Generative artificial intelligence (AI) tools are rapidly reshaping how students write code, analyze data, and articulate scientific reasoning. In bioinformatics education, where computational workflows intersect with biological interpretation, these tools present both substantial opportunities and serious pedagogical risks. Rather than attempting to prohibit or police AI use, which is increasingly impractical, instructors must reconsider what it means to teach bioinformatics when routine coding, data wrangling, and even statistical analysis can be automated. This is not a temporary disruption; students will build careers in an automated world. A central challenge is the relationship between expertise and effective AI use. Generative AI is most productive in the hands of users who can evaluate its outputs, yet students are precisely the population still developing that capacity. This perspective reflects on teaching undergraduate and graduate bioinformatics courses during the early generative AI era, focusing on how AI tools alter student behavior, assessment design, and instructor responsibilities. When integrated deliberately into pedagogy, AI can lower technical barriers and accelerate learning. When students engage with AI without adequate scaffolding, it risks masking misunderstandings and short-circuiting the development of foundational skills.
Teufel et al. (2026) studied this question.