significant ethical issues underscore the need for a structured curriculum, one that treats AI literacy as a teachable competency rather than leaving students to improvise. As generative AI continues to spread through education and healthcare alike, studies like this one offer institutions a starting point for deciding what, and how, to teach about it.
Subject of Research: AI use, self-reported academic effects, and ethical concerns among health science students
Article Title: Bridging technology and pedagogy: a cross-sectional analysis of AI use, self-reported academic effects, and ethical concerns among students across diverse health science curricula
Article References: Alshahrani, A. M., Ibrahim, I. A. E., Jibo, A., Alshahrani, A. F. S., Alshahrani, A. F. A., Alshahrani, K. H. S., Alqarni, G. A. M., Alghamdi, M. A. S., Almarhabi, A. M., Almuawi, M. T. M., Alharthi, S. H., & Albishi, D. M. K. (2026). Bridging technology and pedagogy: a cross-sectional analysis of AI use, self-reported academic effects, and ethical concerns among students across diverse health science curricula. BMC Medical Education. https://doi.org/10.1186/s12909-026-10645-5
Image Credits: AI Generated
DOI: 10.1186/s12909-026-10645-5
Keywords: artificial intelligence, health professions education, ChatGPT, GPA, ethical concerns, cross-sectional study, medical education, survey, curriculum, training gap, Bridging, technology
Ultraprocessed foods have a terrible reputation.
Studies have linked them with multiple chronic diseases. Journalists and health influencers characterize them as poison. Some public health experts call for a global halt to producing and consuming them.
But the debate around ultraprocessed foods is not nearly as settled as it may seem. As a researcher who studies social movements for healthy and sustainable food, I have been documenting the history of processed foods and examining debates around the science and politics of ultraprocessing.
In basic terms, ultraprocessed foods are mass-produced, industrial formulations that use ingredients not typically found in a home kitchen, and that generally contain little to no intact whole foods.
Yet the very definition of ultraprocessed is highly contested. While a growing number of advocates say that ultraprocessing is dangerous to public health, skeptics counter that the fervor around the topic has become a moral panic, and insist that some ultraprocessed foods can be part of a healthy diet.
People have been processing food – that is, transforming crops and animals into food ingredients and products – for all of human history. Cooking, which historians generally consider the first form of food processing, goes back as far as 2 million years. In the millennia and centuries that followed, people developed and refined other traditional food processing methods, including grinding, baking, fermenting, drying and pressing.
New types of food processing were created during the industrial age, beginning in the mid-18th century. Canning, pasteurization and refrigeration were among the major innovations that reshaped not only how people eat but society as a whole.
Before that time, nearly all food was local food – that is, produced within a few dozen miles – and people consumed fresh foods quickly to avoid spoilage. Food processing promised to improve food safety, shelf life, quality, convenience, taste and healthfulness. The world became more urbanized as regional and international supply chains developed. Geographically dispersed farmers were connected to retailers and consumers across the newly modern food system.
From the mid-20th into the 21st century, food manufacturers added a host of complex technological innovations to the food processing playbook. Their efforts made processed foods more standardized for the mass market and more convenient for consumers. But public health experts warned that processed junk foods were packed with salt, sugar and saturated fat – problematic nutrients linked to obesity, diabetes and other diet-related diseases.
By the mid-2000s, some healthy food advocates began to argue that the nature and extent of food processing, rather than the presence of specific problematic nutrients, explained diet-related chronic disease. The journalist Michael Pollan famously implored his audience to avoid “edible food-like substances.” Instead, he advised: “Eat food. Not too much. Mostly plants.”
Brazilian epidemiologist Carlos Monteiro coined the term “ultraprocessing” in 2009 as part of his work on what he and colleagues called the NOVA classification. The NOVA framework establishes a four-category system that characterizes a food based on its level of processing.
