By Arturo Vasquez, AIA, NCARB

Across the United States, universities and healthcare institutions are entering a new phase of transformation driven by artificial intelligence. Academic programs are rapidly evolving to incorporate AI, data analytics, and computational science into fields ranging from medicine and life sciences to architecture and engineering. Yet while educational programs are advancing quickly, the physical environments that support��them��including��campuses, laboratories, and clinical��facilities��are only beginning to catch up.��

For architects and planners, this moment presents a fundamental challenge: how to design buildings and campuses that can support technologies and educational models that are still��emerging.��The use of artificial intelligence, advanced analytics, and computational modeling technologies��is��shaping the future of healthcare and research��to rethink how academic health campuses are conceived, planned, and built��for the future.��

A New Generation of Academic Health Environments��

Academic healthcare campuses have always been complex ecosystems where education, research, and clinical care intersect. But artificial intelligence is accelerating the convergence of these disciplines.��Across the country, universities are launching��new programs��focused on AI in medicine, biomedical sciences, and computational research. These programs are reshaping not only what students learn but how institutions organize their campuses. Increasingly, universities are looking to create integrated academic health environments where clinical care, research laboratories, data science, and education coexist in a flexible ecosystem.��

Many of these organizations are recognizing that the traditional separation between academic facilities, research laboratories, and healthcare clinics is no longer��viable.��Instead, they are moving toward hybrid environments where life sciences, healthcare delivery, and computational research converge.����

This convergence is particularly��evident��in healthcare education, where artificial intelligence is becoming deeply embedded in diagnostics, patient analytics, and treatment planning. As a result, the physical infrastructure that supports medical education must evolve as well.��

Designing for an AI-Driven Future��

One of the most significant implications of artificial intelligence for campus design is flexibility.��Traditional laboratory buildings were designed around fixed programmatic uses��like��wet labs, lecture halls, and specialized research spaces. But AI-driven research and digital medicine increasingly rely on computational laboratories, data analysis environments, and collaborative research spaces that evolve rapidly as technology changes.��

To address this, flexible building typologies��can��be developed to��adapt between��different types��of research and learning environments.��By using AI-enabled planning tools and simulation software,��a model��can show��how��spaces might evolve over time. For example,��testing��how a laboratory floor might transition from traditional wet labs to computational research environments, or how teaching spaces could support simulation-based medical training.��These models allow architects to��anticipate��future program shifts before construction even begins.��Rather than designing buildings for a single purpose,��adaptable��frameworks��are designed��that can evolve alongside the technologies and academic programs they support.��

Data-Driven Campus Planning��

Artificial intelligence is also transforming how universities plan entire campuses.��In the past, campus master planning relied heavily on demographic projections and long-term enrollment forecasts. Today, AI-enabled analytics allow planners to analyze vast datasets related to enrollment trends, research funding, healthcare demand, and patient experience.��

Predictive analytics��are integrated��into campus planning to help universities align physical infrastructure with long-term institutional strategy. These models allow us to examine how student populations may grow, how clinical demand may shift, and how new research programs might affect space��utilization.��By connecting these datasets to architectural planning, institutions can make more informed decisions about where to invest in new facilities and how those buildings should function over time.��

A Case Study in Miami��

A clear example of this emerging design approach can be seen at Florida International University’s Herbert Wertheim College of Medicine campus in Miami.��

The��new 120,000-square-foot academic and clinical facility will support the partnership between FIU and Baptist Health South Florida. The building integrates outpatient healthcare services with academic training environments, creating a platform for the next generation of physician education and clinical research.��The $162-million project��represents��more than just a new medical facility. It reflects a broader shift toward AI-enabled academic health environments where data analytics, digital medicine, and medical education��operate��in tandem.����

To support this vision, AI-assisted tools,��including advanced rendering platforms and computational��analytics��are used��to prototype building layouts, test workflow scenarios, and explore how the campus may evolve over time. These tools allow the design team to simulate clinical operations, optimize patient flow, and ensure that academic and healthcare functions can adapt as medical technologies evolve.��

The Architect’s Role in an AI Era��

The rise of artificial intelligence is transforming many industries, and architecture is no exception. But rather than replacing the architect’s role, AI is expanding it.��Architects now have the ability to analyze more information, test more design scenarios, and better understand how buildings will perform long before they are constructed.��This allows designers to become strategic partners in shaping institutional growth rather than simply responding to predefined building programs.��

In academic healthcare, this shift is particularly significant. Universities are competing to attract students and research talent in emerging fields such as AI-driven medicine and computational biology. The campuses that succeed will be those that can rapidly��adapt��their physical environments to support these disciplines.��Architecture therefore becomes part of a larger institutional strategy,��helping universities visualize the future of education, research, and healthcare delivery.��

From Machines Learning to Humans Learning��

Artificial intelligence is often described as machines learning from human data. But in the built environment, the relationship is increasingly reciprocal.��Designers are now learning from machines��by��using computational tools to uncover patterns, analyze data, and explore design possibilities that were previously impossible to see.��

For academic healthcare campuses, this partnership between human creativity and machine intelligence is opening a new frontier.��The next generation of medical campuses will not simply house classrooms and clinics. They will��operate��as dynamic environments where students, physicians, researchers, and data systems interact continuously.��

And as artificial intelligence reshapes how we learn, teach, and deliver healthcare, architecture must evolve with it,��transforming campuses into living systems designed for discovery, innovation, and better patient care.��

Arturo Vasquez, AIA, NCARB, is Design Principal and Senior Architect, Stantec in Miami.

