Blended AI Course Boosts Nursing Students’ AI Literacy in Quasi-Experimental Trial

Nursing undergraduates in China who completed a 10-week course built around artificial intelligence and generative AI showed substantial gains in AI literacy and more favorable attitudes toward AI, according to a quasi-experimental study published in BMC Nursing. The intervention also appeared to protect students' autonomous learning competency, which declined among classmates who received standard instruction. The study, conducted at a university in southwestern Zhejiang, enrolled 142 undergraduate nursing students.

Seventy-two participated in the AI and generative AI-assisted blended learning course, while 70 received standard instruction. The blended format combined 12 hours of self-paced online learning with 6 hours of hands-on workshops, pairing conceptual study of AI with practical application and guidance on responsible AI use. Researchers assessed three outcomes before and immediately after the intervention using validated instruments: attitudes toward AI, AI literacy, and autonomous learning competency.

Because students were not randomly assigned, the team used analysis of covariance to compare post-test scores between groups, adjusting for the corresponding baseline score, previous AI training experience, frequency of AI use, and digital literacy. The clearest effect appeared in AI literacy. In the intervention group, mean AI literacy scores rose from 55.67 ± 9.84 to 65.01 ± 8.81, a change the authors reported as statistically significant (P < 0.001).

Scores in the control group did not change significantly. After adjustment for covariates, the intervention group scored on average 11.01 points higher on post-test AI literacy than the control group (95% CI 8.55–13.47; P < 0.001). Attitudes toward AI also improved in the course participants, moving from a mean of 48.07 ± 6.77 at baseline to 51.72 ± 9.90 after the intervention (P = 0.049).

Again, the control group showed no significant change. The adjusted between-group difference favored the intervention group by 4.60 points (95% CI 1.98–7.21; P < 0.001), suggesting the course shifted students' dispositions toward AI as well as their measured skills. Autonomous learning competency followed a different pattern.

In the intervention group, scores changed little over the 10 weeks (P = 0.578), remaining essentially stable. In the control group, however, autonomous learning competency declined significantly (P < 0.001). The adjusted post-test difference between groups was 10.94 points in favor of the intervention group (95% CI 7.44–14.43; P < 0.001).

The authors interpret these results as evidence that structured AI education can be integrated into undergraduate nursing programs with measurable benefit. The course design deliberately combined three elements: conceptual learning about AI, practical application through workshops, and instruction on responsible AI use. That combination, the researchers suggest, may explain why literacy and attitudes improved together while self-directed learning did not erode as it did among students without the course.

The findings arrive as healthcare systems increasingly adopt AI tools, raising the question of what competencies nursing graduates need before they enter clinical practice. The authors note that structured AI education remains limited in nursing programs and that evidence on the effects of AI-focused teaching interventions has been scarce, which motivated the study. Several limitations temper the conclusions.

The study used a quasi-experimental design rather than randomization, so unmeasured differences between the two student groups could contribute to the observed gaps. Outcomes were measured immediately after the 10-week course, leaving open whether the gains in AI literacy and attitudes persist over time. The single-university setting in southwestern Zhejiang also limits how far the results generalize to other institutions and countries.

The authors describe the intervention as associated with the improved outcomes rather than definitively causing them, although the covariate-adjusted analysis strengthens the comparison.

Subject of Research: A quasi-experimental study of an AI and generative AI-assisted blended learning course for undergraduate nursing students in China

Article Title: An AI/generative AI-assisted blended learning intervention for nursing undergraduates: a quasi-experimental study of attitudes toward AI, AI literacy, and autonomous learning competency

Article References: Lan, X., Luo, Y., Lu, S., Wu, Y., Gao, X., Xu, L., Tao, Q., Li, M., & Chen, M. (2026). An AI/generative AI-assisted blended learning intervention for nursing undergraduates: a quasi-experimental study of attitudes toward AI, AI literacy, and autonomous learning competency. BMC Nursing. https://doi.org/10.1186/s12912-026-05512-z

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05512-z

Keywords: nursing education, artificial intelligence, generative AI, AI literacy, blended learning, quasi-experimental study, autonomous learning, undergraduate students, healthcare education, China, BMC Nursing, generative