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  4. Public defence: Ragnhild Klingenberg Røed
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This public defense has already taken place.

Public defence: Ragnhild Klingenberg Røed

Ragnhild Klingenberg Røed will defend her thesis for the PhD in Social Science, specialization Social Work and Social Policy with "Developing an AI-driven Child Avatar Chatbot to Improve Interviewing Skills for Professionals Investigating Suspected Child Abuse and Neglect".

Date
28. april 2025
Organizer
  • Faculty of Social Sciences (SAM)
  • Other
Type
Previous public defences

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The trial lecture lasts from 10:00-10:45. The title is: "The distortion of children's accounts in investigations of child abuse: Possible risk factors and how to mitigate them".

The candidate will defend her thesis at 12:00. 

Streaming link for remote participants is above.

  • Webinar ID: 643 7248 9795 
  • Passcode: 2025 

The committee is:

  • Professor of Practice Julia Korkman, Åbo Akademi University
  • Professor Timothy John Brennen, University of Oslo, UiO
  • Associate Professor Dagny Adriaenssen Johannessen, Oslo Metropolitan University, OsloMet

Leader of the public defence is PhD coordinator Professor Amy Østertun Geirdal, Faculty of Social Sciences, Oslo Metropolitan University.

  • Main supervisor is Professor Gunn Astrid Baugerud, Department of Social Work, Child Welfare and Social Policy, OsloMet.
  • Co-supervisor is Associate Professor Miriam Sinkerud Johnson, Department of Behavioural Science, OsloMet.
  • Co-supervisor is Professor Pål Halvorsen, SimulaMet.

The dissertation is available in ODA Open Digital Archive.

Summary

This thesis addresses the development of an AI-driven child avatar for realistic skills training in child interviewing. High-quality interviewing is crucial in cases of child abuse. 

Two studies analyzing real-life interviews with young children, allegedly subjected to abuse, in child protection services and police investigations confirmed international findings: trained professionals rarely used recommended open-ended questions. Advances in AI have increased interest in avatar-based training tools. An evaluation of frontline professionals’ interaction with the mechanical LiveSim avatar for question training showed high user engagement and confirmed usability. A text-based AI child avatar was developed, and effects of training were examined. The study found improved use of open-ended questions, especially when paired with automated, individualized feedback. Feedback function aligned with expert assessments.

This dissertation supports further development of AI-driven conversation-generating avatars with integrated feedback for realistic and cost-effective interview training. Strengthening interview skills will enhance information quality from children in vulnerable situations.