Skip to sources
Time Machine

Health Specialties Teachers, Postsecondary

Scrub through 271years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

2026drag to travel through time
1775180018251850187519001925195019752000now
2026
Known today as Health Specialties Teachers, Postsecondary (BLS SOC 25-1071)
Latest actual · 2024
290K
BLS OEWS May 2024 employment for SOC 25-1071 (Health Specialties Teachers, Postsecondary), as reported by O*NET sourcing the same BLS OEWS survey. This is the BLS 2024 anchor used for projections. The 2024 count encompasses the full breadth of 25-1071: full-time medical school faculty at academic medical centers, nursing program instructors at four-year universities (distinct from the 25-1072 nursing instructors at community colleges and hospitals), pharmacy professors, dental school faculty, public health professors, veterinary medicine faculty, physical therapy and occupational therapy program instructors, and practitioner-educators at community colleges with allied health programs. AAMC separately reports 134,804 full-time faculty specifically at LCME-accredited US medical schools in FY2024 -- roughly half the BLS 25-1071 total, with the remainder in pharmacy, dental, public health, and allied health programs.
Latest actual · 2024
$105,620
BLS OEWS May 2024 median annual wage for SOC 25-1071, as reported by O*NET. This reflects the full-range 25-1071 universe; the distribution is highly bimodal. AAMC FY2024 faculty salary survey data shows median compensation for full-time MD faculty at academic medical centers ranging from roughly $180,000 (basic science) to over $350,000 (clinical procedure-heavy specialties), while community college health professions instructors and entry-level pharmacy or nursing faculty at regional schools anchor the lower end of the distribution. The BLS median blends these populations.
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Lecture and anatomical demonstration (pre-laboratory era)

    The earliest American medical school faculty taught exclusively through lectures and anatomical dissection demonstrations. No microscopes, no chemistry laboratories, no clinical assignments: the professor stood at a podium and read from European texts, occasionally gesturing toward a cadaver on the dissecting table. Students took notes by hand. The same two-term curriculum repeated each year. One in twenty graduates had been within six feet of a living patient during their training. The primary tool of the trade was the academic lecture, modeled on European university tradition and requiring no institutional support beyond a room, a cadaver, and a paying audience.

    Work toolChanging equipment
  • Teaching hospital + laboratory science (Johns Hopkins model, Flexner era)

    Johns Hopkins Medical School opened in 1893 with a model that William Osler would spend the next fifteen years evangelizing across American medicine: the teaching hospital as the primary classroom, the clinical clerkship as the core learning activity, and full-time salaried faculty who could devote themselves entirely to teaching and research rather than splitting their time with a private practice downtown. The Flexner Report of 1910 used Carnegie and Rockefeller money to reward schools that adopted this model and effectively starve those that did not. By 1935 more than half of the 155 schools Flexner visited had closed or merged. The survivors invested in physiology and pathology laboratories, microscope rooms, and formal hospital affiliations. The faculty tool of this era was not a machine but a methodological transformation: the preclinical curriculum became laboratory-grounded science rather than transcribed lectures, and the clinical curriculum became apprenticeship under full-time expert supervision.

    Effect on the work

    The Flexner-era closures reduced the total number of US medical schools from 155 (1910) to 66 (1935), concentrating the faculty workforce in fewer, better-resourced institutions. The remaining schools invested in full-time faculty lines and laboratory infrastructure, creating the demand for the career academic physician that has defined the role ever since.

    Work toolChanging equipment
  • NIH grant infrastructure + audiovisual teaching aids (postwar research-education integration)

    The National Institutes of Health began funding university-based biomedical research in earnest after World War II, transforming the role of health specialties faculty. By the early 1950s, NIH was distributing hundreds of millions of dollars annually in research grants to medical school faculty who now had a third job: securing and managing federal grants alongside teaching and clinical practice. Audiovisual aids -- 35mm slide projectors, 16mm film reels for surgical technique, overhead projectors -- arrived in medical school lecture halls through the 1950s and 1960s. The overhead projector became the dominant lecture tool from the late 1960s through the mid-1990s. Programmed learning texts (forerunners of question banks) and early cadaver simulation models appeared in anatomy and surgery curricula.

    Effect on the work

    The NIH grant expansion drove the faculty headcount at medical schools from 3,500 (1945) to 17,000 (1965), as schools hired research-productive faculty to compete for federal research dollars. Teaching was increasingly structured around this research mission: faculty who could not secure grants were often not promoted, creating the research-or-teach tension that defines academic medicine to this day.

