One-to-one tutoring is one of the most efficient methods of teaching. With the growing popularity of Large Language Models (LLMs), there have been efforts to create LLM based conversational tutors which can expand the benefits of one to one tutoring to everyone. However, current LLMs are trained primarily to be helpful assistants and lack crucial pedagogical skills. For example, they often quickly reveal the solution to the student and fail to plan for a richer multi turn pedagogical interaction. To use LLMs in pedagogical settings, they need to be steered to use effective teaching strategies: a problem we introduce as Pedagogical Steering. We develop StratL, an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design. To validate our approach in a real-world setting, we run a field study with 17 high school students in Singapore and show that StratL succeeds in steering the LLM to follow the PF tutoring strategy. Finally, we highlight challenges in Pedagogical Steering of LLMs and offer opportunities for further improvements by publishing a dataset of PF problems and our code.

翻译：一对一辅导是最高效的教学方法之一。随着大型语言模型（LLMs）的日益普及，学界已开始尝试创建基于LLM的对话式辅导系统，以期将一对一辅导的优势惠及更广泛的学习者。然而，当前LLMs主要被训练为辅助工具，缺乏关键的教学能力。例如，它们往往过早地向学生揭示解决方案，且难以规划丰富的多轮教学互动。为在教学中有效运用LLMs，需要引导其采用科学的教学策略——我们将这一问题定义为"教学引导"。本研究开发了StratL算法，通过优化LLM提示词来引导其遵循预定义的多轮教学计划（以状态转移图表示）。作为案例研究，我们基于"生产性失败"（Productive Failure, PF）这一先进有效的教学设计原理，构建了面向高中数学的辅导原型系统。为在真实场景中验证方法有效性，我们在新加坡对17名高中生开展了实地研究，结果表明StratL能成功引导LLM遵循PF教学策略。最后，我们通过发布PF问题数据集及代码，系统阐述了LLM教学引导面临的挑战，并为后续改进提供了研究方向。