aceModerate evidence
Published recordScienceProblem Space
More
AI talking science: Experimental studies on the perception of AI-based chatbots as sources of science-based information
- Truvace record
- TRV-2026-2596raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Randomized controlled trial
- Sources
- 1 sourcePeer-reviewed
- sha256
- 997856bf23e7…check it
AI-based chatbots offer new opportunities for communicating science-based information, but often fall short of established standards. We conducted two pre-registered experiments examining user perceptions of an AI-based chatbot providing information on nanoparticles in sunscreen. Study one (N = 508) tested whether a disclaimer about the chatbot's uncertain training data affected perceived source trustworthiness and information credibility. The results showed no significant effect of the disclaimer; perceptions w…
The problem
In an online experiment with 1,059 German adults, the same sunscreen-nanoparticle answers were rated less trustworthy (5.11 vs 5.64 out of 7) and less credible (4.96 vs 5.38) when attributed to an AI chatbot than to a scientist.
What this doesn’t fix
Participants only viewed a one-time scripted chat rather than using a real chatbot, the topic carried low perceived risk, and the online panel sample was German-speaking, relatively young and well educated.
Sources
1 on file- Journal of Science Communicationdoi:10.22323/170720260228062648
Cite this record
v1Truvace Impact Record TRV-2026-2596, v1: “AI talking science: Experimental studies on the perception of AI-based chatbots as sources of science-based information.” Truvace, 2026-11-12. /record/TRV-2026-2596 (accessed on the day you copy it). sha256 997856bf23e7d9aa…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
Certified into the record
Check this record hasn’t been altered
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-2596 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.