This study presents a fuzzy-Apriori model that analyses student background data, along with end-of-lesson student-generated questions, to identify interpretable rules. After linguistic and semantic preprocessing, questions are represented in a fuzzy form and combined with background and performance variables to generate association rules, including support, confidence, and lift. The dataset includes 202 students, parent reports from 174 families, 5832 student-generated questions, and 510 teacher-generated questions collected in regular lessons in grades 7–8. The model also incorporates a topic-level dynamic updating step that refreshes the rule set over time. The findings indicate descriptive associations between background characteristics, question complexity and alignment, and classroom performance. It is essential to note that this phase explores possibilities rather than providing a validated instructional method. Question coding inevitably involves subjective elements, and while we conducted the study in real classroom settings, we did not perform causal analyses at this stage. The next step will be developing reliability metrics through longitudinal studies across multiple classroom environments. Future work will test whether using these patterns can inform instructional adjustments and support student learning.