In Frontiers in psychology ; h5-index 92.0
Previous research has shown that simulated child sexual abuse (CSA) interview training using avatars paired with feedback and modeling improves interview quality. However, to make this approach scalable, the classification of interviewer questions needs to be automated. We tested an automated question classification system for these avatar interviews while also providing automated interventions (feedback and modeling) to improve interview quality. Forty-two professionals conducted two simulated CSA interviews online and were randomly provided with no intervention, feedback, or modeling after the first interview. Feedback consisted of the outcome of the alleged case and comments on the quality of the interviewer's questions. Modeling consisted of learning points and videos illustrating good and bad questioning methods. The total percentage of agreement in question coding between human operators and the automated classification was 72% for the main categories (recommended vs. not recommended) and 52% when 11 subcategories were considered. The intervention groups improved from first to second interview while this was not the case in the no intervention group (intervention x time: p = 0.007, ηp 2 = 0.28). Automated question classification worked well for classifying the interviewers' questions allowing interventions to improve interview quality.
Haginoya Shumpei, Ibe Tatsuro, Yamamoto Shota, Yoshimoto Naruyo, Mizushi Hazuki, Santtila Pekka
2023
artificial intelligence, child sexual abuse, investigative interviewing, serious game, simulation training
