Characterization of heterogeneous bioinformatics training needs in the AI era

Abstract

Bioinformatics training providers serve heterogeneous audiences, a challenge sharpened by fast-evolving technologies such as generative artificial intelligence (AI). Analyzing training-needs surveys in aggregate can mask this heterogeneity. We present a network-based clustering protocol that groups respondents by the similarity of their Likert-type topic ratings: respondents form a Spearman correlation network, and community detection groups them into learner profiles with shared training needs, independent of demographics. We demonstrate the protocol on the 2026 SIB Swiss Institute of Bioinformatics (SIB) training needs survey (n=260), covering topics, barriers, and formats. We identified four interpretable learner profiles: AI and data, infectious disease genomics, functional genomics, and biochemistry, imaging & AI. Each had distinct topic priorities, barriers, and format preferences that an aggregate analysis hides. For example, LLM-based knowledge extraction ranked 2nd overall but only 16th in the infectious disease genomics profile, whereas phylogenetics ranked 17th overall but first there. Across profiles, AI/LLM skills and reproducible data practices were in high demand, whereas AI-assisted learning was, unexpectedly, the least preferred format. Because the protocol requires only a shared Likert scale, it is reusable for other training-needs assessments; the implementation is openly available. Profile-based curricula can target each group's topics, formats, and barriers.