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Training the Next Generation: A Generative AI-enhanced Framework for Teaching Bioinformatics to Life Science Students with Relevance to Cardiovascular Innovation

Year: 2025

Type: article

Abstract: Bioinformatics is increasingly essential for life science students preparing for careers in biomedical research, biotechnology, and related fields. However, many undergraduate programs lack accessible entry points for teaching computational biology. This short communication presents a faculty-led instructional framework that integrates Python and generative AI tools to introduce bioinformatics in a life sciences curriculum. Designed for instructors without formal programming backgrounds, this approach emphasizes hands-on learning and interdisciplinary relevance. Our experience shows that generative AI can help reduce technical barriers for life science faculty and support scalable instruction in computational methods and data-centered biological exploration.

Source: Global Journal of Medical and Clinical Case Reports

DOI: 10.17352/2455-5282.000214

Authors: Camellia Moses Okpodu*, Samelia Okpodu-Pyuzza

Language: English

Topic: Genetics, Bioinformatics, and Biomedical Research

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W4411608805

FWCI:

Cites: 7

Cited by:

Related to: 10