The Association Between AI Dependence, AI Literacy, and Critical Thinking Ability Among Nursing Interns: A Multicenter Cross-Sectional Study

Artificial intelligence (AI) has rapidly integrated into higher education, fundamentally altering how students acquire knowledge and complete academic tasks. In the field of nursing education, AI-powered tools are now commonplace, utilized in diverse teaching and learning environments for simulation, academic writing, and clinical case analysis. However, concerns linger regarding the potential impact of sustained reliance on these technologies on the development of critical thinking, a cornerstone of effective nursing practice. A recent study published in Frontiers in Psychology delves into this complex relationship, examining the associations between AI dependence, AI literacy, and critical thinking ability among nursing interns in Hunan, China, and uncovering a significant mediating role for AI literacy.

The research, conducted between January and February 2026, surveyed 517 nursing interns from multiple hospitals. The findings reveal a nuanced picture: AI dependence, rather than being detrimental, showed a positive association with critical thinking ability. Crucially, this relationship is partially explained by AI literacy. This suggests that the extent to which nursing interns understand and can effectively utilize AI tools plays a vital role in how their reliance on AI influences their critical thinking skills.

Understanding the Landscape: AI in Nursing Education

The pervasive adoption of AI in education is reshaping pedagogical approaches. Generative AI, particularly large language models, offers immediate and structured explanations for user queries, proving to be a valuable resource. In nursing education, these tools are employed for a variety of purposes, from enhancing simulation exercises that mimic real-world clinical scenarios to assisting with the intricate process of academic writing and facilitating the analysis of complex clinical cases. This widespread integration reflects a growing recognition of AI’s potential to augment learning experiences.

However, the rapid evolution of AI technology is not without its challenges. Large language models, while powerful, can sometimes generate inaccurate or misleading information. This inherent fallibility raises critical questions for educators, particularly in a field like nursing where precision and accuracy are paramount for patient safety. The core concern is whether an over-reliance on AI outputs might inadvertently hinder the development of critical thinking skills, which are essential for clinical judgment, evidence-based decision-making, and ultimately, ensuring optimal patient care. Critical thinking, defined as the ability to employ higher-order cognitive skills such as conceptualization, analysis, evaluation, and reflection, is fundamental to a nurse’s ability to navigate complex patient situations.

Defining Key Constructs: AI Dependence, Literacy, and Critical Thinking

AI dependence, as explored in this study, encompasses the degree to which individuals rely on AI systems for learning and problem-solving. It extends beyond mere functional reliance to include emotional dependence, highlighting a psychological inclination to trust or favor AI-generated outputs. Concerns have been voiced that an uncritical reliance on AI might lead to a decline in higher-order cognitive engagement.

Conversely, Cognitive Load Theory (CLT) offers a more intricate perspective. CLT posits that individuals have limited working memory capacity, and learning is most effective when cognitive resources are utilized efficiently. AI tools that reduce extraneous cognitive load—such as organizing information or summarizing complex texts—and support germane load (deep processing) can potentially facilitate higher-order thinking. In this context, AI dependence could reflect an efficient allocation of cognitive resources, rather than a detrimental substitution of thought.

AI literacy, a concept gaining significant traction, refers to the knowledge and skills required to understand, evaluate, and effectively use AI tools. This includes an awareness of how AI systems function, their inherent limitations and potential biases, and the ability to critically assess the accuracy and logic of AI-generated information. AI literacy is posited to be a crucial factor in bridging the gap between AI dependence and critical thinking. Individuals with higher AI literacy are more likely to question AI outputs, cross-verify information, and critically integrate AI-generated content into their own analyses. This active engagement, facilitated by AI literacy, can help manage cognitive resources, reduce unnecessary load, and promote deeper learning through evaluation and reflection.

The Study’s Methodology and Findings

The multicenter cross-sectional study employed a convenience sampling method, recruiting nursing interns from various hospitals in Hunan, China, between January and February 2026. Participants completed an online survey comprising a general information questionnaire, an AI dependence scale, an AI literacy scale, and a critical thinking ability scale. The data were analyzed using descriptive statistics, Pearson correlation, and regression-based mediation analysis with 5,000 bootstrap resamples.

The study included 517 nursing interns in its final analysis. Key demographic findings indicated a predominantly female sample (85.5%), with a significant proportion being undergraduate students (50.1%) and hailing from rural areas (72.9%). Notably, a majority (69.6%) had received AI-related training, and nearly half (46.6%) reported using AI on a weekly basis.

