AI-assisted concept mapping enhances orthopaedic nursing students' critical thinking: A quasi-experimental study

Critical thinking (CT) is essential in orthopaedic nursing practice, where nurses must accurately assess fractures, identify complications, plan mobility strategies, and support postoperative recovery. Concept mapping (CMAP) can facilitate structured clinical reasoning, while artificial intelligence (AI) may further support conceptual organization and reflective learning processes. However, evidence regarding AI-assisted CMAP in orthopaedic nursing education remains limited.

To evaluate the effectiveness of an AI-assisted CMAP program in improving CT among undergraduate students enrolled in an orthopaedic nursing course.

A two-group quasi-experimental pretest-posttest design was employed. Participants were allocated to either an experimental group receiving AI-assisted CMAP using ChatGPT (OpenAI) during a structured workshop or a control group receiving standard instructional activities. CT was evaluated using a validated 12-item CMAP-based assessment tool administered before and after the intervention. Data were analyzed using mixed-design ANOVA and ANCOVA.

CT scores improved significantly over time, with greater improvement observed in the experimental group. Mean CT scores in the experimental group increased from 2.40 ± 0.50 at pretest to 2.97 ± 0.72 at posttest, whereas scores in the control group increased from 2.43 ± 0.68 to 2.57 ± 0.63. A significant interaction effect between time and group was identified (F(1,58) = 9.705, p = .003, partial η² = 0.143). Students in the experimental group also reported high satisfaction with the AI-assisted CMAP program.

AI-assisted CMAP may support CT development and structured clinical reasoning in orthopaedic nursing education. Integrating AI-assisted reflective support into concept-mapping activities may provide an effective pedagogical approach for helping nursing students organize and integrate complex clinical information.