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

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.

Authors

Publication Details

Journal
Global Journal of Medical and Clinical Case Reports
Published
2025-06-24
DOI
https://doi.org/10.17352/2455-5282.000214
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Okpodu Camellia Moses, Samelia Okpodu-Pyuzza
Global Journal of Medical and Clinical Case Reports
Genetics, Bioinformatics, and Biomedical Research
article

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

Okpodu Camellia Moses, Samelia Okpodu-Pyuzza
article en

Abstract

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.

Global Journal of Medical and Clinical Case ReportsVol. 12(6)
Openalex Percentile: Top 87%
Genetics, Bioinformatics, and Biomedical Research
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