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AI is more likely to change how health education space is used than to guarantee smaller buildings. In a May 2026 planning perspective, Terin Barrios, SmithGroup principal and health education design strategist, frames the potential effects as compression, expansion and transformation: some traditional functions may need less space, simulation and immersive learning may need more support, and familiar spaces such as libraries and assessment centers may take on new roles. The eventual footprint is uncertain; the central design challenge is planning for change without sacrificing access, adaptability or human connection.
For owners, campus planners and design teams, the practical question is not simply whether AI will shrink a health education building. It is whether the facility program—the mix of rooms, support spaces, infrastructure and shared areas—will keep pace as teaching and assessment change. Barrios’s article offers a design outlook, not a quantified forecast or empirical study. It does not establish that a particular school will need less area, save money or eliminate a facility type.
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That distinction matters. A school might reduce reliance on a traditional lecture room while needing a more involved simulation environment, or retain similar total square footage but assign it to different activities. Planning around a single predicted footprint risks optimizing for a future that may not arrive. A more resilient approach starts with the functions students and faculty need, then provides flexible space and infrastructure to support their evolution.
What does AI change in a health education facility?
In Barrios’s framework, AI-related effects fall into three categories: compression, expansion and transformation. They describe possible changes in the use and support needs of space, not a guaranteed reduction or increase in a building’s total area.
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- Compression: Some lecture-centered instruction may give way to flexible and team-based learning as more teaching emphasizes application. The implication is that some large, fixed-format rooms could be used differently or become less central—not that every health program can remove its lecture spaces.
- Expansion: Simulation and immersive learning can call for a broader set of spaces and technical support: practice rooms, preparation and storage areas, cameras, debriefing spaces, charging and network capacity.
- Transformation: Assessment centers and libraries may retain their educational purpose while changing their layouts and services. Assessment could involve recording, secure data handling and private review; libraries could place greater emphasis on AI literacy, data science and research support.
The three categories can happen at once. A campus could repurpose some lecture space, add support functions around simulation, and reorganize its library without changing its overall area dramatically. The design question is therefore how the pieces fit together—not whether “AI” automatically means a smaller or larger facility.
Will AI mean smaller health education buildings?
There is no reliable basis in the cited article for answering yes. Barrios describes smaller facility footprints as a concern, but says the eventual footprint remains uncertain; similar square footage could serve very different purposes.
Compression of one activity does not equal an equivalent reduction in total building area. A change in instruction might reduce demand for some large lecture-centered settings, while simulation requires space for preparation, equipment, practice and debriefing. A transformed assessment function may need privacy and secure data handling. The net result depends on the institution’s teaching model and the space it chooses to support.
For construction and renovation decisions, this means area targets should follow a program analysis rather than an assumption that AI will reduce demand. Identify what the facility must enable, which current functions may change, and what supporting activities those changes create. Keep the uncertainty explicit when evaluating options: the source provides no measured savings, square-footage target, cost estimate or percentage reduction.
What should simulation and immersive learning spaces include?
AI-enhanced manikins and VR or AR are different tools, but Barrios describes institutions using physical and immersive approaches within hybrid simulation ecosystems. A facility plan should consider the support each activity needs rather than treating a headset or manikin as a self-contained installation.
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For physical simulation, planning should account for practice rooms as well as the surrounding workflow: preparing a scenario, storing equipment, recording activity where appropriate, and providing a place for learners and faculty to debrief. Camera and display needs can also affect room layout and infrastructure. The cited article identifies these functions but does not prescribe equipment specifications, quantities, room dimensions or compatibility requirements.
VR and AR introduce another set of considerations. Headsets need designated storage and charging, and immersive learning depends on adequate network infrastructure. A program that combines immersive technology with physical simulation may also need flexible ways to move between the two modes. The point is not to assume that every institution will adopt the same equipment; it is to avoid designing a room around the device alone and overlooking preparation, support and follow-up.
Before committing to a fixed layout, teams can map a representative learning activity from setup through practice and debrief. Ask where equipment is prepared and stored, how learners move through the activity, what needs to be recorded, and where reflection happens afterward. These are planning questions, not a substitute for institution-specific simulation requirements or technical coordination.
How might assessment centers and libraries change?
Assessment and library spaces may retain their core educational roles while gaining new functions. Barrios’s outlook points to assessment environments that accommodate recording, secure data handling, private review and hybrid human/AI evaluation, and libraries that offer more support for AI literacy, data science and research.
For assessment facilities, the design implication is to consider the complete review process—not just the room where an assessment occurs. Privacy for review, secure handling of data and recording needs may affect adjacencies, room use and supporting infrastructure. The article does not specify a particular evaluation system or establish how much space these functions require, so teams should verify their own program and operational requirements.
