Possibility of Incorporating Artificial Intelligence Tools into the Contents of Undergraduate Biology Courses: The Genetics Curriculum as an Example

Abstract

This research explores the potential of integrating artificial intelligence (AI) tools into the genetics curriculum within an undergraduate biology program. The study analyzes existing genetics courses, identifies teaching gaps, and proposes AI solutions to enhance learning experiences. The research examines the potential of AI for personalized learning, interactive content, real-time feedback, advanced data analytics, and visualization of genetic concepts. A hypothetical virtual experiment is presented that illustrates how AI can be used to analyze genetic data and predict a child’s genotype from parental data. The research also addresses ethical and practical challenges associated with integrating AI into the genetics curriculum, including bias in algorithms, privacy concerns, transparency issues, and the need for training and support for educators. It highlights the importance of collaborative efforts among educators, researchers, developers, and policymakers to ensure the ethical and effective integration of AI into education. The paper concludes by recommending further research to assess the impact of AI tools on student learning outcomes, develop best practices for their implementation, and ensure responsible use of AI in genetics education. This research contributes to the growing body of work exploring the potential and challenges of integrating AI into education, particularly in the field of genetics.