Two Truths and One Lie
Two Truths and a Lie is a light personal-disclosure game where each person shares two facts and one false statement, then the group listens closely and guesses the lie.
How to run it
- Explain the rules of the activity and the goal of getting to know each other in a fun way.
- Ask each participant to think of three statements about themselves:
- Encourage participants to be creative and share interesting or unexpected facts.
- Each participant shares their three statements with the group without revealing which one is the lie.
- The rest of the group discusses and votes on which statement they believe is the lie.
- The participant reveals the correct answer and can share a brief story about the true statements.
- Continue until all participants have had their turn.
Run this exercise live
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Materials:
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Prompts to use +
Each participant prepares three statements about themselves: two true and one false.
The group asks light clarifying questions, then guesses which statement is the lie.
After the reveal, invite a short follow-up story behind one true statement.
Explain the rules of the activity and the goal of getting to know each other in a fun way.
Ask each participant to think of three statements about themselves:
Encourage participants to be creative and share interesting or unexpected facts.
Run this with your AI assistant +
Works with Claude, ChatGPT, Gemini, or your own local model: paste this prompt and your AI will co-facilitate this exercise with you.
Adapt this exercise for my exact room. Exercise: Two Truths and One Lie. Readiness: Publishable Draft. Core moves: Each participant prepares three statements about themselves: two true and one false.; The group asks light clarifying questions, then guesses which statement is the lie.; After the reveal, invite a short follow-up story behind one true statement.; Explain the rules of the activity and the goal of getting to know each other in a fun way.. Return a concise facilitator script with timing, wording, safety notes, and remote/in-person adjustments.
Origin unverified
- Where we observed it: The strongest current observations come from Participatory Methods; aggregators are listed as additional observations, not origin proof. (1 source observed, including Participatory Methods.)
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