Introduction

Clark State College, in collaboration with Unbound Medicine®, embarked on an innovative journey to integrate Artificial Intelligence (AI) into their nursing education curriculum. This initiative aimed to enhance teaching methodologies, streamline administrative tasks, and improve student engagement. This client study provides a comprehensive overview of the implementation, evaluation, and implications of AI tools in nursing education at Clark State College.

Purpose

The primary objectives of this study were to:

• Share insights on the practical application of AI in nursing education.
• Discuss the specific AI products utilized and their intended educational outcomes.
• Examine ethical considerations surrounding AI integration in nursing curricula.
• Evaluate the safety, effectiveness, and bias of the AI tools.
• Highlight the time savings experienced by faculty and how this time was redirected towards enhancing student learning.

Design & Methodology

• Participants: The study involved nine faculty members from the nursing department at Clark State College, each with varying levels of experience in nursing education and familiarity with AI tools.
• Tool Used: Assist AI on the Nursing Central platform from Unbound Medicine.
• Evaluation Criteria:
  • Safety: Assessment of how AI tools protect sensitive student and patient data.
  • Effectiveness: Measurement of AI’s impact on learning outcomes, including student engagement and performance.
  • Level of Bias: Analysis of AI outputs for any inherent biases that could affect educational fairness and equity.
• Data Collection: Data were collected through surveys, interviews, and continuous feedback sessions with faculty members to ensure a comprehensive evaluation of the AI tools. Both qualitative and quantitative insights were gathered to understand faculty experiences and perceptions.

Strengths

• Relevance to Current Trends: The research addresses the growing trend of incorporating technology in education, particularly in nursing, which is crucial for preparing students for modern healthcare environments.
• Comprehensive Evaluation: The study employed a mixed-methods approach, combining quantitative surveys and qualitative interviews, providing a well-rounded understanding of faculty experiences with AI tools.
• Initial Positive Outcomes: Early results indicated significant time savings for educators, leading to improved educational quality through increased focus on personalized student mentoring and curriculum development.
• Alignment with Educational Goals: The use of AI tools aligns with contemporary educational goals of improving efficiency and enhancing student learning experiences.

Weaknesses

• Small Sample Size: The study was conducted with a small sample size, which may limit the applicability of the findings to the broader faculty population.
• Short Duration: The short evaluation period may not capture the long-term effects of AI integration on educational outcomes, necessitating further longitudinal studies.
• Potential Bias: Faculty members who participated may have had predisposed views of AI tools, which could introduce bias in the reported outcomes and perceptions.

Conclusions

Preliminary results indicate that the implementation of AI tools improved educational efficiency, with educators saving an average of 4 hours per week. This time savings allowed faculty to focus on personalized student learning and curriculum development. The evaluation revealed that the AI tools were safe and effective, with minimal bias detected in the outputs. Faculty reported confidence in the AI’s ability to protect sensitive information.

Authors

• Dr. Scott Dolan, PhD, RN
• Garrett Fisher, MSN, RN
• Sarah Hagenbuch, MSN, RN
• Christine Schlater, MSN, RN

If you would like to contact the authors or are interested in conducting a similar research study in partnership with Unbound Medicine, please contact Brooke Hahnert at bhahnert@unboundmedicine.com.

Presented at the Ohio League of Nursing Conference, March 20-21, 2025.