If These Walls Could Talk: Critical Play with Large Language Models in Museums
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Abstract (arXiv)
Large Language Models (LLMs) are increasingly being used in museums to as role playing chatbots which let visitors talk to simulated versions of people and artefacts from the past. While such installations can be playful and engaging, they are also problematic because LLMs cannot be trusted to speak truthfully. I identify a fundamental dilemma for the use of LLMs in museum chatbots: LLMs cannot be trusted to tell the truth, and efforts to make them more reliable may ruin that which is attractive about the bots in the first place - their ability to engage in life-like conversation. In response, I propose designing for critical play with LLM-based bots: Designing for playful interactions with bots that are unreliable but still able to represent the past in an adequate and engaging manner - as fictional characters representing historical narratives, styles of discourse, diverse perspectives, humor and satire.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 50 references that are in the map. First posted on arXiv 14 Jun 2026.
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9 of 50 references.
References · 9
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
- 2023Role-Play with Large Language Modelscited by 290
- 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 130
- 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 105
- 2024AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollockscited by 12
- 2024Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art Explorationcited by 4
- 2022Sensitive Pictures: Emotional Interpretation in the Museumcited by 2
- 2025Using a Large Language Model as Design Material for an Interactive Museum Installationcited by 2
- 2026"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Appscited by 1
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