AI-Powered Tools to Enhance Critical Thinking and Clinical Reasoning in Nursing Education: A Scoping Review with Implications for Low- and Middle-Income Countries

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Authors: Sokha Yem, Sovannra Yim, Sokunthea Kem, Sreypeov Tun, Vann Lida
Cited by: 1

Abstract

Artificial Intelligence (AI) is increasingly integrated into educational systems worldwide, offering innovative approaches to improve learning outcomes in health professions education. In nursing, AI-powered tools such as ChatGPT, intelligent tutoring systems (ITS), virtual patient simulation platforms, and automated assessment systems have shown potential to strengthen critical thinking (CT) and clinical reasoning (CR), which are essential competencies for safe and evidence-based practice. However, their scope, effectiveness, and applicability remain underexplored, particularly in low- and middle-income countries (LMICs), where limited digital infrastructure, faculty capacity gaps, and resource constraints hinder implementation. This scoping review aimed to map existing evidence on AI-powered tools used in nursing and health professions education to enhance CT and CR, identify implementation gaps and barriers, and derive context-specific implications for LMICs, with particular attention to Cambodia. Following the Arksey and O’Malley framework, refined by Levac et al. and Peters et al., and guided by PRISMA-ScR, a systematic search was conducted in PubMed, Scopus, and CINAHL for peer-reviewed publications from January 2015 to October 2024. Forty-two studies from 15 countries were included. Four categories of AI tools were identified: conversational agents (n = 14), intelligent tutoring systems (n = 11), virtual patient simulations (n = 10), and automated assessment systems (n = 7). Most studies reported positive outcomes, with seven of eight RCTs showing significant CT improvement and virtual simulations consistently enhancing CR. Nevertheless, infrastructure limitations, faculty unpreparedness, ethical concerns, and licensing costs remain major barriers. Sustainable AI integration in LMIC nursing education requires context-sensitive infrastructure, capacity-building, governance, and stronger longitudinal research.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Simulation-Based Education in Healthcare Intelligent Tutoring Systems and Adaptive Learning

Publication details

Journal / source
Journal of Applied Artificial Intelligence in Education
Publisher
PT. Academic Bright Collaboration
Pages
107–118
Publication date
29 April 2026
Language
EN