Gunn-Astrid Baugerud, Miriam S Johnson, Rachel Dianiska, Ragnhild K Røed, Martine B Powell, Michael E Lamb, Syed Zohaib Hassan, Saaed S Sabet, Steven Hicks, Pegah Salehi, Michael A Riegler, Pål Halvorsen, Jodi Quas
{"title":"在儿童权益维护中心使用基于人工智能的化身进行访谈员培训:概念验证。","authors":"Gunn-Astrid Baugerud, Miriam S Johnson, Rachel Dianiska, Ragnhild K Røed, Martine B Powell, Michael E Lamb, Syed Zohaib Hassan, Saaed S Sabet, Steven Hicks, Pegah Salehi, Michael A Riegler, Pål Halvorsen, Jodi Quas","doi":"10.1177/10775595241263017","DOIUrl":null,"url":null,"abstract":"<p><p>This proof-of- concept study focused on interviewers' behaviors and perceptions when interacting with a dynamic AI child avatar alleging abuse. Professionals (<i>N</i> = 68) took part in a virtual reality (VR) study in which they questioned an avatar presented as a child victim of sexual or physical abuse. Of interest was how interviewers questioned the avatar, how productive the child avatar was in response, and how interviewers perceived the VR interaction. Findings suggested alignment between interviewers' virtual questioning approaches and interviewers' typical questioning behavior in real-world investigative interviews, with a diverse range of questions used to elicit disclosures from the child avatar. The avatar responded to most question types as children typically do, though more nuanced programming of the avatar's productivity in response to complex question types is needed. Participants rated the avatar positively and felt comfortable with the VR experience. Results underscored the potential of AI-based interview training as a scalable, standardized alternative to traditional methods.</p>","PeriodicalId":48052,"journal":{"name":"Child Maltreatment","volume":" ","pages":"10775595241263017"},"PeriodicalIF":4.5000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using an AI-based avatar for interviewer training at Children's Advocacy Centers: Proof of Concept.\",\"authors\":\"Gunn-Astrid Baugerud, Miriam S Johnson, Rachel Dianiska, Ragnhild K Røed, Martine B Powell, Michael E Lamb, Syed Zohaib Hassan, Saaed S Sabet, Steven Hicks, Pegah Salehi, Michael A Riegler, Pål Halvorsen, Jodi Quas\",\"doi\":\"10.1177/10775595241263017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This proof-of- concept study focused on interviewers' behaviors and perceptions when interacting with a dynamic AI child avatar alleging abuse. Professionals (<i>N</i> = 68) took part in a virtual reality (VR) study in which they questioned an avatar presented as a child victim of sexual or physical abuse. Of interest was how interviewers questioned the avatar, how productive the child avatar was in response, and how interviewers perceived the VR interaction. Findings suggested alignment between interviewers' virtual questioning approaches and interviewers' typical questioning behavior in real-world investigative interviews, with a diverse range of questions used to elicit disclosures from the child avatar. The avatar responded to most question types as children typically do, though more nuanced programming of the avatar's productivity in response to complex question types is needed. Participants rated the avatar positively and felt comfortable with the VR experience. Results underscored the potential of AI-based interview training as a scalable, standardized alternative to traditional methods.</p>\",\"PeriodicalId\":48052,\"journal\":{\"name\":\"Child Maltreatment\",\"volume\":\" \",\"pages\":\"10775595241263017\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Child Maltreatment\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1177/10775595241263017\",\"RegionNum\":2,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FAMILY STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Child Maltreatment","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1177/10775595241263017","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FAMILY STUDIES","Score":null,"Total":0}
Using an AI-based avatar for interviewer training at Children's Advocacy Centers: Proof of Concept.
This proof-of- concept study focused on interviewers' behaviors and perceptions when interacting with a dynamic AI child avatar alleging abuse. Professionals (N = 68) took part in a virtual reality (VR) study in which they questioned an avatar presented as a child victim of sexual or physical abuse. Of interest was how interviewers questioned the avatar, how productive the child avatar was in response, and how interviewers perceived the VR interaction. Findings suggested alignment between interviewers' virtual questioning approaches and interviewers' typical questioning behavior in real-world investigative interviews, with a diverse range of questions used to elicit disclosures from the child avatar. The avatar responded to most question types as children typically do, though more nuanced programming of the avatar's productivity in response to complex question types is needed. Participants rated the avatar positively and felt comfortable with the VR experience. Results underscored the potential of AI-based interview training as a scalable, standardized alternative to traditional methods.
期刊介绍:
Child Maltreatment is the official journal of the American Professional Society on the Abuse of Children (APSAC), the nation"s largest interdisciplinary child maltreatment professional organization. Child Maltreatment"s object is to foster professional excellence in the field of child abuse and neglect by reporting current and at-issue scientific information and technical innovations in a form immediately useful to practitioners and researchers from mental health, child protection, law, law enforcement, medicine, nursing, and allied disciplines. Child Maltreatment emphasizes perspectives with a rigorous scientific base that are relevant to policy, practice, and research.