    Work toolChanging equipment
  • Standardized patients + OSCE examination (clinical skills assessment era)

    Standardized patient programs -- in which trained lay actors simulate patient presentations so students can practice history-taking and physical examination in a controlled setting -- were pioneered by Howard Barrows at the University of Southern California in the 1960s and spread widely through US medical schools in the 1970s and 1980s. The Objective Structured Clinical Examination (OSCE) format, developed by Ronald Harden at the University of Dundee in 1975, gave faculty a structured way to assess clinical skills at multiple stations. By the 1990s the United States Medical Licensing Examination included a clinical skills component requiring standardized patient encounters; this made OSCE design and standardized patient training core faculty competencies. The OSCE and standardized patient program represented the first major technology of health-education assessment -- replacing the single-examiner oral clinical examination with a multi-station, multi-faculty assessment infrastructure.

    Work toolChanging equipment
  • Learning management systems + digital question banks (ExamSoft, WebCT, Blackboard)

    WebCT launched in 1995 and Blackboard in 1997, giving health professions faculty their first platforms for posting lecture notes, assignments, and grades online. ExamSoft, founded in 1992, became the dominant digital exam platform at medical and dental schools through the 2000s, replacing paper-and-pencil examinations with computer-based testing that generated item analytics automatically. Digital question banks from the National Board of Medical Examiners and commercial providers (Kaplan, USMLEWorld, later BoardVitals) gave faculty ready-made formative assessment questions in USMLE-Step format. PowerPoint replaced the overhead projector as the universal lecture medium through the late 1990s. By 2005 virtually every US medical school course had an LMS presence, and by 2010 most had adopted some form of computer-based formative assessment. The faculty task that changed most was exam authoring: writing new questions was now documented, tracked, and benchmarked against item-difficulty statistics.

    Work toolChanging equipment
  • High-fidelity simulation + virtual patient platforms (SimMan, Aquifer, Osso VR)

    High-fidelity patient simulators -- programmable manikins that breathe, produce heart sounds, and can be programmed to deteriorate in response to student interventions -- became core infrastructure at US medical and nursing schools through the 2010s. Laerdal's SimMan and CAE Healthcare's HPS were the dominant platforms. Simultaneously, virtual patient case libraries (Aquifer, formerly Aquifer Medicine) delivered branching clinical-reasoning cases online; students could complete 10-15 virtual patient encounters outside of scheduled class time, freeing faculty supervision hours for the debrief and high-stakes direct observation. Osso VR (2016) and Touch Surgery (2013) extended this model to surgical procedural training. The effect on faculty was structural: simulation technology created a new job category within academic medicine -- simulation faculty or simulation center director -- and shifted routine skills-practice volume away from faculty-supervised time toward asynchronous platforms, allowing faculty to concentrate supervision on the highest-stakes clinical encounters.

    Work toolChanging equipment
  • Generative AI for medical education (ChatGPT Edu, BoardVitals AI, Osmosis AI, Elicit)

    The release of ChatGPT in late 2022 and its rapid adoption by medical students and faculty marked a step-change in what health professions educators had to manage. By 2024 the AAMC had published formal guidance for faculty on AI in medical education, documenting that lecture content generation, clinical case vignette creation, USMLE-style question writing, and feedback on written student work were tasks where AI was already being deployed in health sciences programs. Simultaneously, tools like Elicit and Consensus began absorbing the literature-synthesis labor that previously consumed hours of faculty time per curriculum update cycle (medically relevant knowledge doubles roughly every 73 days, creating a perpetual update burden). BoardVitals AI and Osmosis AI began generating formative question items automatically, reducing the faculty effort required to maintain current exam banks by an estimated 40-60% per NEJM AI review evidence. Glass Health, which generates clinical differential diagnoses from brief patient summaries, made unobserved written clinical reasoning assignments largely untenable as valid assessment instruments, triggering widespread curriculum redesign toward direct observation. The core of the role remains structurally durable: LCME, ACGME, CCNE, and ACPE accreditation standards require faculty-of-record accountability for competency assessment that cannot legally be delegated to an AI system.

    Effect on the work

    The 17.3% BLS employment growth projection for 2024-2034 suggests that AI augmentation is not displacing demand for health specialties teachers -- rather, the structural driver (a large and persistent US physician and allied-health workforce shortage) keeps demand for new graduates, and therefore the faculty who train them, elevated above any automation savings within the curriculum.

