# Voices of Innovation: Reflective Report on Integrating Artificial Intelligence–Simulated Mental Health Patient Scenarios Into Undergraduate Nursing Education in the United Arab Emirates

## TL;DR

This paper explores using AI-simulated patients to teach mental health nursing in the UAE when real clinical placements are limited.

## Contribution

The paper introduces the first-year integration of AI-simulated patient scenarios into undergraduate nursing education in the UAE.

## Key findings

- AI simulations provided a standardized and safe environment for students to practice mental health nursing skills.
- Implementation challenges included technical reliability and faculty preparation.
- The initiative supports therapeutic communication and clinical reasoning in resource-limited settings.

## Abstract

Limited clinical placements for mental health courses in the United Arab Emirates have made it difficult to provide consistent experiential learning for undergraduate nursing students. As a result, nurse educators are considering technology-enabled learning approaches to deliver clinical skills training. This Viewpoint presents a reflective, theory-informed account of the first-year integration of an artificial intelligence (AI)–enabled, voice-interactive simulated patient into an undergraduate mental health nursing practicum. Grounded in Kolb’s experiential learning cycle and aligned with established simulation best practices, the initiative was designed to support therapeutic communication, psychiatric assessment, and clinical reasoning through structured prebriefing, immersive interaction, and guided debriefing. The paper describes the educational rationale, scenario development, implementation processes, and contextual challenges encountered during real-world deployment across university and clinical environments. AI-supported simulations offered a standardized and psychologically safe context for students to engage with complex psychiatric scenarios, particularly when direct patient interaction is constrained. We discuss operational insights related to technical reliability, environmental requirements, faculty preparation, and assessment integration alongside considerations for scalability and sustainability in resource-limited settings. While AI-supported objective structured clinical examinations have been incorporated to support assessment consistency, formal psychometric validation and outcome comparisons have not been undertaken at this stage. By sharing lessons learned from early implementation, this Viewpoint contributes practical insights for nursing educators facing similar structural constraints. AI-enabled simulation is presented as a strategic complement to, rather than a replacement for, traditional clinical placements, with future empirical research needed to evaluate educational outcomes and long-term impact.