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By Charlie Lange

Earlier this month, leaders from the life sciences and biotechnology fields, along with experts in architecture and engineering, real estate and finance, convened in San Diego for Bisnow’s International Life Sciences & Biotech Conference.

Held Sept. 10–11, the event featured speakers, panel discussions and keynote sessions focused on the relationship between scientific institutions and the development and construction teams and strategies necessary to build research facilities.

The “Universities Driving Innovation and Biotech Growth: Attracting, Retaining and Supporting Top Talent, Developing Incubators for Startups and Driving Growth through Partnerships” discussion brought together life science department heads from different California colleges with architects responsible for designing and building the research facilities on their campuses.

The conversation covered how research institutions can work in tandem with A/E firms in securing funding, conceptualizing multi-purpose lab and research spaces, and giving students the resources they need to succeed in the field after graduation.

Moderated by Julie Kilpatrick, Senior Managing Director, Southwest Region, for project management consulting firm Turner & Townsend Heery, the panel included:

• Carmen Domingo, Dean of the College of Science and Engineering, San Francisco State University
• Robert Fagnant, Associate Partner, Syska Hennessy Group
• Tracy Johnson, Dean of Life Sciences, University of California, Los Angeles
• Vlad Pajkic, Partner, ZGF Architects
• Corrine Peek-Asa, Vice Chancellor for Research & Innovation, University of California San Diego
• Jeffrey Roberts, Dean of the College of Sciences, San Diego State University

Building for Flexibility

One common topic was the shift from building department-specific facilities to flexible, shared spaces that can host a variety of disciplines and purposes.

“Everybody always asks for flexibility and modularity,” said Pajik, citing recent projects at Johns Hopkins University and UC Davis, where primary spaces were designed as “core labs” with a variety of equipment and purposes in one place. He said these spaces offer room for collaboration between the sciences and are less expensive than building separate facilities.

Peek-Asa added that such facilities allow for institutions to “solve multiple problems at once.”

“It takes the cardiologist sitting next to the engineering student to understand how we can integrate [solutions],” she said.

“We need to have the flexibility to organize people not around what their Ph.D.s are, but around shared problems of interest,” added Roberts. “When you do that, you can make more efficient and more impactful use of space.”

And that flexibility makes for a better investment, as developments in AI and other new technologies will factor into future needs and functions.

“Listening to researchers, they’re young and fired up on using AI,” said Fagnant. “We’re going to have to parlay our data center experience into some of these facilities. AI integration is going to take a lot of interconnectability between building on campus and the outside world.”

New Ways to Find Funding

Exploring the unknown is foundational to scientific research, but with expansive cuts to federal education funding over the past year, universities have been forced into uncharted territory in securing the money to build or renovate facilities to meet ever-evolving needs.

Johnson brought up how challenges with federal funding have led UCLA to seek investment from private sources, including the companies that will eventually be employing the university’s graduates.

Domingo echoed this sentiment. “One of the important things is for industry to realize that the university systems around them will help create the environment for them to be successful. We’re the backbone of the workforce. Investing in a university like ours and in infrastructure that allows us to train students in the types of skillsets they need is important.”

Roberts added that universities could even find positive new opportunities in the current funding environment.

“It’s going force our faculty to think more creatively and broadly about who they need to reach out to, who they need to work with, and what kinds of problems they need to work on,” he said. “Current challenges from a funding perspective will have some positive impact in terms of building deeper, more meaningful and authentic collaborations with industry.”

Setting Students Up for Success

The panelists also stressed the importance of not only building facilities for learning and research, but also for preparing students to apply what they learn in the market after leaving campus.

“One of the keys in our new building was the ability to teach science differently, with studio-style instruction,” Domingo said. “Instead of going to lecture, then going to the lab afterwards, the space allows us to integrate lab and lecture together, so they’re putting into practice what they’re learning.”

Meanwhile, Johnson spoke about UCLA’s incubator programs, which allow students to work directly with startups.

“We are thinking about what it means to build a culture where our students can see the bridge between what they do in the university, in their classrooms and their labs, to ultimately taking their ideas into industry,” said Johnson.

On the topic of real estate, Peek-Asa added how UCSD’s status as the largest residential campus in the nation has kept students closer to their classrooms and has had positive effects in preserving housing in the local market.

“It’s important, because we’re trying to move our students onto campus so they’re not competing in our real estate market for affordable housing,” she said.

Ultimately, the conversation came back to the importance of public-private partnerships in helping universities develop industry leaders of tomorrow.

“Students are local, so if you invest in your local students, they don’t have to move into the area to be part of your workforce — they’re already there,” said Domingo.

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