Characterization of heterogeneous bioinformatics training needs in the AI era

Network-based clustering of the 2026 SIB Swiss Institute of Bioinformatics training-needs survey (n=260) identifies four learner profiles with distinct topic priorities, barriers, and format preferences.

• Why it matters: Aggregate analysis of training-needs surveys can mask heterogeneity, especially as fast-evolving technologies like generative AI change what learners need.
• What they did: Respondents’ Likert-type topic ratings were used to build a Spearman correlation network, then community detection produced learner profiles independent of demographics.
• The result: LLM-based knowledge extraction ranked 2nd overall but 16th in the infectious disease genomics profile, while phylogenetics ranked 17th overall but first there, enabling profile-based curricula.