Outcome

The learner will understand the evolving implementation and evaluation of AI tools in nursing education, focusing on safety, effectiveness, bias, and the impact of expanding the study to include both ADN and LPN instructors.

Introduction

Artificial Intelligence (AI) is rapidly transforming educational practices, including nursing education. Building on our initial six-month evaluation, Clark State College is expanding its study of AI integration in collaboration with Unbound Medicine to address previously identified limitations and to further explore the impact of AI across diverse nursing education contexts.

Background

Our initial implementation of AI tools in the nursing curriculum demonstrated promising results in efficiency, safety, and bias mitigation. However, the pilot was limited by a small sample size, short duration, and focus exclusively on ADN faculty. Recognizing these limitations, we are broadening the study to include Licensed Practical Nursing (LPN) instructors and extending the evaluation period.

Purpose

The primary objective is to present updated findings on the practical application of AI in nursing education, now encompassing both ADN and LPN programs. We aim to evaluate the safety, effectiveness, and bias of AI tools, explore ethical considerations, and assess the impact on faculty workload and student learning outcomes over a longer timeframe.

Design & Methodology

• Participants: The study involved 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. This process involved gathering qualitative and quantitative insights to understand faculty experiences and perceptions regarding the AI tools.

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, which provided a well-rounded understanding of faculty experiences and perceptions regarding AI tools.
• Initial Positive Outcomes: Early results indicated significant time savings for educators, which can lead 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, making the findings relevant to current trends in nursing education.

Weaknesses

• Small Sample Size: The initial study was conducted with a small sample size of only 9 faculty members, which may limit the applicability of the findings to the broader faculty population. The study expansion included 5 returning participants and 7 new participants.
• Short Duration: The short evaluation period may not be sufficient to 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

• Time Savings: Preliminary results indicate that the implementation of AI tools improved educational efficiency, with educators saving an average of 3 hours 49 minutes per week. This time savings allowed faculty to focus on:
  • Course Preparation: Allowed more time to focus on day-to-day course and student needs.
  • Curriculum Development: Enhanced ability to design and refine course content based on student feedback and learning outcomes.
• Safety and Effectiveness: 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.

Future Research

Future analyses will focus on student academic performance, clinical competence, and job readiness, supporting evidence-based adoption of AI in nursing education.

Authors

• Dr. Scott Dolan, PhD, RN
• Garrett Fisher, MSN, RN
• Sarah Hagenbuch, 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 Organization for Associate Degree Nursing, November 19-22, 2025.

Acknowledgments

Thank you to the following Clark State College faculty for their contributions to this study.

• Morgan Bowman
• Courtney Buck
• Emily Edwards
• Allison Foster
• Kelly Lore
• Nicole Miller
• Katina Osborne
• Elizabeth Richards
• Katherine Stute
• Pam Vaughn
• Tracey Walls
• Karalen Witt