How LLM assistance maps to measurable real-world usefulness for novices in biology labs

Characterize how assistance from large language models translates into measurable real-world usefulness for novice users performing biology laboratory tasks that require tacit knowledge, specifying practical outcome measures and contexts under which such assistance yields improvements.

Background

Beyond the binary question of whether LLMs improve performance, the authors highlight uncertainty about the pathways and conditions through which LLM assistance yields practical benefits for novices in wet-lab tasks. Tacit knowledge demands—such as aseptic technique and visual assessment—may require modalities beyond text, complicating translation from benchmarked capabilities to physical execution.

Addressing this problem would inform evaluation design, interface development, and biosecurity risk assessment by linking LLM outputs to concrete laboratory outcomes for novice users.