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Related Experiment Video

Updated: May 3, 2026

An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

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Large Language Model (LLM) and Human Performance in Child Investigative Interviewing Question Formulation Tasks.

Liisa Järvilehto^1,2, Yongjie Sun^3,4, Nami Aiba⁵

¹Åbo Akademi University, Turku, Finland.

Behavioral Sciences & the Law

|December 9, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in question formulation for child interviews but struggle with dynamic, adaptive conversations. LLMs performed well in static tasks but less effectively in interactive scenarios compared to human professionals.

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Area of Science:

Cognitive Science
Artificial Intelligence
Forensic Psychology

Background:

Investigative interviewing of children is crucial for legal proceedings.
Assessing the capabilities of large language models (LLMs) in specialized communication tasks is an emerging area of research.
Understanding question formulation strategies is key to effective interviewing.

Purpose of the Study:

To compare the question formulation performance of LLMs and humans with varying expertise in child investigative interviewing.
To evaluate LLM performance in both static and dynamic interview simulation tasks.
To identify strengths and weaknesses of LLMs in interview contexts.

Main Methods:

Two tasks were used: a static Interview Excerpt Task and a dynamic Avatar Interviewing Task.
Participants included psychologists, naive individuals, students, and LLMs (GPT-4, Llama-2).
Performance was evaluated based on question types, including recommended, non-recommended, invitations, and option-posing questions.

Main Results:

In the dynamic task, LLMs used fewer recommended questions and more non-recommended questions than human professionals.
In the static task, GPT-4 outperformed psychologists in using invitations and avoiding option-posing questions.
LLMs demonstrated strong question formulation in controlled settings but faltered in adaptive dialogue.

Conclusions:

LLMs possess potential for specific question formulation aspects in child interviews, particularly in static scenarios.
LLMs require further development to effectively handle the nuances of adaptive, real-time dialogue in investigative interviews.
Human expertise remains critical for navigating complex, dynamic interview situations with children.