AI Literacy for Health Professions | Health AI
AI Literacy for Health Professions

Build the Judgment
No AI Tool Can Provide

Health professions faculty, curriculum committees, and life sciences teams need more than AI awareness – they need validated methods for evaluating AI outputs, embedding AI governance into curriculum, and teaching the next generation of clinicians to think critically about the tools they will inherit.

These programs are grounded in the RIGOR™ Framework and led by a practitioner with IRB-active research in AI validation methodology. Not a vendor course. Not a certification program. A science-grounded approach built from the inside.

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Research Scientist & Practitioner-Led
61% of health professions faculty now use AI in their teaching – yet most lack structured training to evaluate what they're using Digital Education Council Global AI Faculty Survey, 2025
60% of health professions students report they do not feel confident in their AI knowledge or skills as they prepare for clinical practice Digital Education Council, 2025; MUSC HCS Program Report, 2026
0 major AI governance platforms address faculty-level AI validation literacy as a structural curriculum gap – this is an unoccupied practitioner space Health AI market analysis, March 2026
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The Gap Health professions programs are integrating AI faster than faculty can evaluate it. UNESCO, ACGME, and accreditation bodies have published competency frameworks – but no practitioner deployment layer exists to bridge them into the classroom.
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The Approach The RIGOR™ Framework applied to AI literacy. The same structured validation discipline used for clinical AI deployment – taught as a teachable, transferable skill for faculty developers and students alike.
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The Difference These programs are led by a PhD research scientist. The methodology is IRB-active research, not a vendor framework. Participants leave with something they can actually teach – not just a certificate and a slide deck.

Key Takeaway

Health professions programs need AI literacy at three levels: foundational knowledge of how models work, domain-specific validation skills for evaluating clinical and research AI outputs, and governance competency for documenting AI use in regulated environments. The RIGOR™ Framework provides the practitioner implementation layer that bridges published competency standards (UNESCO, ACGME, AAMC) into deployable classroom and curriculum practice.

Who This Is For

High-stakes environments. One validation standard.

These programs serve environments where the cost of acting on an unvalidated AI output has real downstream consequences – for patients, for students, for regulatory submissions, or for institutional credibility.

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Health Professions Faculty

Nursing, pharmacy, PA, medicine, and allied health educators who need to evaluate AI tools for curriculum use, embed AI governance into existing courses, and teach responsible AI use to students entering clinical environments already deploying AI decision support.

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Curriculum Committees & Program Directors

Health professions program directors, curriculum committee members, and department chairs building AI competency into undergraduate and graduate programs ahead of accreditation requirements. AAMC, ACGME, and the GMC are already revising standards.

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Biotech & Drug Discovery Teams

Research scientists and computational biologists using AI in lead identification, protein modeling (AlphaFold), or clinical data analysis – where the validation gap connects directly to regulatory exposure and publication defensibility.

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Academic Medical Centers & Research Institutions

University research centers and academic medical programs where AI tools are entering lab and clinical education workflows faster than governance frameworks can catch up. The IRB-ready Prompt Ladder methodology is available for institutional adoption.

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Pharmaceutical & Clinical Research

CROs, pharma R&D teams, and clinical scientists who need to evaluate AI-assisted outputs – from literature synthesis to trial design – with a documented, defensible methodology aligned to FDA and EMA guidance.

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Enterprise Science & Regulated Industries

Organizations deploying AI in regulated or high-consequence environments – healthcare systems, automotive safety, industrial quality – where the same scientific validation discipline transfers across domains. RIGOR™ has been applied at RFP stage against major enterprise vendors.

Built on the RIGOR™ Framework

These programs are not generic AI literacy courses. Every session is grounded in the same methodology Health AI uses for deployment-grade AI validation across healthcare, automotive, and enterprise environments. Participants build vocabulary, structure, and documentation practices that hold up under regulatory review, accreditor scrutiny, or investor diligence – not just workshop output.

View the Framework →
Health Professions Education Programs

Two Programs for Faculty & Institutions

Designed specifically for health professions education – from individual faculty development to institutional curriculum integration. Both programs are grounded in active research and carry IRB-ready methodology available for adoption.

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Applied AI Validation Workshop Series

Faculty Development for Health Professions Educators

A structured workshop series for health professions faculty building practical competency in evaluating AI tools, embedding AI governance into existing curriculum, and teaching responsible AI use to students entering clinical environments already deploying AI decision support.

