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

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Global Journal of Medical and Clinical Case Reports volume12-issue6
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Submitted: June 12, 2025
Published: Jul 8, 2025
Updated: 2025-07-08
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2025-07-08 (2)
DOI: 10.17352/2455-5282.000214
Keywords:
bioinformatics, generative AI, life science education, python programming, cardiovascular innovation

Main Article Content

Camellia Moses Okpodu*
Department of Botany, University of Wyoming, Laramie, WY 82070, USA
Samelia Okpodu-Pyuzza
The Giled Alliance Group, Huntingtown, MD 20639, USA

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.

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Article Details

Camellia Moses Okpodu*, & Okpodu-Pyuzza, S. (2025). Training the Next Generation: A Generative AI-enhanced Framework for Teaching Bioinformatics to Life Science Students with Relevance to Cardiovascular Innovation. Global Journal of Medical and Clinical Case Reports, 12(6), 140–142. https://doi.org/10.17352/2455-5282.000214 (Original work published June 21, 2025)
Short Communications

Copyright (c) 2025 Okpodu CM, et al.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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