Integrating AI-driven tools in higher education is an emerging area with
transformative potential. This paper introduces Iris, a chat-based virtual
tutor integrated into the interactive learning platform Artemis that offers
personalized, context-aware assistance in large-scale educational settings.
Iris supports computer science students by guiding them through programming
exercises and is designed to act as a tutor in a didactically meaningful way.
Its calibrated assistance avoids revealing complete solutions, offering subtle
hints or counter-questions to foster independent problem-solving skills. For
each question, it issues multiple prompts in a Chain-of-Thought to
GPT-3.5-Turbo. The prompts include a tutor role description and examples of
meaningful answers through few-shot learning. Iris employs contextual awareness
by accessing the problem statement, student code, and automated feedback to
provide tailored advice.
An empirical evaluation shows that students perceive Iris as effective
because it understands their questions, provides relevant support, and
contributes to the learning process. While students consider Iris a valuable
tool for programming exercises and homework, they also feel confident solving
programming tasks in computer-based exams without Iris. The findings underscore
students' appreciation for Iris' immediate and personalized support, though
students predominantly view it as a complement to, rather than a replacement
for, human tutors. Nevertheless, Iris creates a space for students to ask
questions without being judged by others.

虚拟辅导员 Iris 是一个聊天型的基于人工智能技术的工具，集成到交互式学习平台 Artemis 中，为大规模教育环境中的计算机科学学生提供个性化、上下文感知的辅助。Iris 通过引入多种提示和问题，避免提供完整答案，培养学生独立解决问题的能力。经实证评估发现，学生普遍认为 Iris 有效，因为它能理解问题、提供相关支持并促进学习过程。尽管学生普遍将 Iris 视为编程练习和作业的有价值工具，并且在计算机考试中也对解决编程任务感到自信，但学生们仍然将 Iris 视为人类辅导员的补充，而非替代工具。然而，Iris 为学生提供了一个可以自由提问而不被他人评判的空间。

Iris：计算机科学教育的 AI 驱动虚拟导师

Iris: An AI-Driven Virtual Tutor For Computer Science Education

This article explores the natural language generation capabilities of large
language models with application to the production of two types of learning
resources common in programming courses. Using OpenAI Codex as the large
language model, we create programming exercises (including sample solutions and
test cases) and code explanations, assessing these qualitatively and
quantitatively. Our results suggest that the majority of the automatically
generated content is both novel and sensible, and in some cases ready to use as
is. When creating exercises we find that it is remarkably easy to influence
both the programming concepts and the contextual themes they contain, simply by
supplying keywords as input to the model. Our analysis suggests that there is
significant value in massive generative machine learning models as a tool for
instructors, although there remains a need for some oversight to ensure the
quality of the generated content before it is delivered to students. We further
discuss the implications of OpenAI Codex and similar tools for introductory
programming education and highlight future research streams that have the
potential to improve the quality of the educational experience for both
teachers and students alike.

本文研究了利用大型语言模型进行自然语言生成的能力，并应用于编程课程中常见的两种学习资源的制作。研究发现，在创建编程练习时，只需要输入关键词，即可显著影响编程概念和上下文主题的内容，同时也证明了大多数自动生成的内容是全新且合理的。这些结果表明，使用大型生成机器学习模型作为教学工具是有重要价值的，但在交付给学生之前需要一些监督来确保生成的内容质量。文章还探讨了 OpenAI Codex 及类似工具对初学者编程教育的影响，并强调了可能改善教学体验的未来研究方向。