<b>Generative AI in Programming Education: Students’ Ethical Judgements Across Five University Sites</b>

Responses were analysed using descriptive summaries, Kruskal-Wallis tests, one-vs-rest Mann-Whitney U contrasts, and site-specific ordinal logistic regression.</p><p dir="ltr"><b>Findings:</b> Judgements varied across scenarios and differed significantly across sites for both outcomes. Across all five sites, amount of AI-generated code was the only scenario characteristic consistently and positively associated with both ethical concern and perceived rule violation.

Demonstrated understanding and stage of use showed weaker and less consistent independent associations. Ethicality and rule-violation judgements showed broadly similar but not identical patterns.</p><p dir="ltr"><b>Implications:</b> Students’ judgements were most consistently associated with the extent to which AI-generated code was incorporated in the assessed artefact. Programming educators should make expectations for generated code explicit and design assessments that make student reasoning visible while supporting pedagogically useful forms of GenAI.</p>