Navigating the AI Frontier: Cultivating Self-Efficacy with Artificial Intelligence Agents in Nursing Education

The rapid integration of artificial intelligence (AI) agents into various professional spheres necessitates a critical examination of their role in higher education. As nursing education strives to prepare future clinicians for an increasingly technologically sophisticated healthcare landscape, understanding students' self-efficacy with AI tools becomes paramount. A forthcoming study by Power (in review), "Evaluating Graduate Education Students’ Self-Efficacy with the Use of Artificial Intelligence Agents: A Case Study," offers invaluable insights into this evolving relationship, providing a framework for fostering confidence and competence with AI among nursing students.

The Self-Efficacy Imperative: Power's Case Study on AI in Education

Power's (in review) case study investigated graduate education students' self-efficacy regarding the use of AI agents, specifically focusing on large language models like ChatGPT, for academic writing tasks. The methodology involved students generating academic text with ChatGPT and then rigorously critiquing the output for elements such as prompt effectiveness, factual accuracy, omissions, and adherence to writing and formatting conventions. The study utilized a newly developed instrument, the ChatGPT Teacher's Sense of Efficacy Scale (Chat-T), to gauge the impact of this AI-focused professional development.

Key findings revealed a nuanced picture: while participants gained a deeper understanding of AI's capabilities and limitations, particularly its pitfalls as an academic writing aid, they also expressed an increased eagerness to integrate these tools into their work. Crucially, this enthusiasm was coupled with a pronounced desire for further targeted training and support, specifically emphasizing effective pedagogical approaches for AI integration. The study underscored that a lack of understanding regarding appropriate AI pedagogical practices can lead to anxiety, suggesting that deliberate, structured support is essential for building confidence (Power, in review). Ultimately, the students developed an appreciation for the complementary roles AI can play alongside human educators.

Fostering AI Self-Efficacy in Nursing Education: Practical Applications

The insights from Power's (in review) research translate directly to the unique demands of nursing education. As AI-powered tools become more prevalent in clinical settings—from diagnostic support systems to intelligent patient monitoring and personalized treatment plans—nursing students must develop high self-efficacy in interacting with these agents. Consider the following applications:

  • Curriculum Integration of AI Literacy: Rather than treating AI as an ancillary topic, nursing curricula should integrate AI literacy across various courses. This could involve modules on understanding AI's underlying principles, its ethical implications in healthcare, and the responsible use of AI for clinical decision support, documentation, and patient education.

  • Simulated Clinical AI Encounters: High-fidelity simulations could incorporate AI agents that mimic real-world clinical scenarios. For instance, students might interact with an AI-powered electronic health record system that offers diagnostic suggestions or an AI-driven patient simulator that provides adaptive feedback. This allows students to gain hands-on experience in a safe environment, identifying AI's strengths and limitations without direct patient risk.

  • Problem-Based Learning with AI Assistance: Encourage students to use AI agents as tools for inquiry during problem-based learning (PBL) activities. For example, students could use AI to research rare diseases, synthesize current evidence-based practices, or even generate differential diagnoses for a complex case study. Subsequent peer and faculty critique, similar to Power's methodology, would then focus on evaluating the AI's output, thereby enhancing critical thinking and responsible AI engagement.

  • Developing AI Evaluation Skills: Teach students to critically appraise AI outputs for factual accuracy, bias, and context-appropriateness. This includes understanding the potential for AI "hallucinations" or errors, fostering a necessary skepticism that complements the eagerness to adopt new technology (Power, in review). Such skills are vital for patient safety and ethical practice.

  • Targeted Professional Development for Faculty: Just as graduate education students desired more training, nursing faculty also need ongoing professional development in effectively integrating AI into their teaching. This ensures that educators can model best practices, mitigate anxieties, and provide the necessary support for students to confidently leverage AI tools.

Conclusion

Power's (in review) case study offers a timely reminder that while AI promises transformative benefits in education, successful integration hinges on cultivating students' self-efficacy and providing comprehensive support. For nursing education, this means proactively preparing students not only to understand AI's potential but also to critically engage with its outputs, recognize its limitations, and confidently apply it in a manner that enhances, rather than diminishes, the essence of compassionate and evidence-based patient care. By addressing the self-efficacy imperative, nursing programs can empower future clinicians to navigate the complexities of AI-enhanced healthcare with competence and confidence.

References

Power, R. (in review). Evaluating Graduate Education Students’ Self-Efficacy with the Use of Artificial Intelligence Agents: A Case Study. Journal of Educational Informatics. 

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