Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instances. In this paper, we present KITE (Knowledge-Informed Tutoring Engine), a Retrieval-Augmented Generation (RAG)-based intelligent tutoring system designed to serve as a classroom teaching assistant for algorithmic reasoning and problem-solving tasks. KITE uses an intent-aware Socratic response strategy to tailor support to different student needs, responding with targeted hints, guiding questions, and progressive scaffolding intended to strengthen students' algorithmic problem-solving ability. To keep responses aligned with course content, KITE uses a multimodal RAG pipeline that retrieves relevant information from course materials. We evaluate KITE using three forms of assessment: RAGAs-based metrics for response grounding and quality, expert evaluation of pedagogical quality, and a simulated student pipeline in which a weaker language model interacts with KITE across two-turn dialogues and produces revised answers after receiving feedback. Results indicate that KITE produces contextually grounded and pedagogically appropriate responses. Further, using simulated students, KITE's feedback helped the student models produce more accurate follow-up responses on procedural and tracing questions, suggesting that its scaffolding can support algorithmic problem-solving. This work contributes a tutoring architecture and an evaluation approach for assessing retrieval-grounded explanations and scaffolded problem-solving feedback.

翻译：学习算法的学生在理解程序追踪、调试推理错误以及将步骤应用于陌生问题实例时，常需教学支持。本文提出KITE（知识赋能辅导引擎）——一种基于检索增强生成（RAG）的智能辅导系统，旨在作为算法推理与问题求解任务的课堂助教。KITE采用意图感知型苏格拉底式响应策略，根据不同学生需求提供针对性提示、引导式提问与渐进式支架，以强化学生的算法问题解决能力。为保持响应与课程内容一致，系统构建多模态RAG管道，可从课程资料中检索相关信息。我们通过三种评估形式对KITE进行评价：基于RAGAs指标的响应依据与质量评估、教学质量的专家评估，以及利用弱语言模型与KITE进行两轮对话模拟学生交互的评估——模拟学生接收反馈后输出修正答案。结果表明，KITE能生成上下文相关且教学合理的响应。进一步通过模拟学生实验发现，KITE的反馈可帮助学生模型在程序性及追踪类问题上产生更准确的后续回答，证明其支架式辅导能够支撑算法问题求解。本研究贡献了一种辅导架构与评估方法，用于评测基于检索的解释性反馈与支架式问题解决指导。