Design and Deployment of a Course-Aware AI Tutor in an Introductory Programming Course

TL;DR

This paper presents a course-specific AI Python tutor designed to support students' learning by providing hints and explanations grounded in course materials, aiming to enhance engagement and understanding without giving full solutions.

Contribution

The authors developed a retrieval-augmented, course-aligned AI tutor that integrates with a web environment to support novice programmers during self-study.

Findings

Students used the tutor mainly for conceptual understanding and debugging.

The tutor was perceived as course-aligned and engagement-promoting.

Interaction logs show students relied on the tutor for guidance rather than copying solutions.

Abstract

Large Language Models (LLMs) have become part of how students solve programming tasks, offering immediate explanations and even full solutions. Previous work has highlighted that novice programmers often heavily rely on LLMs, thereby neglecting their own problem-solving skills. To address this challenge, we designed a course-specific online Python tutor that provides retrieval-augmented, course-aligned guidance without generating complete solutions. The tutor integrates a web-based programming environment with a conversational agent that offers hints, Socratic questions, and explanations grounded in course materials. Students used the system during self-study to work on homework assignments, and the tutor also supported questions about the broader course material. We collected structured student feedback and analyzed interaction logs to investigate how they engaged with the tutor's guidance. We observed that students used the tutor primarily for conceptual understanding, implementation guidance, and debugging, and perceived it as a course-aligned, context-aware learning support that encourages engagement rather than direct solution copying.