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.

When programming students encounter errors in their code, compiler messages or static analysis output often provide limited guidance, particularly for novice programmers. Personalized feedback from instructors can be effective but does not scale well. Recent advances in large language models (LLMs) enable automated feedback generation at scale.This study examines whether LLM-generated feedback with different levels of guidance is associated with differences in students’ problem-solving behavior. We analyze effects on time to solution and number of attempts, and examine whether these effects differ by programming experience. We design three feedback types and compare them to a baseline in which students receive only compiler error messages. Results from an online programming course show that LLM-generated feedback is associated with faster time to solution compared to the no-feedback baseline, with less guided feedback showing slightly stronger effects. Overall, the findings suggest that feedback structure plays an important role in how students progress toward correct solutions and motivate further work on adaptive feedback designs and longer-term learning outcomes.

Large Language Models (LLMs) offer exciting potential as educational tutors, and much research explores this potential. Unfortunately, there’s little research in understanding the baseline behavioral pattern differences that LLM tutors exhibit, in contrast to human tutors. We conduct a preliminary study of these differences with the CIMA dataset and three state-of-the-art LLMs (GPT-4o, Gemini Pro 1.5, and LLaMA 3.1 450B). Our results reveal systematic deviations in these baseline patterns, particulary in the tutoring actions selected, complexity of responses, and even within different LLMs. This research brings forward some early results in understanding how LLMs when deployed as tutors exhibit systematic differences, which has implications for educational technology design and deployment. We note that while LLMs enable more powerful and fluid interaction than previous systems, they simultaneously develop characteristic patterns distinct from human teaching. Understanding these differences can inform better integration of AI in educational settings.

Locally deployed Small Language Models (SLMs) offer a promising solution for providing timely and effective programming feedback to students learning to code. However, SLMs often produce misleading or hallucinated feedback, limiting their reliability in educational settings. Current approaches for improving SLM feedback rely on existing human annotations or LLM-generated feedback. This paper addresses a fundamental challenge: Can we improve SLMs’ feedback capabilities without relying on human or LLM-generated annotations? We demonstrate that training SLMs on the proxy task of program repair is sufficient to enhance their ability to generate high-quality feedback. To this end, we introduce Direct Repair Optimization (DRO), a self-supervised online reinforcement learning strategy that trains language models to reason about how to efficiently fix students’ programs.Our experiments, using DRO to fine-tune LLaMA-3.1–3B and Qwen-2.5–3B on a large-scale dataset of Python submissions from real students, show substantial improvements on downstream feedback tasks. We release our code to support further research in educational feedback and highlight promising directions for future work.

One of the key challenges in programming education is being able to provide high-quality feedback to learners. Such feedback often includes explanations of the issues in students’ programs coupled with suggestions on how to fix these issues. Large language models (LLMs) have recently emerged as valuable tools that can help in this effort. In this article, we explore the relationship between the program repair ability of LLMs and their proficiency in providing natural language explanations of coding mistakes. We outline a benchmarking study that evaluates leading LLMs (including open-source ones) on program repair and explanation tasks. Our experiments study the capabilities of LLMs both on a course level and on a programming concept level, allowing us to assess whether the programming concepts practised in exercises with faulty student programs relate to the performance of the models. Our results highlight that LLMs proficient in repairing student programs tend to provide more complete and accurate natural language explanations of code issues. Overall, these results enhance our understanding of the role and capabilities of LLMs in programming education. Using program repair as a proxy for explanation evaluation opens the door for cost-effective assessment methods.