ai empathy lab
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AI Empathy Resources and Definitions

Explore research-based explanations of AI empathy, empathic AI, emotional AI, voice agents, AI companions, and human-AI relationships. These resources translate current evidence, research methods, and ethical questions into accessible guidance for researchers, practitioners, policymakers, and the public.

Explainers

DefinitionWhat is AI empathy?A machine’s ability to recognize how someone feels, understand why, and respond in a way that helps.TerminologyWhat separates empathic AI, emotional AI, and affective computing?One is a research field, one reads emotion, and one responds to it. Where they overlap, and where the law tells them apart.MethodsHow do you measure empathy in a machine?Researchers score what the system says, ask people what they perceived, and track what they do next.Well-beingAre AI companions good for us?They can ease loneliness in the moment. Heavier use tells a different story, and design decides much of it.EthicsWhen does AI persuasion become manipulation?Persuasion works through reasons people can weigh. Manipulation works around them. Five questions that tell the two apart.

Built for our experiments. Open to other research groups.

Every experiment we publish runs on infrastructure we built ourselves. Participants talk to a live, working AI agent, and every message and every emotion measurement is preserved for analysis. Research integrity comes first, so nothing about a live study's condition changes without a researcher deciding it.

See how it works Ask about access
  1. 01Six AI providers behind one identical participant interface, so the service is never a confound.
  2. 02Voice prosody scores persisted with every message, as research data rather than telemetry.
  3. 03Emotion-triggered experimental manipulation, with every trigger firing logged as data.
  4. 04Recruitment-aware participant IDs, concurrency caps with a fair waiting queue, and consent gates.
  5. 05Daily drift monitoring that reports provider changes instead of silently substituting models mid-study.

What you can run on it

  • Voice conversations

    Participants talk out loud with a live, empathic voice agent, in the same interface whichever AI service runs it.

  • AI interviewer

    A voice interviewer works through your questions, and a participant whose call drops can rejoin where they left off.

  • Text chat

    Participants type instead of talking, and every message is kept for analysis.

  • Multi-session studies

    Participants come back for a series of sessions, and the agent can remember what was said before.