From Evaluated Models to Evaluation Aids: A Multi-Evidence Study of LLM-Based Difficulty Calibration for Programming Examinations
TL;DR
AI evidence can serve as an external reference for problem validation, parallel-class fairness discussion, and longitudinal quality tracking, while the model-identity boundary, single-reviewer design, and review-output instability set explicit limits: AI difficulty scales must not be used for individual student evaluation or automatic grade adjustment.
Abstract
Difficulty differences across parallel-class programming examinations affect the fairness of course assessment. This study repositions large language models from benchmark evaluation targets to auxiliary evidence sources for interpreting exam difficulty, combining AI evidence with aggregated student performance, item exposure, online-judge process data, and teacher interpretation. First, ten models solved an eight-problem final exam synchronously with 120 students: AI pass rate correlated positively with student pass rate (Spearman rho = 0.866, exact p = 0.0119), and a solving-based composite difficulty index correlated negatively with it (rho = -0.905, exact p = 0.0046). A single structured reviewer was then run via auditable API calls on a third-party OpenAI-compatible endpoint whose model label (gpt-5.6-sol) cannot authenticate an official OpenAI upstream model; call metadata and raw responses are archived. Across 79 problems from 11 parallel-class final exams, AI overall difficulty correlated with problem-level pass rate at rho = -0.871 and with non-attempt rate at rho = 0.800; in a 26-problem longitudinal Data Structures and Algorithms B sample, the correlations were -0.829 and 0.883. A 106-problem introductory-course (CS101) sample marks the boundary: the problem-level correlation weakened to rho = -0.552, and the exam-level correlation across 16 exams was near zero, with cohort composition dominating exam-level outcomes. Exposure-discount (0-0.40) and duplicate-problem perturbation tests did not change these directions. AI evidence can thus serve as an external reference for problem validation, parallel-class fairness discussion, and longitudinal quality tracking, while the model-identity boundary, single-reviewer design, and review-output instability set explicit limits: AI difficulty scales must not be used for individual student evaluation or automatic grade adjustment.