NOVA Category 1 includes unprocessed or minimally processed plant or animal foods that are close to their natural state and contain no additives. Category 2 refers to processed culinary ingredients – that is, products derived from Category 1 via pressing, grinding or similar techniques. Category 3 describes processed foods made by combining Category 1 and Category 2 using traditional methods, such as baking, boiling or canning.
NOVA Category 4 foods are ultraprocessed foods, often described as industrially manufactured food products that can’t be made at home. Examples of ultraprocessing include separating whole foods into substances, chemically formulating foods or using cosmetic additives such as food dyes.
According to the framework, corporations design ultraprocessed foods to be convenient, hyperpalatable and highly profitable. They are packaged attractively, marketed intensively and intended to displace other food groups.
Today, ultraprocessed foods make up an increasingly large share of global diets. They account for more than 50% of total calories in the United States, with many low- and-middle income nations not far behind.
Since the NOVA framework emerged, researchers have tried to test whether food processing can explain how certain foods affect people’s health.
The vast majority of this work has explored correlations between consuming ultraprocessed foods and developing health problems. One consistent result stands out: People who consume more ultraprocessed foods are at higher risk for multiple chronic diseases and early death.
However, even that conclusion is more complex than it appears. It turns out that certain categories of ultraprocessed foods – such as sweetened beverages like sodas and processed meats like deli meats – are consistently associated with negative health outcomes including heart disease and stroke. But other ultraprocessed products – breads, cold cereals, yogurt and even savory snacks – have actually been correlated with lower rates of these health problems.
Still other ultraprocessed foods – including sauces, condiments and ready-to-heat dishes like frozen dinners – show no consistent links to people’s health one way or the other.
Only a handful of clinical trials that can actually show cause and effect have explored ultraprocessed foods’ effects on health. One prominent study did find that participants who ate a diet of ultraprocessed foods for two weeks consumed more calories and gained more weight than participants who followed a minimally processed diet, but other experiments have found no clear effect. What’s more, these studies generally last just a few weeks – far too short a time frame to draw conclusions about long-term health impacts.
Some food science researchers are unsurprised by these mixed findings, arguing that the definition of “ultraprocessed” is ambiguous and frequently subjective. For example, a cake prepared by a home baker would be considered a NOVA Category 3 “processed food.” But if that same cake were industrially manufactured using a commercial preservative, then packaged and sold at a grocery store, it would become a NOVA Category 4 “ultraprocessed food” – even if the two cakes were nutritionally identical.
Fundamentally, many nutrition scientists remain unconvinced that processing alone can explain why certain foods are associated with poor health. Instead, they emphasize that unhealthy ultraprocessed foods frequently have poor nutritional profiles and other qualities that make them easy to overeat. And they argue that making ultraprocessed foods healthier, not getting rid of them entirely, can improve public health.
Vilifying ultraprocessed foods may have harms, they suggest, like creating unnecessary fear and promoting disordered eating. It can also stigmatize eco-friendly, plant-based meats or ultraprocessed products such as baby formula that are designed for clinical nutrition.
The wholesale rejection of ultraprocessed foods could also make life more difficult for families who rely on the convenience and affordability the products offer.
On the other hand, many food policy and public health researchers maintain that the common definition of ultraprocessed is clear enough, especially if exceptions are made for healthy ultraprocessed foods. They also note that many supporters of ultraprocessed foods have ties to the food industry. They insist that the existing evidence is strong enough to take immediate policy action, including making changes to how ultraprocessed foods are produced, purchased and marketed.
Regulating ultraprocessed foods wouldn’t just benefit public health, advocates argue. It could also help rein in harms to food culture, business and the environment.
In this sense, ultraprocessed foods are a symptom of a bigger problem: an unsustainable industrial food system that is dominated by a handful of corporations. That argument, of course, is plenty contested too.
Ultimately, researchers and advocates from all sides of the debate mostly agree that the modern food environment needs to be made healthier. The key point of disagreement, however, is whether policymaking and dietary guidance should directly address ultraprocessing or stick to a more traditional focus on nutrients instead.
Garrett M. Broad, Professor of Communication Studies, Rowan University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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