Large language models hold great promise for transforming K-12 education, but there is an urgent need for systematic evaluation of their core educational capabilities. Existing benchmarks often overlook educational goal cognition and overemphasize answer accuracy, thereby failing to capture deeper subject-level knowledge ability and problem-solving ability. To address this gap, we introduce K-12EduBench: a benchmark for evaluating LLMs’ subject-level knowledge ability, subject-specific problem-solving ability, and educational goal cognition ability in K-12 education. K-12EduBench comprises four components: (1) a dataset of 2,640 objective and 619 subjective questions across nine subjects, annotated with answers, problem-solving processes, and cognitive-level labels; (2) nine Item Response Theory (IRT) models for estimating subject-level knowledge ability; (3) evaluation methods and metrics for assessing multi-step problem-solving ability; and (4) prompts and scoring rubrics for measuring alignment with target cognitive levels. Experiments on advanced LLMs show that education-optimized models consistently outperform general-purpose ones across all three abilities, while under-scaled models lag substantially. We observe a strong positive correlation between subject-level knowledge ability and subject-specific problem-solving ability. Despite gains in educational goal cognition ability, current models—even those tailored for education—still fall short of real-world instructional needs.

With the rapid evolution of multimedia technologies and its widespread integration into education, adaptive multimedia learning has gained significant prominence. Cognitive diagnosis (CD) is pivotal in this domain, as it models students' cognitive states using practice data captured by multimedia learning applications. However, existing methods often simplify these states to mere proficiency on knowledge concepts. Constructivism in education emphasizes learning as a continuous cognitive development process, during which students' cognitive states become increasingly complex, involving not only their construction of concepts but also their construction of relations between concepts that have long been overlooked. To this end, we propose the Hierarchical Disentanglement of Cognitive States for Enhanced Cognitive Diagnosis (HDCD). Inspired by the Structure of Observed Learning Outcomes (SOLO) taxonomy, which categorizes cognitive development into core hierarchical levels (Multistructural, Relational, Extended Abstract), we introduce a hierarchical disentanglement strategy to define cognitive states aligned with each SOLO level: Intra-Concept Cognitive States, Relational Cognitive States, and Extended Cognitive States. Specifically, (i) At the multistructural level, intra-concept cognitive states are sampled from student's personalized cognitive distribution, representing the construction of individual concepts. (ii) At the relational level, inter-concept cognitive states are first sampled to represent the construction of relations between concepts. We then employ a hypergraph transformation to collaboratively update both intra-concept and inter-concept cognitive states, forming relational cognitive states. Considering that students' self-constructed knowledge systems involve multiple types of inter-concept relations, relational cognitive states are implemented under both undirected and directed relation views in this work, and then fed into local diagnostic functions, respectively. (iii) At the extended abstract level, outputs from the local diagnostic functions are fused using multi-view attention mechanisms, resulting in extended cognitive states, which integrate information from multiple relational views, are then fed into a global diagnostic function for final prediction. Extensive experiments on real-world datasets demonstrate the superior performance and interpretability of our HDCD.