# Exploring the Role of AI Assistants in Computer Science Education: Methods, Implications, and Instructor Perspectives

arXiv: https://arxiv.org/abs/2306.03289 · HTML: https://mlsanity.com/paper/2306.03289/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2023.

## Built on by (most cited first, 12 of 12)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024)
- [Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants](https://mlsanity.com/paper/2408.11841/) (2024)
- [Generating Planning Feedback for Open-Ended Programming Exercises with LLMs](https://mlsanity.com/paper/2504.08958/) (2025)
- [ChatGPT in Data Visualization Education: A Student Perspective](https://mlsanity.com/paper/2405.00748/) (2024)
- [Assessing Personalized AI Mentoring with Large Language Models in the Computing Field](https://mlsanity.com/paper/2412.08430/) (2024)
- [Designing Conversational AI to Support Think-Aloud Practice in Technical Interview Preparation for CS Students](https://mlsanity.com/paper/2507.14418/) (2025)
- [Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning](https://mlsanity.com/paper/2601.12134/) (2026)
- [CodeGuard: Improving LLM Guardrails in CS Education](https://mlsanity.com/paper/2602.02509/) (2026)
- [Assistant or Actor? Student Trust, Control, and Delegation Regret When Using a General-Purpose AI Agent](https://mlsanity.com/paper/2607.18257/) (2026)
- [Designing Human-AI Collaboration to Support Learning in Counterspeech Writing](https://mlsanity.com/paper/2410.03032/) (2024)
- [Thinking Like a Student: AI-Supported Reflective Planning in a Theory-Intensive Computer Science Course](https://mlsanity.com/paper/2511.01906/) (2025)

## Abstract (arXiv)

The use of AI assistants, along with the challenges they present, has sparked significant debate within the community of computer science education. While these tools demonstrate the potential to support students' learning and instructors' teaching, they also raise concerns about enabling unethical uses by students. Previous research has suggested various strategies aimed at addressing these issues. However, they concentrate on the introductory programming courses and focus on one specific type of problem. The present research evaluated the performance of ChatGPT, a state-of-the-art AI assistant, at solving 187 problems spanning three distinct types that were collected from six undergraduate computer science. The selected courses covered different topics and targeted different program levels. We then explored methods to modify these problems to adapt them to ChatGPT's capabilities to reduce potential misuse by students. Finally, we conducted semi-structured interviews with 11 computer science instructors. The aim was to gather their opinions on our problem modification methods, understand their perspectives on the impact of AI assistants on computer science education, and learn their strategies for adapting their courses to leverage these AI capabilities for educational improvement. The results revealed issues ranging from academic fairness to long-term impact on students' mental models. From our results, we derived design implications and recommended tools to help instructors design and create future course material that could more effectively adapt to AI assistants' capabilities.