发表机构
IMT Atlantique（IMT大西洋高等电信学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对维度建模课程，提出教学驱动型教师助手，无需商业大语言模型预算或GPU设施。架构含确定性模块处理多任务，大语言模型作语言执行器。评估学生问题发现标准检索不足，确定性管道精度高但覆盖有限，为教学策略提供新思路。
AI 中文摘要
由大语言模型驱动的教育聊天机器人对学习成果显示出有前景的效果，但大多数系统将诸如内容选择和教学结构等教学决策隐式委托给大语言模型，使得辅导策略难以追踪、评估和重现。本文提出了一个面向法语大学维度建模课程的教学驱动型教师助手，无需商业大语言模型预算或GPU基础设施运行。该架构将教师的教学推理形式化为确定性模块，在生成任何文本之前处理意图检测、概念链接和教学方法选择；大语言模型仅作为语言执行器。对195个真实学生问题的评估解决了两个研究问题。首先，表明仅标准语义检索不能可靠地找回教学所需内容，从而证明了架构中采用的上游编排策略的合理性。其次，与检测性能因模型而异且会默默产生错误的免费层大语言模型相比，确定性管道实现了高配对精度（73%），具有完全可追溯性和明确弃权，不过其有限的覆盖范围证实检测策略需要进一步完善。
英文摘要
Educational chatbots powered by large language models (LLMs) show promising effects on learning outcomes, yet most systems delegate pedagogical decisions such as content selection and didactic structuring implicitly to the LLM, making tutoring strategies difficult to trace, evaluate, and reproduce. This paper presents a didactical-driven teacher assistant for a French-language university course on dimensional modelling, operating without commercial LLM budget or GPU infrastructure. The architecture formalises the instructor's pedagogical reasoning into deterministic modules that handle intent detection, concept linking, and didactic approach selection before any text is generated; the LLM acts solely as a linguistic executor. Evaluation on 195 authentic student questions addresses two research questions. First, we show that standard semantic retrieval alone does not reliably recover the pedagogically required content, thereby justifying the upstream orchestration strategy adopted in our architecture (RQ1). Second, compared to free-tier LLMs whose detection performance varies widely across models and which produce errors silently, the deterministic pipeline achieves high pair precision (73%) with full traceability and explicit abstention, though its limited coverage confirms that the detection strategy requires further refinement (RQ2).
Journal refInternational Conference on Computer Supported Education, May 2026, Benidorm/Spain, France. pp.177-189
