How to integrate AI into your research flow with integrity.
The HEARTS Framework is a six-part method for deciding whether an AI-assisted research workflow is actually better — not just faster. It’s how we, as UX researchers, incorporate AI with intention and integrity.
Six lenses. One question each.
HEARTS is an acronym for six design principles you run an AI-assisted workflow through. Each letter is a lens — and each lens comes with one blunt question you ask your workflow.
Human-led & centered
The researcher is the pilot, not the passenger. AI is a tool directed by and for humans. We prioritize the needs, context, and well-being of everyone in the research process — practitioners, participants, and partners.
Where does the human stay in control? If the answer is “nowhere,” you’ve built an automation, not an AI-assisted workflow.
Experience-focused
We are accountable for the quality of the experience. Every interaction with AI and its outputs — by a researcher, a participant, or a stakeholder — must be intuitive, respectful, and positive.
Does this improve the experience for everyone it touches, or just speed things up for you?
Amplification, not automation
AI amplifies researchers’ superpowers — you just have to earn it. Delegate the repetitive and unnecessary; protect the brainpower for critical thinking, strategic synthesis, and building empathy.
Is AI freeing the researcher to do something more valuable, or just doing the valuable part for them?
Rigorous & responsible
Maintain integrity, minimize bias, maximize confidence. We proactively mitigate bias and maximize accuracy in every tool and process. We don’t “check the box” on ethics; we build our practice around it.
How are you validating accuracy and checking for bias — your own and the model’s?
Trustworthy & transparent
Transparency is the bedrock of trust; traceability is the bridge. From collection to analysis, we provide a clear path for verification and prove the lineage of our insights. We disclose by default.
If someone audited this process tomorrow, could they follow the trail?
Safe, secure & sustainable
Protect the people, the data, the practice, and the planet. Safe — the well-being of everyone involved. Secure — zero-trust for their data, no PII in public models. Sustainable — workflows your team can maintain.
Are you protecting people’s safety, securing their data, and building something that lasts?
Score it before you ship it.
Before launching any AI-integrated study, score your workflow on each pillar from 1 to 5 — 1 is high-risk, 5 is high-integrity. Write the gaps down in plain language.
A score of 1 or 2 on R, T, or S means you redesign before you collect data — not after. Those three are where the damage is hardest to undo. Re-run the scorecard after launch to see whether the rigor held.
HEARTS aligns with the standards — and adds two they miss.
HEARTS translates the Responsible-AI consensus and re-centers it for researcher workflows. It aligns with the major frameworks on most dimensions, and adds two the compliance frameworks miss: Experience and Amplification.
| HEARTS | OECD (2024) | NIST AI RMF | EU AI Act | Academic Consensus |
|---|---|---|---|---|
| Human-Led | Human-centred values | Human role in accountability | Human oversight (Art. 14) | Human oversight & control |
| Experience-Focused | Well-being (partial) | — | — | UX-specific |
| Amplification | Inclusive growth (partial) | — | — | Human–AI complementarity |
| Rigorous & Responsible | Robustness; fairness | Valid & Reliable; Fair | Accuracy, robustness | Fairness & non-discrimination |
| Trustworthy & Transparent | Transparency; accountability | Accountable & Transparent | Transparency (Art. 50) | Transparency; accountability |
| Safe, Secure & Sustainable | Security & safety | Safe; Secure & Resilient | Safety; data governance | Privacy, security & well-being |
Built from experience, not a blank page.
While leading Responsible-AI research at Instacart, I found that OECD, NIST, the EU AI Act, and the academic literature all circle the same core — keep humans in control, be fair, be transparent, protect data, don’t harm people or the planet. But that consensus was written for governments and engineering orgs.
Not for the researcher staring at a transcript, wondering if the AI summary is trustworthy.
HEARTS translates it for the researcher doing the work — and adds the two things the compliance frameworks miss. First announced at the AI Club webinar on Sept 26, 2025, and pressure-tested since in enterprise workshops, the AI in UXR 101 course, and the AIxUXR community.
Questions.
What is the HEARTS Framework?
A six-part framework for evaluating whether an AI-assisted research workflow is rigorous, ethical, and trustworthy — not just faster.
Who created it?
Kaleb Loosbrock, a Staff/Principal-level UX researcher and AIxUXR consultant — based on research and experience integrating AI into his workflows at Instacart, and refined with the AIxUXR community.
How is it different from NIST or the EU AI Act?
HEARTS aligns with them, but it’s built for the working researcher — and adds two dimensions they don’t name: Experience and Amplification.
Does it replace Responsible AI standards?
No. It operationalizes the Responsible-AI consensus (OECD, NIST, EU AI Act) for research practice; it complements those standards.