Metacognitive and information-analytical actions in solving genetics problems in medical biology: pilot questionnaire dataset (N=42)

This dataset contains anonymized response-level data from 42 higher-education students across three study cohorts (n=17, n=10, n=15) who completed an eight-item pilot questionnaire on how they solve and reflect on genetics problems in medical biology. The questionnaire covers a traditional autosomal-recessive cystic fibrosis problem, self-reported problem-solving actions, judgments of information relevance and sufficiency, verification of generative-AI responses, perceived priorities and difficulties in genetics problem solving, and prior experience using generative AI in medical biology learning tasks. Data were collected anonymously and voluntarily in September 2026. The package includes the Ukrainian questionnaire, an English translation, anonymized respondent-level responses, an analysis-ready coded dataset, and a bilingual codebook. Direct identifiers and response timestamps are not included in the public files. Original Ukrainian free-text responses are preserved. Minor grammatical variants in some response options were harmonized in the codebook without changing semantic categories. The dataset is intended to support reproducible descriptive analysis and reuse in research on genetics education, metacognition, information-analytical skills, and generative AI in higher education.