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For libraries, the shift is not necessarily from physical to digital or from study to technology. The outlook is for a greater emphasis on research and emerging literacies, which can coexist with quiet work, collaboration and access to support. Barrios cautions against removing study and social space in pursuit of an “efficient” AI-optimized facility: those settings can still matter to learning and connection even as services evolve.
How can a facility stay adaptable as technology changes?
Barrios recommends designing for continuous evolution rather than optimizing solely for today’s AI capabilities. That means treating flexibility as a combination of room layout, furniture, power, data capacity, equipment support and access—not as a label attached to a multipurpose room.
In practical terms, planning teams can test a proposed program against several plausible uses. Can furnishings be reconfigured for team-based learning? Is there adequate power and connectivity for the planned activities? Is storage and charging accounted for? Can a room support a change in equipment or teaching format without a wholesale redesign? The source does not supply technical standards or a prescribed amount of spare capacity; those decisions require project-specific requirements and coordination.
Flexibility also has limits. A room cannot serve every activity equally well if privacy, secure data handling, specialized equipment or distinct patterns of use are important. The aim is to distinguish between functions that benefit from adaptable space and functions that require dedicated conditions, then avoid locking adaptable areas into one short-lived arrangement.
Barrios states the principle directly: “We must resist optimizing space for today’s AI capabilities and instead design for continuous evolution.” The planning consequence is to prioritize durable capacity and reconfigurable use while remaining specific about the institution’s current needs.
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How should equity and human connection shape the plan?
Technology-rich space is not equitable simply because it is available on campus. Barrios includes reliable connectivity, device lending, digital literacy support and community access among the considerations for digital equity. A facility plan should ask who can use its resources, what support is available, and whether access extends beyond students who already have devices and connectivity.
That evaluation may reach beyond room layouts. Device lending and digital literacy support are services, while reliable connectivity and community access have operational implications. The design and planning team should coordinate with the institution on how those commitments will work in practice rather than treating them as an equipment checklist. The cited article identifies these equity measures but gives no utilization figures, costs or implementation model.
Human connection is another planning requirement, not leftover space after technology has been accommodated. Informal gathering, collaboration and faculty-student interaction can support the learning environment alongside simulation, assessment and research functions. Barrios warns that eliminating “excess” study and social space may undermine the effectiveness of the learning environment. A space plan should therefore make visible both its technical capacity and its places for people to meet and learn together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should owners ask before approving a project?
A disciplined planning discussion can turn the broad AI question into decisions an owner and design team can assess. The following prompts reflect Barrios’s stated planning priorities; they are not quantitative design standards.
- Which learning activities are changing? Identify where instruction is shifting toward application, teamwork, simulation or immersive learning, and which existing spaces those activities affect.
- What support does each activity require? Include preparation, storage, charging, cameras, debriefing, privacy, data handling and network capacity where relevant—not only the primary teaching room.
- What can be reconfigured later? Review furniture, room arrangements and technical capacity for the possibility of new equipment or teaching formats. Separate flexible functions from those with distinct requirements.
- How are access and support provided? Consider connectivity, device lending, digital literacy and community access alongside the technology itself.
- Which human spaces remain protected? Account for informal study, social interaction, collaboration and faculty-student contact as deliberate parts of the learning environment.
- What is known, and what remains uncertain? Distinguish current program needs from projections. The cited perspective gives no project-specific area, cost or outcome forecasts, so avoid presenting such estimates as established effects of AI.
SmithGroup’s health education practice describes work spanning medical, nursing, dental and allied health education, including simulation, team-based classrooms, informal learning and community outreach. That range helps place the planning discussion in context: AI-related decisions sit within a wider educational program, not in isolation from teaching, research and access needs.
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Ultimately, an AI-era health education facility is not defined by how many AI tools it houses. Its value depends on whether its spaces and infrastructure can support changing forms of instruction and assessment while preserving adaptability, equitable access and human-centered learning. The available evidence supports that as a planning outlook—not as a universal prediction about building size.
Frequently Asked Questions
Is the AI-era facility argument based on measured construction data?
No. The cited discussion is Terin Barrios’s design perspective, not a peer-reviewed study or a collection of measured project outcomes. It reports no quantified forecasts, costs or comparative performance results.
Does the outlook apply only to medical schools?
The perspective concerns health education facilities broadly. SmithGroup describes work in medical, nursing, dental and allied health education, so the relevant space mix will depend on the institution’s specific programs.
Should a school replace its physical simulation spaces with VR?
The cited perspective describes physical simulation and VR/AR as parts of hybrid simulation ecosystems, not mutually exclusive choices. It does not provide evidence for a universal replacement decision; schools need to plan around their own learning activities and support requirements.
Does digital equity mean providing every learner with a device?
The article identifies device lending as one possible part of a wider approach that also includes reliable connectivity, digital literacy support and community access. It does not prescribe a universal device-distribution policy.
