29. Introducing Data Science Concepts to Animal Science Graduate Students Through Application-based Learning.

Abstract The rapid integration of artificial intelligence (AI) and data science into biological research has created a growing need for computational literacy among graduate students in life science disciplines. However, many students’ entering biology-related graduate programs have limited formal training in programming, statistics, or machine learning, creating a significant barrier to engaging with modern data-driven research. The objective of this study was to evaluate baseline perceptions of AI and data science among animal science students prior to participating in a course introducing these concepts through application-based learning. A pre-course perception survey was administered to students enrolled in an introductory AI and data science course in animal science (n = 10). Survey questions used a 5-point Likert scale to assess prior programming experience, familiarity with AI concepts, attitudes toward AI applications in animal science and motivation to learn these skills. Students reported limited prior experience with programming languages (mean = 2.9 ± 1.45) and moderate use of AI tools in daily life (mean = 2.98 ± 1.32). Despite limited technical exposure, students indicated moderate understanding of basic AI and data science concepts (mean = 3.6 ± 0.70 and 3.6 ± 0.52, respectively). Participants expressed strong agreement that AI and data science can be effectively applied in animal science (mean = 3.9 ± 0.57) and that learning these skills would benefit their academic or professional development (mean = 4.0 ± 0.67). Students also expressed moderate motivation to learn AI applications in livestock systems (mean = 3.5 ± 1.08). While this project provides the current perceptions of students and their skill levels in AI and data science, a follow-up post survey to assess the effects of such a course, and combining it with a Mann-Whiteney U test will further inform the required pedagogical shifts. These preliminary findings highlight the value of interdisciplinary teaching approaches that integrate biological applications with computational methods (such as an AI in animal science course) and may inform curriculum design for graduate programs seeking to prepare life science students for increasingly data-intensive research environments.