Background: As the utilization of artificial intelligence (AI) in healthcare increases, nursing programs must incorporate AI into the curriculum, but minimal literature exists. Since AACN (2021) has moved nursing education to competency-based education, it is critical for faculty to assess student competence in areas such as the use of AI. The current study evaluated students’ scores on a competency focused on developing an electronic health record (EHR) alert using AI and students’ scores on items from the Self-Assessment of Nursing Informatics Competencies Scale (SANICS; 2009, 2015). Methods: A retrospective descriptive design was used with a convenience sample of students from two programs, Doctor of Nursing Practice (DNP) and Master of Nursing (MSN), in an online informatics course. For this competency, students searched the literature comparing two AI tools to PubMed and UpToDate (individual). Based on their review of the literature, students used AI to create an alert screen and accompanying workflow analysis (group). To evaluate students’ mastery of the competency, students’ scores were assigned a value of 1 (mastered), 2 (competent), or 3 (did not master). The SANICS was administered at baseline and at the end of the course. SANICS competencies, associated with this assignment, focused on searching the literature, assessing accuracy and relevance of information, clinician participation in the design, implementation, and evaluation of systems and advocacy for incorporating informatics concepts into practice. To evaluate the SANICS competencies, students’ self-reflection scores (baseline and end of course) were analyzed using Paired Samples t-test. The IRB designated the study as exempt. Results: Analysis of competency mastery revealed that few students mastered the individual portion of the competency (DNP=12.4%; MSN=23%) while the majority of students mastered the group portion of the competency (DNP=69.4%; MSN=61%). Students’ self-reflection scores on select SANICS competencies significantly increased from baseline to the end of the course (p=<.001/competency). Conclusion: While the group scores indicated most mastered that portion of the competency, individual scores indicate a need for more emphasis on literature review. Students’ self-reflection scores demonstrated improvement in select competencies. While students learned to incorporate AI into their practice, more work is necessary to determine progression toward competence.