Lesson 356 of 2116
IRB And Ethics In AI Research: What Changes, What Doesn't
Using AI in human-subjects research raises new IRB questions. Here's how to get approved without surprising your review board.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The classic IRB checklist still applies
- 2IRB Protocol Amendments: AI-Assisted Drafting for the Iterative Reality of Research
- 3The premise
- 4Research Ethics Protocols for AI-Assisted Studies
Concept cluster
Terms to connect while reading
Section 1
The classic IRB checklist still applies
- Informed consent, properly documented
- Risk minimization and justification
- Data security plan
- Equitable participant selection
- Privacy protections commensurate with sensitivity
What AI adds to the checklist
- 1If AI analyzes participant data, the consent form must say so
- 2If data is sent to a third-party API (OpenAI, Anthropic, Google), the consent AND the data security plan must disclose this — and often the vendor's data-use policy must be attached
- 3If the AI could re-identify anonymized data, the IRB needs to know
- 4If the AI generates content that participants will see (chatbot-based research), the IRB needs the exact model version and prompt frozen
- 5If AI scoring affects participant outcomes, bias testing is typically required
The ethics of LLM participants
Some researchers now use LLMs as 'synthetic participants' — simulating survey responses or interview answers. This is cheap and fast. It is also a minefield: LLMs carry training-data biases, and findings from synthetic participants may not generalize. IRBs increasingly ask whether synthetic-participant studies count as 'human subjects research' at all. Answer this for your project before you submit.
Key terms in this lesson
The big idea: AI doesn't create new ethical categories — but it intensifies old ones. Disclose specifically, protect participants aggressively, and talk to your IRB before you submit.
Section 2
IRB Protocol Amendments: AI-Assisted Drafting for the Iterative Reality of Research
Section 3
The premise
Amendment drafting is a recurring administrative burden; AI accelerates it without replacing the substantive risk-benefit analysis.
What AI does well here
- Draft amendment narratives for common scenarios (new study site, new investigator, recruitment material change)
- Generate revised consent forms with tracked changes and a brief change rationale
- Produce the amendment justification matching the institution's IRB form structure
- Generate the cover letter summarizing the amendment's significance to ongoing risk-benefit analysis
What AI cannot do
- Make the regulatory determination of whether an amendment is minor vs. significant
- Substitute for the IRB's own substantive review
- Generate amendments to consent that haven't been discussed with the study team
Section 4
Research Ethics Protocols for AI-Assisted Studies
Section 5
The premise
AI use in research carries new ethics considerations IRBs are now examining; explicit protocol planning prevents review delays.
What AI does well here
- Disclose AI use in IRB protocols (analysis, recruitment, intervention, interaction)
- Document AI risks specific to your protocol (re-identification, hallucination harm, bias)
- Address AI in informed consent when participants interact with AI
- Stay current on your institution's AI-research-ethics guidance
What AI cannot do
- Substitute AI use disclosure for substantive ethics review
- Replace researcher accountability for participant welfare
- Anticipate every AI ethics issue (the field evolves)
Section 6
Using AI to Draft IRB Protocol Deviation Reports
Section 7
The premise
AI can structure deviation reports covering what happened, impact, and corrective action.
What AI does well here
- Describe deviation factually
- Propose corrective actions
What AI cannot do
- Decide reportability threshold
- Speak for the IRB
Understanding "Using AI to Draft IRB Protocol Deviation Reports" in practice: AI is transforming how professionals approach this domain — speed, precision, and capability all increase with the right tools. Document protocol deviations clearly for IRB review — and knowing how to apply this gives you a concrete advantage.
- Apply IRB in your research workflow to get better results
- Apply deviation in your research workflow to get better results
- Apply reporting in your research workflow to get better results
- 1Apply Using AI to Draft IRB Protocol Deviation Reports in a live project this week
- 2Write a short summary of what you'd do differently after learning this
- 3Share one insight with a colleague
Section 8
AI and an IRB protocol summary rewrite
Section 9
The premise
IRB lay summaries default to jargon. AI can lower reading level while preserving the regulatory content.
What AI does well here
- Lower reading level to 8th grade.
- Replace jargon with everyday words.
- Preserve risk and benefit language.
What AI cannot do
- Decide which risks must be disclosed.
- Replace the PI's responsibility for accuracy.
- Capture local IRB preferences without you sharing them.
Section 10
AI and IRB Amendment Summary: Tracking Protocol Changes
Section 11
The premise
AI can diff two protocol versions and produce an IRB amendment summary that organizes changes by IRB-relevant category.
What AI does well here
- Categorize changes (consent, eligibility, procedures, risk) per IRB norms
- Surface changes that may trigger re-consent of enrolled participants
What AI cannot do
- Decide whether a change is minor or substantive under the IRB's policy
- Sign the IRB submission as the PI
Section 12
AI and IRB Protocol Skeletons: Human-Subjects Drafting
Section 13
The premise
AI can take a study summary and produce an IRB protocol skeleton with sections for purpose, recruitment, consent, risks, and data.
What AI does well here
- Produce the standard IRB section structure
- Suggest standard consent language as a starting point
What AI cannot do
- Make ethical judgments about risk and benefit
- Replace IRB review or institutional sign-off
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “IRB And Ethics In AI Research: What Changes, What Doesn't”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 11 min
AI for IRB Modification Requests: Clean Justifications That Get Approved
Draft IRB modification requests that clearly state what changed, why, and the risk implications.
Creators · 40 min
AI Systematic Review Protocol Draft: Drafting With Human Oversight
AI can draft a systematic review protocol draft narrative that organizes inputs into a structured document the responsible professional reviews, edits, and signs.
Builders · 7 min
AI and student research ethics: the IRB rules even teen researchers should know
AI explains the consent and ethics rules for any research project involving people.