An AI-Facilitated Virtual Teaching Assistant for Undergraduate Nursing Honors Research: An Embedded Mixed-Methods Evaluation.
TL;DR
It is suggested that INSPIRE-AI has the potential to support research self-efficacy through structured scaffolding; however, this interpretation should be considered alongside the broader educational support that students received throughout the honors program.
Abstract
Background
Artificial intelligence (AI) is increasingly integrated into higher education. However, evidence on theory-informed AI interventions supporting nursing research training remains limited.
Purpose
To evaluate an AI-facilitated teaching assistant (INSPIRE-AI) on final-year undergraduate nursing honors students' research self-efficacy, motivation, and research interest, and explore students' experiences of using INSPIRE-AI.
Methods
An embedded mixed-methods study comprising a one-group quasi-experimental pretest/posttest design with postintervention semistructured qualitative interviews. Nursing students received access to INSPIRE-AI throughout the honors year. Quantitative survey data (N = 146) were analyzed using paired t-tests and repeated-measures general linear models.
Results
Research self-efficacy improved significantly (P < .001). Students reported that INSPIRE-AI supported structured thinking and reduced uncertainty, though engagement varied due to trust concerns, perceived surveillance, and preference for familiar AI tools.
Conclusions
Together, these findings suggest that INSPIRE-AI has the potential to support research self-efficacy through structured scaffolding; however, this interpretation should be considered alongside the broader educational support that students received throughout the honors program.