Grounded in the RIGOR™ Framework and aligned to UNESCO, ACGME, and IACAI AI competency guidelines – with the practical implementation layer that makes those frameworks deployable in a classroom or curriculum committee setting. Led by a PhD research scientist practitioners, not vendor trainers. Custom-tailored to your program's tools, student level, and accreditation context.

What Faculty Leave With:

  • A structured method for evaluating AI tools before incorporating them into course content
  • A working vocabulary for discussing AI limitations with students, colleagues, and accreditors
  • A draft AI use policy or course-level integration framework for their specific program
  • Curriculum design guidance mapped to AAMC, ACGME, and IACAI AI competency frameworks
Faculty Development HPE Curriculum Accreditation-Aligned Custom-Tailored
Ideal for: Nursing, pharmacy, PA, medicine, allied health faculty · Curriculum committees · Program directors building AI literacy into accreditation prep
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The Prompt Ladder

AI Literacy Research Initiative – IRB-Ready Methodology

A structured classroom AI literacy curriculum using a validated prompt ladder methodology – a within-subject design that teaches health professions students how to critically evaluate AI outputs by comparing responses across Claude, ChatGPT, and Gemini for the same clinical or scientific query.

The methodology is IRB-ready and designed for institutional adoption. Programs that integrate the Prompt Ladder gain a research-grounded, publishable AI literacy intervention – not just a workshop. The data collected contributes to the growing evidence base on health professions AI literacy outcomes.

What the Methodology Provides:

  • Structured prompt sequence across health-relevant domains (clinical reasoning, drug information, research synthesis)
  • Validated rubric for evaluating AI output quality, accuracy, and clinical appropriateness
  • Within-subject cross-model comparison design (IRB-ready; publishable)
  • Student reflection framework aligned to AI competency outcomes
IRB-Ready Research-Grade Cross-Model Validation Publishable Design
Ideal for: Undergraduate and graduate health professions programs · Academic medical centers building evidence-based AI literacy curriculum · Faculty researchers targeting MedEd or JChemEd publication

Both programs are available for institutional adoption. Contact us to discuss scope, timeline, and IRB documentation support.

Life Sciences & Research Programs

Four Delivery Formats.

Each program can be delivered as-designed or customized to the specific tools, research domain, and risk context of your team.

01

When to Trust the Model

AI Validation Essentials for Life Sciences Researchers

Half-day · 4 hrs · up to 25 $4,500 – $6,000

What It Addresses

Most researchers using AI tools have no structured method for evaluating when the output is trustworthy – or how to document that judgment for a manuscript, collaborator, or regulatory submission. This program closes that gap using RIGOR™ as a practical evaluation tool.

Hands-on throughout. Participants work on outputs from their own research domain, not generic case studies.

Participants Leave With

  • The RIGOR™ one-page validation reference card
  • A working draft AI use protocol for their lab or team
  • A defensible validation statement template for manuscripts and submissions
  • Structured vocabulary for communicating AI limitations to collaborators and reviewers
Ideal for Research scientists, computational biologists, lab teams in biotech incubators, academic research centers
02

AI-Ready

How to Talk About Your AI to Investors and Regulators

2.5 hrs · up to 30 $3,000 – $4,500

What It Addresses

Sophisticated investors and regulatory reviewers are now asking pointed questions about AI validation methodology in diligence and submissions. Most founders and senior scientists have no structured response. This session provides the language, the framework, and the documentation approach.

Covers what FDA expects when AI touches a drug development workflow, what "validation" means in an IND context, and how to frame AI-assisted research without overclaiming.

Participants Leave With

  • A working vocabulary defensible in regulatory and investor contexts
  • A one-paragraph AI methodology statement adaptable for IND, publication, or pitch deck
  • Clarity on where FDA AI/ML guidance creates risk in their specific pipeline
  • A documentation framework for ongoing AI use
Ideal for Biotech founders, CSOs, senior researchers preparing for diligence, IND submissions, or Series A
03

AI Tools in Chemistry, Biology & Drug Discovery

Hands-On Validation for Structural Prediction, Molecular Modeling & Cheminformatics

Half-day · 4 hrs · up to 20 $3,500 – $5,500

What It Addresses

Researchers and students using AI tools in chemistry, structural biology, and drug discovery face a shared problem: the tools are powerful, the confidence scores are seductive, and the validation standards are poorly understood. This workshop builds hands-on competency for the tools people are actually using – not hypothetical examples.

