Socratic teaching, known for its emphasis on heuristic questioning and deep thinking, has demonstrated significant advantages in promoting students’ cognitive development. However, traditional Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. Recent breakthroughs in large language models (LLMs) in natural language generation and dialogue comprehension offer the potential for automated Socratic teaching. In this paper, we propose Knowledge-Enlightened Learning Enhanced by LLMs (KELE), a novel multi-agent framework for structured Socratic teaching with LLMs. KELE constructs a structured Socratic teaching rule system (SocRule) and a “consultant–teacher” multi-agent collaborative teaching mechanism, in which two LLMs respectively take charge of teaching planning and execution, ensuring a logically coherent and hierarchically structured Socratic teaching process. We also construct SocratDataset, a structured Socratic teaching dataset covering 34 teaching strategies and over 42,000 dialogue turns, and train SocratTeachLLM, a specialized LLM for Socratic teaching tasks. Additionally, we build a comprehensive Socratic teaching quality evaluation system for LLMs, covering 9 dimensions from single-turn dialogue to multi-turn teaching processes. Experimental results show that SocratTeachLLM significantly outperforms GPT-4o, which has a much larger parameter size, across all Socratic teaching capabilities.

“In this paper, we present our system at CCL24-Eval Task 6: Chinese Essay Rhetoric Recognition and Understanding (CERRU). The CERRU task aims to identify and understand the use of rhetoric in student writing. The evaluation set three tracks to examine the recognition of rhetorical form, rhetorical content, and the extract of rhetorical components. Considering the potential correlation among the track tasks, we employ the unified multi-task learning architecture to fully incorporate the inherent interactions among the related tasks to improve the overall performance and to complete the above 3 track tasks with a single model. Specifically, the framework mainly consists of four sub-tasks: rhetorical device recognition, rhetorical form recognition, rhetorical content recognition, and rhetorical component extraction. The first three tasks are regarded as multi-label classification tasks, and the last task is regarded as an entity recognition task. The four tasks leverage potential information transfer to achieve fusion learning. Finally, the above four sub-tasks are integrated into a unified model through parameter sharing. In the final evaluation results, our system ranked fourth with a total score of 60.14, verifying the effectiveness of our approach.”