ABSTRACT Generative artificial intelligence (AI) tools are increasingly used by students in introductory coding courses; however, evidence-based guidance for integrating these tools into biology education remains limited. We examined student experiences with generative AI in a beginner R programming course focused on metagenomic data analysis. An anonymous survey ( n = 43) captured quantitative ratings and qualitative reflections on how AI influenced learning, productivity, and problem-solving practices. Most respondents entered the course with little to no prior coding experience (79%) and reported frequent AI use throughout the semester, indicating that AI quickly became embedded in students’ workflows. Students rated AI as highly helpful for suggesting R code, explaining syntax and logic, and brainstorming analyses, with over 70% endorsing each use case. However, AI errors were common: over 90% of students encountered incorrect output at least sometimes, including domain-specific misinterpretations and overcomplicated or syntactically incorrect code. Notably, students identified a need for clearer instructional support in core AI-mediated practices. The most frequent recommendation for course redesign was to introduce foundational R concepts prior to AI use, highlighting a threshold-competency principle for effective AI integration. Together, these findings suggest that generative AI can support novice coders but does not substitute for foundational instruction. Effective AI integration requires deliberate pedagogical scaffolding and reflection rather than code generation alone. These principles are likely to remain critical as AI tools become more capable and more widely adopted in undergraduate biology education.
Jeffrey L. Blanchard (Wed,) studied this question.