{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T20:49:23Z","timestamp":1763412563615,"version":"3.45.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study investigated how users perceive the concept of \u201cknowledge\u201d in robots and how this perception differs from their views on human knowledge. Based on the Justified True Belief (JTB) framework for knowledge, I conducted three experiments to verify whether users apply the same standards to a robot\u2019s knowledge as they do to human knowledge. In Experiment 1, I demonstrated that the conditions under which users attribute knowledge differ between humans and robots through an experiment where participants read a Gettier-type scenario and answered questions. Experiment 2 examined the threshold at which information is perceived as \u201cknowledge,\u201d revealing that users tend to demand more direct verification from robots than from humans. Experiment 3 examined the impact of beliefs about the robot\u2019s knowledge, identified in Experiments 1 and 2, on users\u2019 moral judgments. The results showed that users attributed less responsibility to the robot as an accomplice to the crime than to a human accomplice. These results suggest that users perceive robots\u2019 knowledge as inflexible, requiring direct perception, and fundamentally different from human knowledge and reasoning. These findings provide important insights for the design of human\u2013robot interaction (HRI) systems. Understanding users\u2019 perceptions of robot knowledge may be essential for building trust in future human\u2013robot collaboration.<\/jats:p>","DOI":"10.1007\/s44163-025-00534-z","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T18:48:15Z","timestamp":1763405295000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A JTB based study of epistemic attribution to robots as \u201cKnowledge\u201d"],"prefix":"10.1007","volume":"5","author":[{"given":"Tetsuya","family":"Matsui","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"issue":"21","key":"534_CR1","doi-asserted-by":"publisher","first-page":"5954","DOI":"10.1126\/scirobotics.aat5954","volume":"3","author":"T Belpaeme","year":"2018","unstructured":"Belpaeme T, Kennedy J, Ramachandran A, Scassellati B, Tanaka F. Social robots for education: a review. 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