DETERMINING UNDERGRADUATE NURSING STUDENTS' READINESS LEVEL TOWARDS ARTIFICIAL INTELLIGENCE IN PRIVATE NURSING COLLEGES AT DISTRICT CHARSADDA, KPK, PAKISTAN

Abstract

Background: AI is transforming the health sector through more precise diagnoses, automation of administrative work, and even the most conducive ground for predictive analysis in patient care. This aspect of AI applications has the potential to enhance patient outcomes and decision-making towards quickening clinical operations in nursing. As AI features health care settings, nursing students should not only be prepared but also know how AI will affect their profession upon graduation. In addition, preparedness means not only the knowledge itself, but also the ability to apply concepts in clinical practice and understanding of the ethical issues in the use of AI in patient care. This study focuses on the readiness for AI among undergraduate nursing students in private institutions of Charsadda, KPK, Pakistan, intending to identify knowledge gaps that may impede successful integration of AI into the practice of nursing. It explores the need for readiness among students about health care environments which might be driven by AI so that the educational techniques are built which foster abilities to function effectively in such technologically enhanced health care environments. 

Aim: To determine undergraduate nursing students' readiness level towards artificial intelligence (AI) in private nursing colleges at Charsadda, KPK.

Methodology: The study uses a cross-sectional design and a questionnaire adapted from a study published in 2024 focusing on how prepared undergraduate nursing students are toward artificial intelligence. A Likert scale was used to determine the level of AI readiness among 109 participants, who were third and fourth-year students in undergraduate nursing programs.

Results: According to the results, 56.0% of the students showed a medium level of readiness, followed by high readiness (32.1%) and low readiness (11.9%). Even though most students had some prior exposure to AI, there is still a gap between theoretical understanding and real-world application in clinical settings. 

Conclusion: The study emphasizes the incorporation of AI in nursing education, how to use it practically, and ethical considerations related to AI-driven technologies. This would guarantee that nursing students have the necessary tools to function in healthcare settings powered by AI. Future research should concentrate on longitudinal studies to assess how readiness levels change over time, what factors affect the readiness level, and the long-term effects of AI-ready training on nursing practices.