Tools covered (customizable by cohort):

AlphaFold 2/3 DeepMind/Google Structure prediction, pLDDT confidence scores, PAE maps, publication readiness criteria
RFdiffusion & ProteinMPNN Baker Lab De novo protein design, backbone generation, diffusion model validation
Cheminformatics & SAR RDKit · DeepChem Molecular fingerprints, AI-assisted lead optimization, model interpretability
LLMs for Science BioGPT · Elicit · Semantic Scholar Evaluating AI-synthesized literature claims, PubChem AI search, hallucination risk
AI Microscopy & Imaging CellPose · bioorthogonal AI Real-time cellular tracking, AI-enhanced bioorthogonal imaging analysis

Participants Leave With

  • A tool-specific validation checklist covering all programs used by the cohort
  • A structured framework for communicating AI output confidence in manuscripts, grants, and regulatory submissions
  • Criteria for when computational predictions require experimental validation vs. can stand independently
  • Documentation templates aligned with journal and regulatory expectations (FDA, EMA, Nature/Science data availability standards)
  • A working vocabulary for discussing AI tool limitations with collaborators, reviewers, and IRBs

This workshop is highly customizable. Tool selection is confirmed during the scoping conversation based on the cohort's actual research stack.

Ideal for Computational biologists, structural chemists, medicinal chemistry teams, undergraduate and graduate students in life sciences programs, drug discovery teams using AI-based modeling
04

Building Your Team's AI Protocol

A Facilitated Working Session for a Single Team

Half-day · 4–8 participants $3,000 – $5,000

What It Addresses

A bespoke working session for one team. Not a workshop – a structured facilitation that produces a real deliverable: a draft AI use protocol built around the team's specific tools, research domain, risk tolerance, and regulatory context. The protocol is designed to hold up under institutional or regulatory scrutiny.

The Team Leaves With

  • A completed draft AI use protocol tailored to their specific context
  • RIGOR™ pillar assessment for each AI tool currently in use
  • A prioritized list of validation gaps and remediation steps
  • A document ready for institutional review, compliance, or an IRB
Ideal for Single research labs, biotech compliance leads, teams preparing for regulatory submission or institutional AI review
"The benchmark-only standard is no longer defensible. Validation is a lifecycle discipline – and it begins with the humans making decisions, not the software monitoring them."
– Olga Lavinda, PhD · CEO, Health AI
How It Works

From first conversation to delivered program.

Step 01

Scoping Conversation

A 20-minute call to understand your team's current AI tools, context (clinical, research, educational), regulatory environment, and the specific gap to close. No commitment required.

Step 02

Program Selection

We identify the right format. For health professions programs: whether the Applied AI Validation Workshop Series, Prompt Ladder adoption, or a customized session fits the curriculum need. Custom engagements quoted separately.

Step 03

Delivery

In-person or hybrid. Led by Dr. Lavinda directly – not delegated. For Prompt Ladder adoptions, full IRB documentation and student materials provided.

Step 04

Deliverables & Follow-On

Every participant leaves with a concrete output. Institutions can extend to ongoing faculty development, curriculum advisory, or IRB-collaborative research arrangements.

