Enterprise AI Analysis
Research on the experience of utilizing artificial intelligence in nursing learning from the perspective of nursing students – a qualitative study
This study explores the lived experiences of undergraduate nursing students interacting with Artificial Intelligence (AI) in their learning, offering insights into AI's functional benefits, content challenges, performance aspects, and ethical considerations. The findings provide a qualitative basis for optimizing AI integration in nursing education.
Executive Impact: Key Takeaways
AI's role in nursing education is rapidly expanding, offering significant opportunities for enhanced learning outcomes and operational efficiency, but also introducing new challenges in content reliability and ethical use.
21
Students Interviewed
5
Main Themes Identified
13
Sub-themes Emerged
35%
Efficiency Improvement Potential (Healthcare AI)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Functional Experience
Content Experience
Performance Experience
Attitudes & Behavior
Ethical Considerations
Theme 1: Functional Experiences with AI
Participants widely reported that AI technologies effectively overcome traditional learning constraints, offering flexibility and accessibility in nursing education. AI enhances learning engagement by sparking interest and fostering self-directed exploration, and significantly improves learning efficiency by summarizing information and individualizing research guidance. Tools like virtual online courses, simulation platforms, and AI assistants were highlighted for their ability to streamline complex curricula and support academic development.
- Overcoming Temporal and Spatial Constraints: AI tools enable learning and practice "anytime and anywhere," removing limitations of physical location and schedules.
- Innovative AI Enhances Learning Engagement: AI positively impacts motivation, encouraging students to explore new knowledge actively and fostering self-directed learning.
- AI Enhances the Efficiency of Learning: AI assists in quickly understanding research trends, extracting key information, and reducing the academic burden of demanding nursing curricula.
- AI Meets Individualized Research Needs: AI provides structured guidance for academic writing, helps sort ideas, and fosters broader cognitive engagement.
Theme 2: Content-Related Experiences with AI
AI serves as a valuable supplement to traditional teaching, integrating interactive training and knowledge consolidation. However, significant concerns were raised regarding the accuracy and reliability of AI-generated content. Issues included fabricated references, inconsistent logical coherence, and overly generalized or shallow information, highlighting the need for critical evaluation.
- AI is a Supplement to Traditional Classroom Teaching: AI robots and virtual simulation platforms facilitate interactive training and knowledge consolidation, supplementing traditional instruction.
- Accuracy and Reliability of AI-Generated Content: Participants expressed concerns about fabricated references, logical inconsistencies, and generalized, superficial content in AI outputs, necessitating careful verification.
Theme 3: Performance-Related Experiences with AI
AI technologies demonstrate strong multi-terminal adaptability, integrating seamlessly with various electronic devices, which enhances accessibility. Despite this, objective limitations of AI platforms, such as a lack of clear operational instructions and unstable network performance for virtual simulations, significantly impact students' willingness to engage and highlight the need for backend optimization.
- Multi-Terminal Adaptability of AI Technology: AI is compatible across smartphones, tablets, and PCs, enhancing ease of access and use.
- Limitations of Virtual Simulation Platforms and the Need for Back-End Optimization: Students faced challenges with unclear instructions and inconsistent performance/lag on simulation platforms, suggesting a need for technical improvements.
Theme 4: Attitudes Towards AI and Its Influence on Behavior
Students recognize the increasing integration of AI with traditional teaching and strongly prefer a complementary, blended approach. However, a growing reliance on Generative AI was noted, which participants perceived as diminishing their independent critical and reflective thinking skills, leading to potential cognitive inertia.
- Students' Expectations for the Integration of Traditional Classrooms and AI: A strong preference for a complementary approach that combines AI with conventional instruction, rather than replacement.
- Dependence and Inertia Induced by Generative AI: Participants reported a growing reliance on GenAI, which they felt reduced motivation for independent critical thinking and reflection, leading to procrastination.
