Artificial Intelligence and Genetics Education and Career Pathways Interdisciplinary Curriculum, Genomic Applications, Ethical Challenges, and Emerging Opportunities

The rapid growth of genomic sequencing and biological data has created a need for computational systems capable of identifying patterns that cannot be efficiently examined through traditional laboratory methods alone. Artificial intelligence and genetics have therefore emerged as closely connected fields, combining molecular biology, genomics, computer science, mathematics, statistics, and healthcare. Artificial intelligence can help researchers interpret DNA sequences, identify disease-associated genetic variants, predict protein structures, support genome-editing research, and develop more personalised approaches to medicine. This review paper examines Artificial Intelligence and Genetics as an emerging interdisciplinary area of undergraduate study and professional development. Using a secondary-research methodology, the paper reviews educational resources, government publications, scientific literature, and publicly available reports to explore: (i) why AI and genetics must be studied through an interdisciplinary curriculum; (ii) how AI is being applied in genomic research, clinical genetics, agriculture, drug development, and precision medicine; (iii) the technical, ethical, and regulatory challenges associated with genetic data; and (iv) the skills and educational pathways that can prepare undergraduate students for careers in computational genomics and related fields. The review finds that AI is increasingly useful for processing large and complex genomic datasets, prioritising potentially harmful genetic variants, supporting rare-disease diagnosis, improving gene-editing design, and integrating genomic information with clinical and environmental data. However, the effectiveness of these technologies depends on the quality and diversity of datasets, the transparency of algorithms, appropriate clinical validation, protection of genomic privacy, and the continued involvement of qualified geneticists and healthcare professionals. The paper concludes that AI and Genetics offers promising educational and career opportunities for students interested in both life sciences and computation. Undergraduate programmes in this area should combine foundational genetics, programming, statistics, bioinformatics, ethics, and practical research experience. Students trained across these disciplines will be better positioned to contribute responsibly to genomic medicine, biotechnology, agricultural genetics, pharmaceutical research, and biological data science.