Evaluations of an artificial intelligence instructor's voice: Social Identity Theory in human-robot interactions

Chad Edwards, Autumn Edwards, Brett Stoll, Xialing Lin, Noelle Massey

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

This study employs the Computers are Social Actors (CASA) paradigm to extend the predictions of Social Identity Theory (SIT) to human-robot interaction (HRI) in the context of instructional communication. SIT posits that individuals gain a sense of personal worth from the groups with which they identify. Previous research has demonstrated that age group identification is meaningful to individuals’ self-concepts. Results demonstrated that higher age identified students rated the older A.I. voice instructor (representing an out-group member) higher for credibility and social presence and reported more motivation to learn than those students with low age identification. Implications are discussed for SIT and design features of computerized voices.

Original languageEnglish (US)
Pages (from-to)357-362
Number of pages6
JournalComputers in Human Behavior
Volume90
DOIs
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

Fingerprint Dive into the research topics of 'Evaluations of an artificial intelligence instructor's voice: Social Identity Theory in human-robot interactions'. Together they form a unique fingerprint.

Cite this