Enhancing Learning in Graduate Nursing Education Through a Co‐Designed AI Virtual Tutor: A Mixed‐Methods Evaluation

ABSTRACT

Background

Large language model tools are increasingly used in higher education, offering opportunities to support self-directed learning. In nursing education, course-specific AI virtual tutors may provide contextualised support while addressing concerns about content accuracy and alignment; yet empirical evidence remains limited.

Objective

This study evaluated the use and perceived impact of a co-designed AI-powered virtual tutor embedded in a graduate-level Master of Nursing (MN) course. We explored how students used the tutor, their perceptions of benefits and limitations, and its influence on learning and engagement.

Methods

A pilot study using a mixed-methods explanatory sequential design was employed. The tutor was trained on course-specific materials and integrated into the institutional learning management system. Data included anonymised usage logs and user interactions coded using Bloom's Taxonomy of Educational Objectives, post-course surveys assessing AI self-efficacy, usability, and learning impact, and semi-structured interviews with students and teaching assistants (TAs). Quantitative and qualitative strands were integrated through a joint display.

Results

A total of 651 interactions by individuals within a group of ~120 MN students were logged. Interactions peaked in evenings and around assignment deadlines. Most interactions reflected lower-order education processes, with more application and analysis later in the course. Eleven participants completed surveys; students reported high AI self-efficacy and moderate tutor use. Perceived usefulness was mixed, but most reported the tutor enhanced both lower- and higher-level learning and recommended its future use. Interviews revealed that students valued the tutor's immediacy and course-specific accuracy, while TAs noted efficiency gains. Reported challenges included usability issues, scope limitations, privacy concerns, and risk of over-reliance on the tool.

Conclusions

A co-designed AI virtual tutor was feasible and valued for contextual relevance, though perceived usefulness was variable. Findings support responsible, pedagogically integrated use of AI tutors in graduate nursing education.