Recently, several multi-turn dialogue benchmarks have been proposed to evaluate the conversational abilities of large language models (LLMs). As LLMs are increasingly recognized as key technology for advancing intelligent education, owing to their ability to deeply understand instructional contexts and provide personalized guidance, constructing dedicated teacher-student dialogue benchmarks has become particularly important. To this end, we present EduDial, a comprehensive multi-turn teacher-student dialogue dataset. EduDial covers 345 core knowledge points and consists of 34,250 dialogue sessions from interactions between teacher and student agents. Its design is guided by Bloom’s taxonomy of educational objectives and incorporates ten questioning strategies—including situational questioning, zone of proximal development (ZPD) questioning, and metacognitive questioning—better capturing authentic classroom interactions. Furthermore, we design differentiated teaching strategies for students at different cognitive levels, providing more targeted guidance. Building on EduDial, we develop EduDial-LLM 32B via training and propose an 11-dimensional evaluation framework that systematically measures the teaching abilities of LLMs, encompassing overall teaching quality and content quality. Experiments on 17 mainstream LLMs reveal that most models struggle in student-centered teaching scenarios, whereas EduDial-LLM achieves significant gains, consistently outperforming all baselines across all metrics. Code is available at https://github.com/ECNU-RAIL/EduDial-EMNLP2026.

Knowledge Tracing (KT) is a pivotal task in personalized education, aiming to predict students’ future performance based on their historical interactions. While prior work has focused on learning behavioral sequences using question IDs or surface-level textual features, these methods often fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. To address this, we propose LLM-KT, a novel framework that integrates the reasoning power of Large Language Models (LLMs) with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. Specifically, for task-level alignment, we design a plug-and-play instruction to leverage the rich knowledge and reasoning capacity of LLMs for the KT objective. For modality-level alignment, we introduce two mechanisms to integrate representations learned by traditional methods: (1) a Semantic History Projector that flexibly inserts compressed context embeddings into LLMs using question- and concept-specific tokens to capture long-term history; and (2) a Behavioral Dynamics Projector that enhances LLMs with sequential interaction patterns via a sequence adapter. Extensive experiments on four standard datasets demonstrate that LLM-KT achieves state-of-the-art performance, significantly outperforming over 20 competitive baselines.

Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings. However, current supervised fine-tuning methods only learn surface-level teaching patterns without dynamic adaptation. Recent reinforcement learning approaches address this, yet two key challenges remain. First, they evaluate teaching effectiveness solely based on whether students produce correct outputs, unable to distinguish whether students genuinely understand or echo teacher-provided answers. Second, they cannot perceive students’ evolving cognitive states through dialogue, failing to adapt teaching strategies accordingly. We propose Unidirectional Cognitive Optimization (UCO) to address these challenges. UCO employs a multi-turn interactive reinforcement learning paradigm with two synergistic reward functions: the Progress Reward captures cognitive advancement, evaluating students’ transition from confusion to comprehension, and the Scaffold Reward dynamically identifies each student’s Zone of Proximal Development (ZPD), encouraging the teacher to conduct productive teaching within this zone. We evaluate UCO against 11 baseline models on BigMath and MathTutorBench. Results demonstrate that UCO outperforms all equivalent-scale models and achieves performance comparable to leading closed-source models. Code is available at https://github.com/ECNU-RAIL/UCO-EMNLP2026.

Large language models (LLMs) have advanced the development of personalized learning in education. However, their inherent generation mechanisms often produce homogeneous responses to identical prompts. This “one-size-fits-all” mechanism overlooks the substantial heterogeneity in students’ cognitive and psychological characteristics, thereby posing potential safety risks to vulnerable groups. Existing safety evaluations primarily rely on context-independent metrics such as factual accuracy, bias, or toxicity, which fail to capture the divergent harms that the same response might cause across different student attributes. To address this gap, we propose the concept of “Student-Tailored Personalized Safety” and construct CASTLE based on educational theories. This benchmark covers 15 educational safety risks and 14 student attributes, comprising 92,908 bilingual scenarios. We further design three evaluation metrics: Risk Sensitivity, measuring the model’s ability to detect risks; Emotional Empathy, evaluating the model’s capacity to recognize student states; and Student Alignment, assessing the match between model responses and student attributes. Experiments on 18 LLMs demonstrate that CASTLE poses a significant challenge: all models scored below an average safety score of 2.5 out of 5, indicating substantial deficiencies in personalized safety assurance. The code is available at https://github.com/ECNU-RAIL/CASTLE-EMNLP2026.