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dc.contributor.authorRøed, Ragnhild Klingenberg
dc.contributor.authorBaugerud, Gunn Astrid
dc.contributor.authorZohaib Hassan, Syed
dc.contributor.authorShafiee Sabet, Saeed
dc.contributor.authorSalehi, Pegah
dc.contributor.authorPowell, Martine B.
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorSinkerud Johnson, Miriam
dc.date.accessioned2023-09-04T10:40:26Z
dc.date.available2023-09-04T10:40:26Z
dc.date.issued2023-07-13
dc.description.abstractTraining child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to trainees. In the iterative process, use of a chatbot avatar to test the language and conversation model is crucial. The model is fine-tuned with interview data and realistic scenarios. This study used a pre-post training design to assess the learning effects on questioning skills across four child interview sessions that involved training with a child avatar chatbot fine-tuned with interview data and realistic scenarios. Thirty university students from the areas of child welfare, social work, and psychology were divided into two groups; one group received direct feedback (n = 12), whereas the other received no feedback (n = 18). An automatic coding function in the language model identified the question types. Information on question types was provided as feedback in the direct feedback group only. The scenario included a 6-year-old girl being interviewed about alleged physical abuse. After the first interview session (baseline), all participants watched a video lecture on memory, witness psychology, and questioning before they conducted two additional interview sessions and completed a post-experience survey. One week later, they conducted a fourth interview and completed another postexperience survey. All chatbot transcripts were coded for interview quality. The language model’s automatic feedback function was found to be highly reliable in classifying question types, reflecting the substantial agreement among the raters [Cohen’s kappa (κ) = 0.80] in coding open-ended, cued recall, and closed questions. Participants who received direct feedback showed a significantly higher improvement in open-ended questioning than those in the non-feedback group, with a significant increase in the number of open-ended questions used between the baseline and each of the other three chat sessions. This study demonstrates that child avatar chatbot training improves interview quality with regard to recommended questioning, especially when combined with direct feedback on questioning.en_US
dc.identifier.citationRøed, Baugerud, Zohaib Hassan, Shafiee Sabet, Salehi, Powell, Riegler, Halvorsen, Sinkerud Johnson. Enhancing questioning skills through child avatar chatbot training with feedback. Frontiers in Psychology. 2023en_US
dc.identifier.cristinIDFRIDAID 2162198
dc.identifier.doi10.3389/fpsyg.2023.1198235
dc.identifier.issn1664-1078
dc.identifier.urihttps://hdl.handle.net/10037/30668
dc.language.isoengen_US
dc.publisherFrontiers Mediaen_US
dc.relation.journalFrontiers in Psychology
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleEnhancing questioning skills through child avatar chatbot training with feedbacken_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)