Key findings from the study include:

• Moderate Levels of AI Engagement and Skills: Nursing interns reported moderate levels of AI dependence, with functional dependence slightly outweighing emotional dependence. This suggests AI is primarily utilized as a practical aid. AI literacy scores were also moderate, aligning with broader trends in educational technology adoption. Critical thinking ability scores were found to be at a moderate level, which was higher than in some previous studies that included a broader range of students, potentially indicating the influence of clinical experience on cognitive skills.

• Positive Associations: The study found significant positive correlations between AI dependence and both AI literacy and critical thinking ability. Similarly, AI literacy was strongly and positively associated with critical thinking ability. These initial correlations suggest that greater engagement with AI is linked to higher levels of both AI understanding and critical thinking.

• AI Literacy as a Mediator: The most significant finding emerged from the mediation analysis. After accounting for demographic factors and other relevant variables, AI literacy was found to partially mediate the relationship between AI dependence and critical thinking ability. This means that while AI dependence has a direct positive effect on critical thinking, a portion of this effect is channeled through the mediator, AI literacy. The indirect effect was statistically significant, with a 95% confidence interval ranging from 0.399 to 0.610, underscoring the importance of AI literacy in this relationship. The mediation model successfully explained 52.0% of the variance in critical thinking ability.

The mediation model indicated that as AI dependence increases, AI literacy also tends to increase. This heightened AI literacy, in turn, positively influences critical thinking ability. While AI dependence also had a direct positive association with critical thinking, the mediating role of AI literacy highlights that the way interns engage with AI—informed by their literacy—is crucial.

Implications for Nursing Education and Practice

The findings of this study carry significant implications for the future of nursing education and professional development. The positive association between AI dependence and critical thinking, mediated by AI literacy, suggests that AI tools, when used effectively, can be powerful cognitive enhancers rather than inhibitors.

Key takeaways for educators and institutions include:

• Embrace, Don’t Restrict, AI: Instead of outright restrictions on AI use, educational institutions should focus on fostering critical and responsible engagement with these tools. The research suggests that simply limiting access to AI may not be the most effective strategy.

• Prioritize AI Literacy Training: The study’s central finding emphasizes the critical role of AI literacy. Curricula should be enhanced to include comprehensive training on AI fundamentals, ethical considerations, bias detection, and effective evaluation of AI-generated content. This will equip nursing interns with the necessary skills to navigate AI tools safely and productively.

• Integrate AI as a Cognitive Scaffold: Clinical instructors can leverage AI as a cognitive support tool. Structured learning activities that encourage interns to use AI for information gathering, hypothesis generation, and then critically evaluate and verify the outputs can enhance learning. This approach positions AI as a partner in the learning process, promoting deeper analysis and reflective practice.

• Understanding Functional Dependence: The study’s observation that functional dependence was more prominent than emotional dependence suggests that interns are more likely to use AI for task completion. This understanding can inform the design of AI-integrated learning modules that directly support clinical workflows and information management, while still emphasizing the need for critical oversight.

• Addressing Potential Biases: While not explicitly detailed in the findings, the mention of AI ethics within the AI literacy scale underscores the importance of training future nurses to be aware of and mitigate potential biases in AI algorithms, which could impact clinical decision-making.

Addressing Limitations and Future Directions

Despite its valuable contributions, the study acknowledges several limitations. The cross-sectional design precludes definitive causal inferences, meaning that while associations were found, it is not possible to definitively state that AI dependence causes improved critical thinking through AI literacy. The study’s reliance on self-reported data, while common in such research, may be subject to social desirability bias or inaccuracies in self-perception. Furthermore, the convenience sampling method and recruitment from a specific region in China limit the generalizability of the findings to other cultural contexts and educational systems.

Future research should adopt longitudinal designs to track the development of these constructs over time, allowing for a more robust understanding of causal relationships. The use of mixed-methods approaches, incorporating objective measures of AI use and critical thinking alongside self-reports, could provide richer insights. Investigating the specific types of AI tools and their applications within nursing education, as well as exploring the impact of institutional policies and pedagogical approaches on AI dependence and literacy, would also be beneficial. Finally, expanding research to diverse international settings would enhance the generalizability of findings.

Conclusion

In conclusion, this multicenter cross-sectional study provides compelling evidence that AI dependence is positively associated with critical thinking ability among nursing interns. Crucially, AI literacy emerges as a significant partial mediator in this relationship. These findings underscore the imperative for nursing education programs to proactively integrate AI literacy training. By equipping students with the skills to critically evaluate and responsibly utilize AI, educational institutions can ensure that artificial intelligence serves as a valuable cognitive support, fostering the development of essential professional reasoning and clinical judgment skills vital for the future of nursing. The era of AI in healthcare education is here, and fostering informed, critical engagement is key to unlocking its full potential for improving patient care.