Students’ wrong answers in science assessments can reveal plausible misconceptions, yet standard LLM benchmarks usually evaluate only answer correctness. We introduce MisconceptionBench, a benchmark for evaluating whether LLMs can reason about misconception-linked distractors and empirical student response patterns. It contains 761 K-12 science items, 1,474 expert-annotated distractors, and response distributions from more than 150,000 students. We evaluate eight LLMs on three tasks: misconception matching, misconception generation, and student response distribution prediction. In matching, text-similarity baselines remain competitive in two- and three-candidate settings; clear LLM gains emerge only with four candidates, where GPT-4o and Llama-3.3-70B significantly outperform TF-IDF and MiniLM. In generation, the strongest models are tightly clustered, with Hit@1 of 38.5-39.6%, while retrieval-only baselines recover many targets without an LLM. Response distribution prediction remains difficult: no model exceeds F1=0.21 for identifying grade-band differences. Overall, misconception-centered reasoning requires evaluation beyond answer accuracy.

This study examines the relationship between high school students’ reliance on GenAI and potential predictors: student demographic characteristics, academic background, and learning goal orientation. The findings highlight the importance of guiding students to use GenAI appropriately, preventing over-reliance on AI, and fostering mastery goal orientations in the AI age.