Enhancing nursing education: An AI-powered Chatbots for fostering engagement and higher-order thinking skills

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Abstract
Background AI-powered chatbots are increasingly integrated into educational settings, offering opportunities to enhance student engagement and higher-order thinking skills (HOTS). However, limited research exists on their role in nursing education, especially in China. This study aimed to explore how AI-powered chatbots impact nursing students’ engagement and the development of HOTS, mediated by feedback quality (FBQ) and self-regulated learning (SRL). Methods A cross-sectional, quantitative research design was employed to investigate the interplay between perceived usefulness (PUC), ease of use (EoU), engagement (ENG), FBQ, SRL, and HOTS. 470 nursing students from different academic years participated in the study. Data were collected using a structured survey measuring six key constructs. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to evaluate the direct and indirect relationships within the conceptual framework. Results PUC and EoU significantly influenced ENG, which, in turn, strongly mediated the effects of FBQ and SRL on HOTS. ENG had a substantial impact on FBQ (β = 0.852) and SRL (β = 0.892), while the combined indirect effect of FBQ and SRL on HOTS (β = 0.798) demonstrated the critical role of these mediators. The study also confirmed that intuitive chatbot design and high-quality, timely feedback are essential for fostering cognitive skills. Conclusions AI-powered chatbots show promise in enhancing engagement and supporting the development of higher-order thinking skills in nursing education. The findings emphasize the need for scalable, user-friendly chatbot systems tailored to educational contexts. Future research should focus on advanced feedback algorithms, long-term impacts on clinical competency, and scalability in diverse learning environments.

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