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

arXiv: https://arxiv.org/abs/2405.08008 · HTML: https://mlsanity.com/paper/2405.08008/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Large Language Models are Diverse Role-Players for Summarization Evaluation](https://mlsanity.com/paper/2303.15078/) (2023)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in CS Education](https://mlsanity.com/paper/2308.03992/) (2023)

## Built on by (most cited first, 9 of 9)

- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students](https://mlsanity.com/paper/2411.18708/) (2024)
- [A Survey Study on the State of the Art of Programming Exercise Generation using Large Language Models](https://mlsanity.com/paper/2405.20183/) (2024)
- [A Scoping Review of Large Language Model-Based Pedagogical Agents](https://mlsanity.com/paper/2604.12253/) (2026)
- [From Coders to Critics: Empowering Students through Peer Assessment in the Age of AI Copilots](https://mlsanity.com/paper/2505.22093/) (2025)
- [Autograder+: A Multi-Faceted AI Framework for Rich Pedagogical Feedback in Programming Education](https://mlsanity.com/paper/2510.26402/) (2025)
- [An Experience Report on a Pedagogically Controlled, Curriculum-Constrained AI Tutor for SE Education](https://mlsanity.com/paper/2512.11882/) (2025)
- [From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education](https://mlsanity.com/paper/2512.20714/) (2025)

## Abstract (arXiv)

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.