Common Questions

Frequently Asked Questions

How do I integrate AI literacy into a health professions curriculum?
Integrating AI literacy into health professions curriculum requires working at three levels simultaneously: foundational literacy (what AI models do and how they fail), domain-specific validation skills (how to evaluate AI outputs in clinical or research contexts), and governance competency (how to document AI use and communicate limitations to regulators, reviewers, or accreditors). UNESCO, ACGME, and IACAI have each published competency frameworks – the RIGOR™ Framework functions as the practitioner implementation layer that makes those frameworks deployable in a classroom or workshop. The Applied AI Validation Workshop Series and Prompt Ladder methodology are both built around this three-level structure.
What AI competencies should health professions students have before graduation?
Based on guidance from AAMC, the General Medical Council, and WHO, graduating health professionals should be able to: identify when an AI system is being used in their clinical environment; evaluate AI-generated outputs for reliability and potential bias; document their reasoning when acting on an AI recommendation; communicate AI limitations to patients, colleagues, and supervisors; and recognize when an output requires independent verification. The Prompt Ladder methodology provides a structured classroom approach to building these competencies at the undergraduate and graduate level, with outcomes that are measurable and publishable.
What is the Applied AI Validation Workshop Series?
The Applied AI Validation Workshop Series is a faculty development program designed for health professions educators. It builds practical competency in evaluating AI tools for educational use, embedding AI governance into existing curriculum, and teaching responsible AI use to students in clinical training programs. Grounded in the RIGOR™ Framework – the same validation methodology applied to clinical AI deployment – and led by a PhD research scientist practitioners, not vendor trainers. Sessions are custom-tailored to each program's tools, student level, and accreditation context. Faculty leave with structured methods they can adapt and teach immediately.
What is the Prompt Ladder and can my institution adopt it?
The Prompt Ladder is an IRB-ready AI literacy curriculum using a structured prompt methodology that teaches students how to critically compare AI system outputs (Claude, ChatGPT, Gemini) across clinical and scientific queries. It was developed for health professions students who need to evaluate AI outputs rigorously rather than accept them uncritically. The within-subject design is publishable in medical education and science education journals. The full methodology – including student materials, facilitator guide, rubrics, and IRB documentation – is available for institutional adoption. Contact us to discuss implementation.
Are there AI governance requirements coming for health professions accreditation?
Accreditation bodies are actively moving in this direction. The AAMC has introduced guiding principles encouraging AI-related competencies throughout medical training. The GMC revised graduate outcomes to include digital fluency and data literacy. ACGME published milestone frameworks for AI competency in faculty development. Programs that wait for formal requirements will face reactive compliance. Those building structured AI literacy infrastructure now – using validated frameworks, documented competency outcomes, and IRB-grounded methodology – are building the evidence base that accreditation standards will eventually formalize. A well-documented Prompt Ladder implementation, for example, provides exactly the kind of evidence a program needs when an accreditor asks how AI literacy is being addressed.
How is this different from other AI literacy courses or certification programs?
Most AI literacy programs in health professions education are either generic (covering AI concepts without domain-specific validation methodology) or vendor-produced (designed to build comfort with a specific tool rather than critical evaluation across tools). This program is different in three ways: it is grounded in the RIGOR™ Framework – the same validation methodology applied to clinical AI deployment, not a simplified version; it is led by an active health professions faculty member with IRB-active research in AI literacy, not a training associate; and it produces deployable outputs – a protocol, a curriculum module, or a validated research methodology – not just a certificate. The Prompt Ladder is among the only AI literacy curricula in health professions education with a within-subject comparative design publishable in peer-reviewed journals.

Start the Conversation

Ready to build a validated team?

Start with a 20-minute scoping call. We'll identify the right program for your context – faculty development, IRB research adoption, or life sciences training – and whether customization is needed.

Talk to a Validation Scientist View the RIGOR™ Framework

Our Team

Validation Science & AI Governance

Olga Lavinda, PhD

CEO of Health AI. Research scientist specializing in AI validation, polypharmacology, and translational science. Dr. Lavinda developed the RIGOR™ Framework from her background in polypharmacology, chemometrics, and NIH-funded translational science. All programs are led by Dr. Lavinda and collaborating research scientists directly.

olgalavinda.com · LinkedIn

Frontier Biology & Life Sciences Curriculum

Collaborating Faculty Scientists

Research scientist in molecular biology and frontier biotechnology with expertise spanning generative biology, computational protein design, autonomous lab systems, and AI-driven drug discovery workflows. Contribute curriculum design and scientific direction for life sciences programs.

Full bios available upon request
Book a Workshop Tell us your context – tools, audience, format – and we'll design the right session.

Last Updated: March 2026

Sources & References

  1. Digital Education Council. Global AI Faculty Survey. 2025. (61% of faculty globally use AI in teaching; ~60% of students lack AI confidence)
  2. Khamis N, Ungaretti T, Tackett S, Chen BY. From AI Literacy to Leadership: Milestones for Faculty Development in Health Professions Education. Medical Science Educator. 2025. doi:10.1007/s40670-025-02438-0
  3. MUSC College of Health Professions. HCS Program Redesign: Preparing Students for AI in Health Care. 2026. chp.musc.edu
  4. Frontiers in Public Health. Advancing public health education by embedding AI literacy. 2025. PMC12307283.
  5. Frontiers in Public Health. From resistance to readiness: faculty development as the key to AI literacy in public health. 2026. doi:10.3389/fpubh.2026.1794913
  6. UNESCO. AI Competency Framework for Teachers. 2024.
  7. IACAI. Integrating Artificial Intelligence into Medical Education: A Vision for the Future. 2024. medbiq.org
  8. AAMC. Guiding Principles: AI in Medical Education. 2024.
  9. FDA & EMA. Guiding Principles of Good AI Practice in Drug Development. January 14, 2026.
  10. Izquierdo-Condoy JS et al. Artificial Intelligence in Medical Education: Transformative Potential, Current Applications, and Future Implications. JMIR. 2026. doi:10.2196/77127

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Olga Lavinda, PhD · CEO, Health AI · © 2026 Health AI LLC. RIGOR™ is a trademark of Health AI.