Theme 5: Ethical Considerations for the Use of AI
Key ethical concerns include the generation of content that may duplicate existing work, raising plagiarism fears. While teachers emphasize regulated use, inconsistencies in enforcement exist. Students employ strategies to mitigate risks, like generating multiple outputs and "training" AI, but robust frameworks and systematic AI literacy are crucial for academic integrity.
- Concerns Regarding the Generation of Content that May Duplicate Existing Work: Students worry about plagiarism when submitting AI-generated assignments, as the content may not stem from independent thought.
- Teachers Emphasize the Regulated Use of AI: Educators generally set regulations for AI use, though enforcement varies, highlighting the need for consistent guidelines.
- Implementation of Strategies to Mitigate Risks to Academic Integrity: Students use tactics like comparing multiple AI outputs, using different platforms, and customizing AI tools to maintain academic integrity.
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Main Themes / Sub-themes Identified in Nursing Students' AI Experience
Enterprise Process Flow: Qualitative Data Analysis Method
1. Immersion
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2. Inductive Coding
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3. Category Formation
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4. Theme Development
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5. Contextual Interpretation with Exemplars
| Argument of Research | Strategy |
|---|---|
| Enhancing learning effectiveness |
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| Addressing content-related risks |
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| Back-end support for AI |
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| Overcoming dependence and integrating tradition with modernity |
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| Institutional construction of academic integrity |
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Case Study: Functional Advantages - Overcoming Constraints
Participant P12: "The virtual simulation platform is very helpful. Whenever I need to practice nursing procedures, I can access them anytime and anywhere, as long as I have an internet connection."
This highlights AI's ability to provide flexible, on-demand learning environments that are crucial for practical skill development in nursing, breaking down traditional barriers of time and physical presence.
Case Study: Content Reliability Concerns - Fabricated Information
Participant P3: "Yesterday, I used AI to write a paper. I instructed it to include only real sources and to follow the academic structure of a paper, including references. However, the references it provided were fabricated, and I couldn't find them on CNKI.”
This illustrates a critical challenge in AI content generation: the potential for AI to produce factually incorrect or unverified information, which poses significant risks to academic integrity and the quality of research in an enterprise context.
Case Study: Behavioral Impact - Over-reliance and Inertia
Participant P1: "I'm actually kind of dependent on it. Like, when I get an assignment that I figure AI can write for me, the first thought that pops into my head is, ‘Let me go chat with it first' (smile)."
This quote exemplifies the risk of over-reliance on AI tools, which can lead to a reduction in independent critical thinking and a tendency towards cognitive inertia, impacting essential human competencies like problem-solving and self-directed learning.
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Your AI Implementation Roadmap
A phased approach to integrate AI into your enterprise, leveraging the insights from this research for successful adoption and optimal outcomes.
Phase 01: Strategic Assessment & Planning
Conduct a comprehensive analysis of current workflows and identify key areas where AI can enhance efficiency in nursing education. Define clear objectives and success metrics based on the functional and efficiency benefits highlighted in the study. Develop a tailored integration plan, focusing on supplementary roles for AI rather than full replacement.
Phase 02: Pilot Program & Platform Integration
Implement AI tools (e.g., virtual simulations, generative AI for content assistance) in a controlled pilot environment with a select group of students and educators. Prioritize multi-terminal adaptable platforms and ensure robust backend optimization to address performance issues. Gather initial feedback to refine operational instructions and system stability, informed by student experiences with limitations.
Phase 03: Ethical Framework & Training Development
Establish clear guidelines for AI usage, emphasizing academic integrity and responsible content creation, directly addressing concerns about plagiarism and reliability. Develop training modules for students and faculty on "AI literacy," focusing on critical evaluation of AI-generated content and mitigating over-reliance, as observed in student behavior.
Phase 04: Scaled Deployment & Continuous Optimization
Expand AI integration across the curriculum, fostering a blended learning model that combines traditional teaching with AI-assisted instruction. Continuously monitor performance, content reliability, and student attitudes. Implement feedback mechanisms to address emerging issues and ensure AI tools effectively enhance, rather than hinder, critical thinking and human competencies in nursing practice.
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