We discuss a novel approach to Premodern Japanese Language Pedagogy (PJLP) with potential applications in other languages and fields. The integration of artificial intelligence into education has largely operated as a top-down project, affording minimal agency to everyday users. This dynamic mirrors the broader frontier model ecosystem, which concentrates massive human and financial resources within a few labs. Drawing inspiration from grassroots initiatives such as the DIY and Maker movements, this paper advocates for an approach to AI in Education that fosters instructional and student agency over the pedagogical process. Specifically, we discuss a tutoring framework for textual analysis in the context of a graduate seminar in premodern Japanese literature, as well as a bilingual interactive dictionary and a conversational partner created for a language course in Classical Japanese. Created through prompt engineering as custom instances of a Large Language Model (LLM), these three tools are designed to counteract the tendency of out-of-the-box LLMs to either bypass student effort through over-explanation or misguide learners via hallucinations. To illustrate how this approach can promote active comprehension and pedagogical alignment, we provide transcripts (logs) of actual exchanges, sample instructions (system prompts), and guidance for instructors curious about exploring this approach in a variety of fields (starter kit).

翻译：我们探讨了一种前现代日语教学法（PJLP）的新方法，该方法在其他语言和领域具有潜在应用价值。人工智能与教育的融合在很大程度上一直是一个自上而下的项目，赋予日常用户极低的自主权。这种动态反映了更广泛的前沿模型生态系统，该生态系统将大量人力和财力资源集中在少数实验室中。本文借鉴了DIY和创客运动等草根倡议，主张在人工智能教育中采用一种增强教学和学生自主权的方法。具体而言，我们讨论了一个在前现代日本文学研究生研讨班中用于文本分析的教学框架，以及为一个古典日语语言课程创建的双语互动词典和对话伙伴。通过提示工程作为大语言模型（LLM）的自定义实例创建，这三个工具旨在抵消现成LLM倾向于通过过度解释绕过学生努力或通过幻觉误导学习者的倾向。为了说明这种方法如何促进主动理解与教学一致性，我们提供了实际交流记录（日志）、示例指令（系统提示），以及为对在各种领域探索该方法感兴趣的教育工作者提供的指南（入门套件）。