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Updated: May 28, 2026
09:34
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
Learning R with generative AI in a metagenomic data science course.
Jeffrey L Blanchard1,2,3
1Biology Department, University of Massachusetts Amherst, Amherst, Massachusetts, USA.
Journal of Microbiology & Biology Education
|May 27, 2026
Summary
Generative artificial intelligence (AI) aids novice coders in R programming for biology but doesn't replace foundational learning. Effective integration requires pedagogical support and reflection, not just code generation.
Area of Science:
- Biology Education
- Computational Biology
- Artificial Intelligence
Background:
- Generative artificial intelligence (AI) tools are increasingly adopted by students in introductory programming.
- Evidence-based guidance for integrating AI into biology education is limited.
- Novice coders in biology often lack prior programming experience.
Purpose of the Study:
- Examine student experiences with generative AI in a beginner R programming course.
- Assess AI's influence on learning, productivity, and problem-solving in metagenomic data analysis.
- Identify best practices for AI integration in undergraduate biology education.
Main Methods:
- Anonymous survey administered to 43 students in an introductory R programming course.
- Collected quantitative ratings and qualitative reflections on AI use.
- Focused on a course analyzing metagenomic data.
Main Results:
- Most students (79%) had little prior coding experience and frequently used AI.
- Students found AI helpful for R code suggestions, syntax explanation, and brainstorming (over 70% endorsement).
- AI errors were common (over 90% encountered incorrect output), necessitating foundational R instruction before AI use.
Conclusions:
- Generative AI can support novice programmers but cannot substitute for fundamental instruction.
- Effective AI integration in biology education requires pedagogical scaffolding and student reflection.
- Clearer instructional support for AI-mediated practices is needed for successful adoption.
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