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This paper introduces the Tree-of-Thought Socratic Teaching (ToST) framework, which enhances the teaching capabilities of Large Language Models (LLMs) by enabling multi-path guidance and parallel thinking in problem-solving. By employing a Parallel Sowing questioning strategy and a Multi-Path Adaptive Guidance mechanism, ToST allows students to explore multiple valid solutions rather than being confined to a linear reasoning path. Experimental results show that ToST significantly improves guidance success rates, facilitating a more flexible and effective learning experience for students.
ToST empowers students to navigate multiple solution paths simultaneously, transforming the way LLMs can facilitate Socratic teaching.
Large Language Models (LLMs) exhibit strong problem-solving abilities, positioning them as promising agents for Socratic teaching to guide students through step-by-step heuristic questioning. However, existing approaches typically adopt a one-problem-one-solution paradigm, restricting the teaching guidance to a single linear reasoning path. This design limits instructional flexibility, weakens error recovery, and restricts students'ability to engage in parallel thinking to explore multiple valid solutions. To overcome these, we propose ToST, a Tree-of-Thought Socratic Teaching framework that explicitly supports multi-path guidance under a one-problem-multiple-solutions paradigm. ToST employs Parallel Sowing, a parallel-thinking-oriented questioning strategy to encourage students to approach problems from diverse perspectives, and a Multi-Path Adaptive Guidance mechanism to provide more robust and non-linear instructions across alternative solution trajectories. Concurrently, to fill the void in systematically evaluating such non-linear instructional capabilities, we advance the task of multi-path Socratic guidance by establishing MPSG-Bench, a comprehensive benchmark that includes a dataset of 31K multi-path teaching dialogues and a five-dimensional evaluation framework grounded in the SOLO (Structure of Observed Learning Outcomes) theory to assess parallel-thinking guidance. Experimental results demonstrate that ToST significantly enhances guidance success rates while empowering students to navigate and explore multiple solution paths more effectively under both automatic and human metrics.
