Аңдатпа
Open-ended short responses can reveal more about student understanding than selectedresponse items yet scoring them during live instruction is rarely feasible. This paper presents a bring-your-own-device (BYOD) mobile classroom system that combines rubric-guided large language model (LLM) scoring of short open-ended answers, real-time teacher analytics through a classroom dashboard, and evidence-based post-quiz feedback generation. The system includes a teacher-reviewed workflow in which instructors can inspect model outputs and, when needed, adjust scores or feedback during formative classroom use. In the reported evaluation, however, teacher override was not applied or analyzed; the agreement metrics compare the system’s initial rubric-guided outputs with expert reference scores. The system was evaluated in three classroom groups (N = 60 students; 480 question-level scoring cases). Three expert raters participated, each assigned to one classroom group, so each response received one expert reference score. In this test, system scores showed strong but not complete agreement with expert reference scores (QWK = 0.887; MAE = 0.089; RMSE = 0.121). Mastery-label accuracy reached 78.3%. Expert raters gave positive scores to the generated feedback reports for groundedness (M = 4.78), specificity (M = 4.37), and actionability (M = 4.57) on a 1–5 scale. Within the limits of this small-scale classroom evaluation, the findings suggest that rubric-guided LLM scoring may support formative assessment in mobile classrooms, provided teacher oversight is maintained for borderline cases.