    AI audit toolsPattern detection
Projection cone · present → 2036

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
BLS National Employment Matrix 2024-34
2034
+17.3%
BLS Employment Projections program, industry-occupation matrix with labor-productivity and demographic assumptions. The 2024-34 cycle projects +17.3% employment change for SOC 25-1071, equivalent to approximately +50,100 positions -- from 289,600 (2024) to 339,700 (2034). This is classified as "much faster than average" against an all-occupations average of roughly +4%. The BLS methodology cites continued robust demand for health-related degrees driven by the projected physician and allied-health workforce shortages across primary care, nursing, and specialty medicine. The projection does not separately model the AI-assisted curriculum production that is beginning to reshape faculty workload -- the structural demand driver (student enrollment) is what drives the headcount projection.
AAMC — The Complexities of Physician Supply and Demand 2024
2036
+12%
AAMC physician workforce projections model a shortage of 37,000-124,000 physicians by 2034, with primary care and specialty care both affected. The shortage is the structural demand driver for health specialties teachers: more physicians needed means more medical school slots, more clinical training capacity, and more faculty. The +12% estimate here is a conservative read of the implied demand for health professions faculty from the AAMC physician pipeline projections, anchored on the 2024 employment base. The figure is not a direct AAMC projection for 25-1071 headcount but a demand-side inference.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Eloundou et al. — "GPTs are GPTs" (2023)
2028
49%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for Education, Training, and Library occupations including postsecondary teachers. Postsecondary teachers as a group score in the moderate-to-high range for LLM exposure: the tasks of preparing lecture materials, writing exam questions, synthesizing literature for curriculum updates, and providing written feedback on student work all have substantial LLM exposure. The score here reflects the exposure share of tasks, not a headcount displacement projection -- consistent with kind:'exposure'. The critical distinction: LLM exposure does not translate to headcount decline in a structural-shortage profession. High exposure means the daily workflow can be substantially augmented, not that the job disappears.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereWrite, update, and validate USMLE-style, NCLEX-style, or NAPLEX-style formative exam questions for each course module — using AI question-generation tools (BoardVitals AI, Osmosis AI question writer, ExamSoft item analytics) to draft 8-12 new multiple-choice items per lecture topic, then applying clinical expertise to review item validity, distractor plausibility, and alignment with current evidence-based guidelines.

Write, update, and validate USMLE-style, NCLEX-style, or NAPLEX-style formative exam questions for each course module — using AI question-generation tools (BoardVitals AI, Osmosis AI question writer, ExamSoft item analytics) to draft 8-12 new multiple-choice items per lecture topic, then applying clinical expertise to review item validity, distractor plausibility, and alignment with current evidence-based guidelines.[4],[8],[9]

Where your edge is

Adopt AI-assisted item generation for the bulk of formative question production — the NEJM AI review (2024) documents 40-60% time savings on assessment creation at medical schools using generative AI tools. Redirect your expert effort to the clinical review layer: validating that distractors reflect real clinical decision-making errors students make, confirming alignment with updated guidelines (e.g., recent ACC/AHA, IDSA, or FDA label changes), and flagging items where AI produced plausible-sounding but medically outdated or incorrect content.

AI is sitting alongside you hereDevelop and continuously update course content in anatomy, physiology, pharmacology, pathology, clinical medicine, or specialty disciplines — using AI research-synthesis tools (Elicit, Consensus) to track the medical literature, identify guideline updates, and synthesize evidence for curriculum revision, keeping pace with a medical knowledge base that doubles every 73 days (NLM 2022 estimate).

Develop and continuously update course content in anatomy, physiology, pharmacology, pathology, clinical medicine, or specialty disciplines — using AI research-synthesis tools (Elicit, Consensus) to track the medical literature, identify guideline updates, and synthesize evidence for curriculum revision, keeping pace with a medical knowledge base that doubles every 73 days (NLM 2022 estimate).[3],[10],[11]

Where your edge is

Use Elicit to run a saved search across PubMed for key clinical topics in your specialty (e.g., "diabetes management 2025", "first-line antibiotic MRSA 2025") and have it extract outcome data, study design, and confidence ratings from the top 20 hits automatically — this turns a 2-hour literature sweep into a 15-minute review queue. Then apply your clinical expertise to decide which updates are practice-changing enough to revise lecture slides, case vignettes, or pharmacology tables. The judgment call of what matters clinically requires your domain credibility; the synthesis work does not.

