Oppositional Simulation as a Method for Expert Witness Skill Acquisition

Preparing early-career forensic human factors experts for sworn testimony typically requires resource-intensive mock trials and mock depositions. To supplement this training, the authors present a structured simulation method using a commercially available large language model (LLM), helping experts refine their communication skills for adversarial testimony. This simulation is not meant to hone human factors expertise; instead, it may sharpen the linguistic and affective mechanisms that underlie successful sworn testimony. The authors present a fictional case study (based on real events) in which a human factors expert is hired to opine on product misuse versus design defect. The authors conduct a live deposition simulation for this product liability case, with the LLM playing the role of opposing counsel. Ratings of ecological validity from practicing forensic experts will be presented to judge the fidelity of the tool. This proof of concept offers a low-cost, low-stakes training tool to prepare scientists for litigation.

Authors

Institutions

Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2026-09-06
DOI
https://doi.org/10.1177/10711813261485917
Primary Topic
Digital and Cyber Forensics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Oppositional Simulation as a Method for Expert Witness Skill Acquisition

C E Cohen, Joseph Cohen
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Digital and Cyber Forensics
article

Oppositional Simulation as a Method for Expert Witness Skill Acquisition

C E Cohen, Joseph Cohen
article en

Abstract

Preparing early-career forensic human factors experts for sworn testimony typically requires resource-intensive mock trials and mock depositions. To supplement this training, the authors present a structured simulation method using a commercially available large language model (LLM), helping experts refine their communication skills for adversarial testimony. This simulation is not meant to hone human factors expertise; instead, it may sharpen the linguistic and affective mechanisms that underlie successful sworn testimony. The authors present a fictional case study (based on real events) in which a human factors expert is hired to opine on product misuse versus design defect. The authors conduct a live deposition simulation for this product liability case, with the LLM playing the role of opposing counsel. Ratings of ecological validity from practicing forensic experts will be presented to judge the fidelity of the tool. This proof of concept offers a low-cost, low-stakes training tool to prepare scientists for litigation.

Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Engineering Research & Analysis (United States) (US)
Quality Education
Openalex Percentile: Top 3%
Digital and Cyber Forensics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.