Sage Journals: Discover world-class research

Abstract

Law enforcement officers often respond to behavioral health crises, where individuals with mental health conditions face an 11.6-fold higher risk of police use of force. While extended reality (XR) offers a promising training solution for de-escalation, current systems lack the necessary realism for developing skills under pressure. This study introduces an XR training prototype powered by an AI-driven Non-Playable Character (NPC) to address these shortcomings. A two-phase evaluation assessed the system's usability and its training efficacy with officers. Results showed high user engagement, a strong sense of virtual presence, and an optimized cognitive load for learning. Most significantly, participating officers demonstrated a 50% reduction in race and gender bias according to IAT scores. This highlights the potential of AI-enhanced XR to improve police decision-making and mitigate bias in high-stakes encounters.

Keywords

artificial intelligence AR/VR/MR education and learning extended reality machine learning user-centered design

Get full access to this article

View all access options for this article.

References

Witmer

B. G.

Singer

M. J.

(1998). Measuring presence in virtual environments: A presence questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225–240. https://doi.org/10.1162/105474698565686

Greenwald

A. G.

McGhee

D. E.

Schwartz

J. L.

(1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464.

Harmon-Jones

Bastian

Harmon-Jones

(2016). The discrete emotions questionnaire: A new tool for measuring state self-reported emotions. PLoS One, 11, e0159915.

Sweller

(1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5.

Kennedy

R. S.

Lane

N. E.

Berbaum

K. S.

Lilienthal

M. G.

(1993). Simulator Sickness Questionnaire (SSQ) [Database record]. APA PsycTests. https://doi.org/10.1037/t04669-000

Laniyonu

Goff

P. A.

(2021). Measuring disparities in police use of force and injury among persons with serious mental illness. BMC Psychiatry, 21(1), 500. https://doi.org/10.1186/s12888-021-03510-w

Lavoie

Álvarez

Baker

Kohl

(2023). Training police to de-escalate mental health crisis situations: Comparing virtual reality and live-action scenario-based approaches. Policing: A Journal of Policy and Practice, 17, paad069.

Morabito

M. S.

Socia

Wik

Fisher

W. H.

(2017). The nature and extent of police use of force in encounters with people with behavioral health disorders. International Journal of Law and Psychiatry, 50, 31–37. https://doi.org/10.1016/j.ijlp.2016.10.001

Muñoz

J. E.

Lavoie

J. A.

Pope

A. T.

(2024). Psychophysiological insights and user perspectives: enhancing police de-escalation skills through full-body VR training. Frontiers in Psychology, 15, 1390677. https://doi.org/10.3389/fpsyg.2024.1390677

10.

O’Brien

(2016). Theoretical perspectives on user engagement. In: O’Brien

Cairns

(Eds.), Why engagement matters (pp. 1–26). Springer. https://doi.org/10.1007/978-3-319-27446-1_1

11.

Skarbez

Smith

Whitton

M. C.

(2021). Revisiting Milgram and Kishino’s reality-virtuality continuum. Frontiers in Virtual Reality, 2, 647997.

Beyond Human Actors: Leveraging AI for Enhanced Public Safety XR Training

Abstract

Keywords

Get full access to this article

References