AI is sitting alongside you hereDeliver didactic lectures on clinical science topics — pharmacology, pathophysiology, diagnostic reasoning, or specialty-specific content — using AI tools (ChatGPT Edu, Osmosis) to draft first-version slide decks and case vignettes, then layering in current clinical evidence and bedside teaching insights drawn from active practice to ensure content has the epistemic authority that students require from a clinical expert.

Deliver didactic lectures on clinical science topics — pharmacology, pathophysiology, diagnostic reasoning, or specialty-specific content — using AI tools (ChatGPT Edu, Osmosis) to draft first-version slide decks and case vignettes, then layering in current clinical evidence and bedside teaching insights drawn from active practice to ensure content has the epistemic authority that students require from a clinical expert.[3],[9]

Where your edge is

Use ChatGPT Edu to generate a 50-slide lecture deck first draft — it will produce plausible structure and standard pharmacology or pathophysiology content. Then invest your effort in replacing generic AI examples with cases from your own clinical experience, updating drug dosing or guideline references to 2025 standards, and adding the "this is what you'll actually see in clinic" framing that students find irreplaceable. AAMC faculty survey data (2025) consistently shows students rate clinical-experience-grounded content as significantly more valuable than AI-only generated material.

Where this role is heading

Natural next steps for someone with your foundation: not exits, evolutions.

A direction you could grow

Medical and Health Services Managers

Health specialties faculty — particularly those who have served on curriculum committees, led LCME/ACGME accreditation self-studies, or directed simulation centers — have the clinical credibility and institutional governance experience to transition into medical education leadership or health services administration roles. Academic medical centers urgently need administrators who can bridge clinical operations and education program management, evaluate AI clinical decision-support tools for institutional deployment, and develop AI governance frameworks for health professions programs. The CRI increase reflects that Medical and Health Services Managers are meaningfully augmented by AI for operations analytics and reporting, and the role is growing significantly with health system consolidation.

What you'd add
  • · Healthcare operations management: budgeting for clinical programs, workforce planning, and quality improvement (PDSA cycles, LEAN healthcare)
  • · LCME/ACGME/CCNE accreditation program administration: self-study coordination, continuous quality improvement documentation, and site visit preparation
  • · Health IT and EHR governance: evaluating AI clinical decision support tools for institutional deployment (FDA 510(k) clearance, bias audits)
  • · Strategic planning for academic medical centers: enrollment, clinical revenue, and research portfolio management
  • · Healthcare AI policy: developing institutional AI use policies for clinical and educational applications aligned with AMA and AAMC guidance
What it takesSome new skills to pick up
Share this year
Drops anyone you send it to straight into 2026.
Preview card
Part of Education · see all 51roles →
Different role?

See the same long-arc view for your own profession.

Browse the directory by industry, or search by title or SOC code. New roles ship every few weeks. Every profile cites every claim.

Browse all roles

The data behind this timeline

On record since1765
Latest tracked employment289,600 (US, 2024)
Latest median pay$105,620 (2024)
Outlook+17.3% by 2034 (BLS National Employment Matrix 2024-34)
View all 26 cited data points
YearUS employmentMedian annual paySource
19453,500n/aESTIMATE
196517,000$12,000ESTIMATE
199085,000n/aESTIMATE
2000n/a$68,000ESTIMATE
200388,130$61,790BLS-OEWS
2004105,610$64,630BLS-OEWS
2005108,680$70,890BLS-OEWS
2006116,370$77,190BLS-OEWS
2007114,070$80,700BLS-OEWS
2008125,100$84,390BLS-OEWS
2009133,070$84,840BLS-OEWS
2010144,780$85,270BLS-OEWS
2011153,430$79,860BLS-OEWS
2012152,130$81,140BLS-OEWS
2013163,850$85,030BLS-OEWS
2014168,090$90,210BLS-OEWS
2015178,900$90,840BLS-OEWS
2016186,740$99,360BLS-OEWS
2017194,610$97,870BLS-OEWS
2018199,480$97,370BLS-OEWS
2019201,920$97,320BLS-OEWS
2020200,040$99,090BLS-OEWS
2021191,830$102,720BLS-OEWS
2022207,700$100,300BLS-OEWS
2023225,360$105,650BLS-OEWS
2024289,600$105,620BLS-OEWS
Embed this timeline on your site

Free for any site. Paste this where the timeline should appear; it stays interactive, every datapoint stays cited, and it sets no cookies on your page. How embedding works

<iframe src="https://futurehistory.earth/embed/25-1071"
  width="100%" height="430" style="border:0"
  title="Health Specialties Teachers, Postsecondary, a Future History timeline"
  loading="lazy"></iframe>

See all roles in Education