Deception detection in educational AI: challenges for Japanese middle school students in interacting with generative AI robots - Salem, Ahmed; Sumi, Kaoru (Frontiers in Artificial Intelligence, 2024)
Educational materials that utilize generative AI (e.g., ChatGPT) have been developed, thus, allowing students to learn through conversations with robots or agents. However, if these artificial entities provide incorrect information (hallucinating), it could lead to confusion among students. To investigate whether students can detect lies from these artificial entities, we conducted an experiment using the social robot Furhat and we make it engage in various types of deceptive interactions. Twenty-two Japanese middle school students participated in ten teaching sessions with Furhat using a human and an anime facial appearances while employing different types of deception: Lying, Paltering, Pandering, and Bullshit. The results revealed that the majority of students were deceived by those lies. Additionally, the robot's facial appearance (i.e., social agency) affected both the learning effectiveness and the likelihood of being deceived. We conclude that an anime robot face is recommended to be used as it excelled in learning effectiveness as it attracts students attention. An anime face also provided protection against deceptive techniques due to its low social agency which leads to ineffectiveness in persuasion and deception. This study underscores the importance of preparing AI-based educational tools and scripts carefully to prevent the dissemination of false information produced through generative AI hallucinations to students.