A source-based quiz can help a computing teacher identify material to explain again, but its report must preserve the limits of the evidence behind that decision. We present ClassQuest, a prototype connecting teacher-reviewed question drafts, private student responses, topic reports and targeted retries. A Year 10 classroom account of an earlier prototype supplies 20 usefulness ratings from 24 invitees, with a mean of 8.0 and median of 9 on a ten-point scale. A Boolean-logic episode documents how immediate feedback, a teacher explanation and a fresh question were combined. We separate this account from a completed technical audit and reusable teaching guide. An exhaustive single-item audit found no count mismatches across 5,456 constructed response configurations, while diagnostic cases exposed a retry increase caused entirely by changing participation and a progression suggestion based on one learner's three answers. A further case shows that an unoffered retry can be labelled missing. A separate reporting companion distinguishes opportunity to respond, pending review and matched retry cohorts. Its completion-sensitivity check matched an independent oracle in 46,375 constructed states across roster sizes 1-30, identifying when unknown responses can change a reporting band. Our contributions are a source-linked formative workflow, executable examples of interpretation failures, and reporting and teaching materials for examining those failures. Neither the questionnaire nor the audit establishes improved learning; the findings identify reporting requirements and practical directions for formative teaching.
Large language models (LLMs) are increasingly considered for education, recruitment, workplace decision support, and other consequential applications, making pre-deployment social-bias detection essential. Yet major benchmarks are English-centred or translated, focus mainly on negative stereotypes, and may miss linguistic and cultural variation. We investigate gender, racial, and religious bias using 12,000 locally written Reddit posts in English, Spanish, French, and German. Only search terms are translated, preserving local context. The dataset is balanced by language and category; English gender is additionally balanced by stereotype valence. Three open-weight and three proprietary API-based models are evaluated with a two-blank design that reduces grammatical-gender cues. Religion has the highest absolute choice imbalance in every model-language condition. Spanish is highest for gender, whereas German is highest for race and religion. The evaluated API models score below the open-weight models throughout. Negative English-gender items exceed positive items; Australian and Mexican contexts exceed their Canadian and Spanish counterparts. These descriptive differences measure choice concentration, not stereotype endorsement or causal country effects. The crossed patterns show how aggregate scores can conceal category- and context-specific behaviour, motivating closer inspection before deployment.