Teaching bioinformatics with generative AI: judgment, uncertainty, and responsibility

Area of Science:

• Bioinformatics Education
• Computational Biology Pedagogy
• Artificial Intelligence in Science

Background:

• Generative AI tools are transforming scientific education, impacting coding, data analysis, and reasoning.
• Bioinformatics education faces unique challenges due to the intersection of computational workflows and biological interpretation.
• The increasing practicality of AI necessitates a pedagogical shift rather than prohibition.

Purpose of the Study:

• To explore the opportunities and pedagogical risks of generative AI in bioinformatics education.
• To examine how AI tools alter student behavior, assessment design, and instructor responsibilities.
• To reflect on teaching bioinformatics in the early generative AI era.

Main Methods:

• Perspective piece reflecting on teaching undergraduate and graduate bioinformatics courses.
• Analysis of the impact of AI on student skill development and learning processes.
• Consideration of pedagogical strategies for integrating AI into bioinformatics curricula.

Main Results:

• Generative AI can lower technical barriers and accelerate learning when deliberately integrated.
• Unscaffolded AI use risks masking student misunderstandings and hindering foundational skill development.
• A central challenge is balancing AI productivity with the development of student expertise to evaluate AI outputs.

Conclusions:

• Instructors must adapt bioinformatics teaching to an automated world, focusing on critical evaluation of AI.
• Pedagogical strategies are needed to scaffold AI use, ensuring students develop essential skills.
• Integrating AI thoughtfully can enhance bioinformatics education, but risks exist without proper guidance.