RACP AI-Ready REALTOR© Certification Course
92% of REALTORS® now use or plan to use AI in their practice (NAR 2025 Member Survey). Most have no training, no documentation, and no defensible record of how they’re using these tools with clients. When the first fair housing complaint, E&O claim, or state board inquiry investigates — “I just used ChatGPT” is not a defense.
The Responsible AI Certified Professional™ (RACP) is your independent, third-party verification that you understand the risks, follow documented standards, and apply AI responsibly in client-facing work. It is issued by the National Institute for Responsible AI Practice (NIRAP), a standards body.
About this course.
The Responsible AI Certified Professional™ course is a structured training program developed by the National Institute for Responsible AI Practice to establish a recognized standard of competency for professionals who use AI in client-facing roles. Participants complete a curriculum covering risk identification, documentation requirements, and jurisdiction-specific compliance considerations. Upon completion, credential holders receive a verifiable designation that signals accountability to clients, employers, and regulators.
Note: Course content is presented using AI-assisted video production.
What you get.
- Self-paced online curriculum aligned to NAR's published AI guidance
- 60-question certification exam (50 scored, 10 pretest)
- Verifiable digital credential, displayable on LinkedIn, MLS profiles, signature blocks, business cards, and listing presentations
- Public listing in NIRAP's registry of credentialed professionals
- Two-year credential with a structured recertification path
What the curriculum covers.
- AI literacy: hallucination, sycophancy, bias, confidence versus accuracy
- Risk identification and triage in real estate workflows
- Duty of care and fiduciary obligation in AI-augmented practice
- Documentation standards that hold up under scrutiny Fair housing, AI disclosure rules, and state-level compliance
- Client communication and disclosure thresholds
